The Hub Designs MDM Think Tank met with several executives from Agility Multichannel – Richard Hunt, CEO, Graham Cook, CTO, Gene Maggard, SVP of Global Technical Sales, and Stephen Golembiewski, Principal Field Consultant, for an in-depth briefing on Agility® 5.2, the latest version of their product information management solution.

Agility Multichannel defines its mission as helping clients in the business-to-business, retail, and manufacturing environments to be more effective in communicating with their customers. They recognize that no customer journey through the buying process is the same, and most do not reach the end. They stated the aim of the software is to deliver compelling, consistent product information across all channels and buying stages in order to improve customer satisfaction and increase conversions.

Of all of the channels for publishing up-to-date product information, a company’s web site is probably the most important, so Agility provides lots of different means to publish data to a web site or an e-commerce site.

The next channels they support are printed catalogs and brochures, which are still very popular in support of the buying process. Other print publishing capabilities include flyers and ads.

On the digital side, Agility can output to iPads and tablets, and can support e-mail campaigns with current, accurate, consistent product information. It can also provide updates to in-store point of sales systems and call centers.

And the product can also support campaigns for smartphones, social networking sites and product comparison sites.

Agility supports multiple languages and multiple versions of product information.

From the company’s perspective, it sees a number of problems with most companies’ environment(s) for handling product information:

• Siloed processes which lead to inconsistent product information being provided to customers
• Repetition of product content management tasks increases workload on company staff
• Not enough depth and detail to the product information being provided to customers
• No single view of analytics on product information for decision making
• Lack of support for cross sell/up-sell in individual channels and across channels
• Multiple, manual approval processes leading to long “new product introduction” processes
• Disjointed, long lead time preparing products for market

All of this can result in lost sales, reduced customer satisfaction, and inefficient processes.

There are four areas that Agility attempts to address through what it calls “PIM for Agile Commerce”:

(1) Master data management for products – consolidating and coordinating information from internal source systems such as Enterprise Resource Planning (ERP), and from external sources such as suppliers. Agility offers integration via Application Programming Interfaces (APIs), XML import/export functionality, a Content Gateway, support for various ETL tools, and an Excel plug-in, as well as a Supplier/Vendor Portal.

(2) Agility provides a wide range of support for the “product journey” that has to be managed by a product manager or merchandiser. “Gadgets” for tasks such as browsing, searching, editing, previewing and analyzing product performance can be grouped into “workspaces” that are appropriate to different roles within the company. This allows product managers to quickly locate, view and enrich a product or series of products with additional attribute data and assets, and create up-sell and cross-sell opportunities and subsequently approve the product for the next state in the workflow. Agility is strong on content management, and it tracks all of the digital assets created for a particular product – digital photographs, portable document format (PDF) files, digital movies, Office documents, etc.

(3) The next area addressed by Agility is marketing communications and content syndication, which the company refers to as “template-driven commerce”. A particular product and its associated content can be edited as needed, then sent for approval within the context and style of the selected channel.

So the team can use the core product information developed in the “product journey” to publish information to web sites and e-commerce platforms, comparison sites, e-mail campaigns, social networks and smartphones. Providing a single place for content to be managed, across all of a company’s products and marketing channels, removes the silo-based approach to managing product data. Preventing inconsistency across channels protects the company’s brand.

(4) The final area is what Agility Multichannel calls “design-driven commerce” – basically integrating tightly with a design program such as Adobe InDesign, which pulls product information into a design environment that can produce various artifacts. These include materials for stores and call centers, iPads and other tablets, flyers, ads, catalogs and brochures.

Agility Multichannel provided a great overview of the benefits of a single product information repository. The application can manage all aspects of product information, supporting the editing and maintenance process typically owned by a product manager or merchandiser, and publishing through both a template-driven approach (creating feeds that can easily be used by downstream applications) and a design-driven approach, supporting more creative and varied layouts.

The company has a vision of becoming the “de facto” choice for product information management. It hasn’t made any move yet towards becoming a multidomain MDM solution, preferring to remain focused on improving its functionality for product information management (PIM).

Agility’s customer base is split roughly 50/50 between Europe and North America, and it works with 10 channel partners to deliver its solutions. Growing its base of channel partners is a big focus for the company over the next few years.

The product has strong support for product taxonomies, and supports a best practice of having the product classification drive its attribution. In other words, the attributes for a product vary by what kind of product it is. A product can exist within several classifications or hierarchies, and the application can provide security down to the attribute level.

Agility has a very flexible, object-based data model, and supports both Oracle and Microsoft SQL Server as its database management system. The product has built-in workflow support and provides queues for issues that have been routed to data stewards for resolution.

Here is a view of the product’s high-level architecture:

From an integration perspective, Agility offers many upstream and downstream options, as depicted in this diagram:

One interesting capability of the product is the ability to publish a series of products and their corresponding pictures and specifications to the Apple iTunes store in the form of an app. So companies can quickly and easily reach the world’s largest and ever-growing “digital marketplace” with their products.

We were impressed with Agility Multichannel’s innovative approach to product MDM, with its emphasis on collaborative authoring and workflow, and the many target channels, platforms and applications it supports. The company clearly has some stiff competition out there in the PIM and product MDM market, but I think their message will resonate with many customers in the business-to-business, retail, and manufacturing industries. The number of creative tools and digital publishing mechanisms is distinctive, beyond what we’ve seen from other solutions in the PIM / product MDM space.

Dan Power is the publisher of Hub Designs Magazine, and the Founder & President of Hub Designs, a leading consulting firm specializing in master data management (MDM) and information governance. He can be reached via http://hubdesigns.com/contact_us.html. 

Join us for this hard-hitting webinar on Thursday, May 2nd at 2:00 pm EDT (1:00 pm CDT, noon MDT, 11:00 am PDT).

Customer, Product, Employee, Supplier – business problems don’t involve just a single domain of master data. And information can’t be managed effectively or accurately through multiple master data management (MDM) silos. In fact, this can defeat the whole purpose of your MDM initiative.

Multidomain MDM gives companies the ability to improve efficiencies and consistency in business operations, add value and insight to customer interactions, and drive cost out of the information supply chain.

Join us for this webinar, sponsored by Stibo Systems and Hub Designs, and learn:

• Why multidomain MDM is more cost-effective compared to a single domain approach.
• How your company can benefit from multidomain MDM and achieve a simpler architecture, better data governance, and more accurate analytics.
• How multidomain MDM supports cross-function / cross-team collaboration and improves business performance and effectiveness.
• How multidomain MDM can save your master data initiative from failure.

Speaker: Dan Power is the Founder & President of Hub Designs, a global consulting firm specializing in developing and delivering high impact master data management (MDM) and data governance strategies.

To register, just click on the following link: http://bit.ly/StiboHub.

Please share this on Twitter: On @HubDesigns: “Multidomain #MDM: The Best Value For Your Company”, by @Dan_Power at http://wp.me/p5Tdn-Wx #DataGovernance

He will cover Oracle’s strategy, plans for innovation, and the company’s relationship with its customers. Rae Wong, a principal analyst at Constellation Research, will moderate the session.

The first question to Mark Hurd was, “what does it mean to be an Oracle customer?” Mark answered that Oracle is in the middle of transforming, having acquired one hundred companies since the PeopleSoft acquisition. By the end of May, Oracle will have spent $4.8 billion on acquisitions this fiscal year. Between the acquired intellectual property and organic R&D, Oracle feels that it has the best-of-breed technology. Oracle has set its sights on innovating and leading in the cloud.

“From a customer perspective, do the customers always understand the acquisition strategy?” The answer was “the good news is we’re doing a lot of stuff. The bad news is we’re doing a lot of stuff.” Assuming that people probably don’t know that much about Oracle’s strategy; that it has such a huge portfolio, Oracle realizes that it has some work to do in communicating its strategy to its customer base and the marketplace in general.

The audience was mostly technologists. Oracle made an announcement today that it’s taking its applications and putting them “in memory” in order to increase speed. We want to know what’s happening really fast. In its internal operations, Oracle can now understand what’s going on and what’s been happening some 15-20 times faster than before.

It’s a matter of getting hardware and software integrated together. Larry Ellison made a comment recently that “the greatest innovation coming to software right now is hardware”. But the overall Oracle stance on innovation is: (a) realizing that Oracle customers need to save money in some areas, so they can spend more on innovation. Capturing customer intent via social media – since mobile devices are now in 2 billion people’s hands – is critical. Customers are now more demanding. Now the sale is between you, the customer, and everyone the customer knows, can Tweet to, and talk to on Facebook. So Oracle is putting a lot of energy into the customer experience. Oracle has done a string of acquisitions – ATG, Endecca, FatWire – and putting those capabilities into Fusion, into Siebel, and other applications. This trend towards “big data” can drive companies towards a broader, global, fully integrated “360 degree view of the customer”.

The move to “the cloud” is all about the physical location of the computer, but also (according to Oracle), the intellectual property. Oracle wants to give the customer a choice between traditional on-premise solutions and solutions in the cloud, and to choose between the two depending on the context that makes sense to the customer – by geography, by business unit, by application, by functional area, etc.

Oracle’s average customer spends a third of their budget on development and testing. Customers have built their infrastructure to “peak usage”. Now people can build to average usage, and buy extra capacity in the cloud “by the drink” (i.e. as they need it).

Oracle doesn’t think it’s late to the cloud, it thinks it’s first. Oracle will do just about any combination or hybrid that makes sense for the customer.

Another question was “Oracle is hard to work with.” Mark Hurd made a joke that this was the first time he’d heard this. He did acknowledge the point, though, realizing that Oracle has work to do in this area. When Oracle puts a dedicated account representative on site (typically at its largest 250 customers), customer satisfaction and loyalty is off the charts.

Oracle’s direction is to have more salespeople and to have them be more specialized (with more architects), and more of the integrated relationships, with key account directors. Oracle wants to make it easier to work with the company, not harder.

“Is Oracle a disruptor in the marketplace, or is Oracle being disrupted?”

The reality is that IT has got to simplify – it’s too hard. Bad news – we’re in a very weird industry. What we do is convince customers that they have to buy all this stuff, and somehow put it all together. Companies spend almost $900 billion per year in this industry. Most of it is spent trying to glue parts together. This makes no sense.

“I don’t know what the OS is on my car – when I turn the key, it just works. But we’ve got ourselves at the other end of the spectrum. One bank in this country alone spends $10 billion per year on IT. They spend most of their time gluing infrastructure together. So the real issue that has to get disrupted is this need for integration.

The business processes are too complicated, and business people that don’t understand the IT implications of what they’re asking for have driven them. The disruption that’s going to occur is a step back to a more IT-savvy management team, making these hard decisions about simplifying IT, and getting their money out of things that don’t add value, and into things that do. Oracle will be driving this trend.”

The cloud has driven the trend towards simplification – you just turn it on and it works. And it’s managed for you.

In Constellation’s research, two-thirds of the IT budget is used just to keep the lights on. The shift in power is towards the line-of-business and away from IT.

Mark’s first reaction when hearing “I have a bad IT organization” is “you have a bad management team”. IT works for you – if you look at basic processes like taking an order, those are processes in the business, not in IT. And those processes are too complicated. Sometimes, more than 100 applications are involved in relatively simple things such as dispatching service technicians or taking orders. IT sometimes can’t diagnose when processes are unique or not. The real answer is – they’re not unique. We’ve got to drive towards simplification. Those are hard discussions, at the top of the company, and without those, you can’t simplify IT.

On cost savings and innovation, there was a question about third party maintenance – for software and hardware. Oracle typically doesn’t like people messing with its intellectual property. Its objective with maintenance isn’t just support, it’s a subscription that provides you with perpetual rights to the software. And a lot of the costs in upgrading from one major release to another are to re-implement the customizations made to the current release. So when customers are angry about upgrade costs, they have to realize most of it is self-inflicted. There are other ways to reduce costs than going to third-party maintenance. Oracle is very concerned about its brand, so it won’t let any third-party organizations or partners put Oracle applications in the cloud for quality control and brand integrity reasons.

“What is the role of users groups in shaping the experience of customers of working with Oracle?”

Oracle wants its customers’ input. It has over half a million customers belonging to user groups all over the world. “You’re our most valuable assets we have in the company, and any representation of the customer is important to us. You may not like everything you hear, and we may not like everything we hear. But we need Oracle customers to be thrilled, happy and ‘zealots’ in the marketplace.”

Oracle wants to be easier to work with, and realizes it needs its customers.

Mark Hurd loves being in the tech sector, because of its dynamism. Over time, the IT industry will morph together – blurring the distinctions between hardware, software and services.

Please post this on Twitter: Reading @HubDesigns: ‘COLLABORATE 13 Keynote by Mark Hurd, Oracle President’ at http://wp.me/p5Tdn-Wp (please retweet)

Today, I attended the keynote speech by Aron Ralston. He was an experienced outdoorsman out for a walk in a Utah park when a dislodged boulder trapped him for five days. His story was captured in the movie “127 Hours”, based on his book “Between a Rock and a Hard Place”.

After watching a trailer from “127 Hours”, Aron took the stage, making a joke about the April blizzard expected to hit Denver tomorrow, and laughing about being the guy that cut his arm off. The way he sees it, he’s only gained from the experience, which happened ten years ago this month.

Aron related his story to everyone in the audience – whatever our “boulders” might be – and how he found his way to being grateful for the boulder.

“Our boulders in our lives are gifts – they show us what is important to us. Our relationships at home, the source of our greatest vulnerability – divorce, depression, whatever it is for you – we will always be meeting these challenges.”

The area in southeast Utah, in “Canyon Country”, is known as Robbers’ Roost, because Butch Cassidy and his gang used to hide out there after robbing a train or stealing horses. The area is so remote it doesn’t have a paved road in the eastern part of two counties.

In exploring that fateful day, Aron came across two young ladies out for a little adventure of their own. Aron made a great joke about the difference between their reaction to him and the reaction of the two actresses in “127 Hours” to James Franco. He described the way the light bounces down the twisting canyon as “otherworldly”.

The girls invited him to cut short his hike and go out with him to grab a burger and a beer, but Aron deferred to go see the Grand Gallery, which is over 100 cave paintings preserved for thousands of years.

After parting ways, Aron continued down to the lower slot where all the tributaries of the Blue John system come together. He entered a drop-off around 2:00 in the afternoon, walking around a bend to see a beautiful scene of sandstone and light.

Around 2:45, he entered a gauntlet of boulders that have accumulated at the bottom of the canyon. He confronted a boulder that represented an obstacle, which could also be a steppingstone.

Aron described how he was fully extended, hanging from an 800-pound rock, when the rock pulled free, coming straight at his head. His right hand slipped between the rock and the wall of the canyon, and his hand disappeared into an impossibly small space between the boulder and the wall.

Aron showed a clip from the movie, recreating the scene. You could hear a pin drop in the auditorium. The pain was incredible, driving him out of his mind for more than an hour. But he told a joke to lighten the mood, and the audience caught its breath and moved on with him.

Aron realized that his water supply was his lifeline, and that he had to stop and think in order to get out of this situation. His emotional response was not helpful at all. When the crisis comes – when your boulder hits you – the fear response is not your most effective response. Stop, think, observe, find options, plan.

He realized he was not going anywhere. His wrist had been crushed to the width of his pinky finger. The pain had become irrelevant. The pain was not going to kill him, but his response to the pain might.

The option of cutting his arm off to get free started to occur to him, as a last resort. The first night, he spent fifteen hours trying to chip the bolder away. He tried rigging a system of ropes to try to lift the rock off, but that option failed.

No one was coming for him, and it would only be a day or two before he died there alone. He went out on a five-day trip, and he hadn’t told anyone where he was going. After about 24 hours, he was back to the option of amputating his arm.

He realized he would need a tourniquet to survive the 8-10 mile walk out to his truck, and a 57-mile drive out of the park. It seemed like a slow act of suicide to him. He turned on his video camera and started taping a message for his parents and his sister. Aron was briefly overcome with emotion as he recounted the words in that message.

Aron asked the audience what they would say if they had a video camera and they were in that same situation. It would reveal something very powerful about you and what’s important to you. That was the first gift of that trap. The rock showed him what was truly important in his life.

What was important were not his achievements in life, in high tech or becoming a mountain guide. What was important in the end were his loved ones. “I love you, thank you, I’m sorry, goodbye.”

It’s not just what you do – it’s who you are. How do we relate to one another, especially the people closest to you?

By the second and third days, Aron was willing to trade the uncertainty of dying on the way out vs. the certainty of dying in the canyon. He was down to his last couple of ounces of water, and a couple of bites of a mummified burrito.

By the fourth morning, he tried using his knife as a dagger rather than a saw. His knife came up against his bone. He hit bottom, because he realized his dull knife wouldn’t be able to cut through the bone.

After drinking his last water and eating his last food, he survived the fourth night, shivering in 40-degree temperatures. He had lost almost 30 pounds of body weight in those four days.

On the fifth day, he ran out of options and out of hope. He realized it was not up to him. He gave up the illusion of control. He felt relief at knowing in his heart there was no way to guarantee anything in this life. He felt a sense of peace, and spoke to his family on camera again. He felt their warmth and their love. “The force that’s more powerful than the will to live is the will to love.”

On the fifth afternoon, he felt like the end was going to come quickly. He wrote his name and birth and death dates, plus “RIP” on the sandstone wall of the canyon.

He left his last will and testament on the video camera, and he hunkered down to wait for the end. His watch beeped, and April 30^th turned into May 1^st. His epitaph on the wall was outdated!

He knew he was not going to see the dawn. Convulsing, a couple of hours later, he stood up and stepped free of the rock. He could look back and see his body. He walked down a passageway into a living room. He saw a little boy playing with a truck. He scooped the boy up with his left arm and a handless right arm. The boy’s eyes locked on his, and in the next moment, he was back at the boulder. But the vision of that little boy changed everything. If that vision was true, he would get out of there. A few hours later, the sun came back over the desert, and he made it through the night.

At 127 hours, he got a flash of insight. He would break the bone using the boulder. He crouched down and bent his arm until it broke. Then he realized there were, in fact, two bones in the arm.

He stood up straight, reversing the process until the second bone broke. He felt this wasn’t horrible at all, but that it was beautiful. The second gift of the boulder was to realize what was possible for us. He felt an excitement at the euphoria of getting free. When the knife hit the nerve, that moment was the crux. It felt like he incinerated his arm in a vat of liquefied metal.

When he opened his eyes again, he was smiling. He cut through the last piece of skin, and he was free. He stepped out of his grave and into his life again.

The ecstasy of that moment, realizing that he would get his life back in one moment, was too much to handle. He caught his breath, put on the tourniquet, and took one final picture of the boulder and his severed hand. He took that picture to say thank you to the boulder for its gifts.

The boulder had shown him something so beautiful about life.

He started to walk out through the slotted canyon, stepping out into the sunshine. He rappelled down a 65-foot drop to a pool of water, gulping down almost a gallon of water.

Chanting “one more step” to himself, staying focused on what was right in front of himself, trying to avoid any stupid mistakes. He hiked 6.5 miles, with his eyesight blurring and his heart pounding. Just when he thought he couldn’t go any further, he came across a family. He called for help, and they started running towards him.

They gave him water, and said “we need to get going”. There were rescuers out looking for him, but he only had minutes left.

A helicopter landed nearby, and the pilot got him onto the chopper. His mother made that rescue happen. When she found out that he hadn’t turned up for work, she went into action.

He made it to his sister’s wedding later that summer. He continued climbing, to finish climbing the last of the 59 mountains that were 14,000 feet or higher, an achievement no one else has done.

“You’re not alone. It’s not easy. It’s not enough to enrich our own lives. We’re here to enrich the lives of others. The gifts of the boulder were to know what’s important to us, what’s possible for us, what’s extraordinary in us.”

Aron ended with a picture of his son Leo, the little boy he envisioned that last horrible night, the courageous little lion that gave him what he needed to get through that ordeal.

“Our boulders – smile at them, be grateful for them, maybe even to embrace them, to welcome them into your life because they will give you gifts.”

The audience gave him a standing ovation at the end of his talk. Thank you to Aron Ralston and to COLLABORATE 13 for arranging for him to speak.

Please post this on Twitter: Reading @HubDesigns Magazine: ‘Keynote by Aron Ralston at COLLABORATE 13 Conference’ at http://wp.me/p5Tdn-Wd (please retweet)

It’s astonishing to realize that Pitney Bowes was founded in 1920. Most of us probably consider this company a postage metering, mail handling and printing giant. A good deal of the company’s business still focuses on solutions for postal and print services but Pitney Bowes continues to build out digital solutions as a complementary line of business.

Looking at its multi-decade history, it’s apparent that Pitney Bowes is an innovation company that has been providing data management solutions for quite some time. From the Pitney Bowes website:

Delivering more than 90 years of innovation, Pitney Bowes provides software, hardware and services that integrate physical and digital communications channels. Long known for making customers more productive, Pitney Bowes is increasingly helping companies grow their business through advanced customer communications management.

Pitney Bowes has very deliberately built out offerings for data management, analytics and business insight with a strong customer focus. Multiple acquisitions have been made over the years to broaden – and deepen – solution capabilities.

Navin Sharma, director of global product strategy, and Robert Cruz, vice president of business development, met with the Hub Designs MDM Think Tank to talk about the approach that Pitney Bowes is taking for master data management and data governance with the Pitney Bowes Spectrum platform.

Solving Customer Problems

Most customers are based in North America, but Pitney Bowes is expanding around the globe.

Pitney Bowes has built a strong beachhead in services for multi-channel marketing and customer communications, including customer analytics and segmentation. Other solution areas include risk, fraud and security management for financial institutions and government agencies, and customer-related services for the telecommunications industry.

Companies that have adopted the Spectrum Platform for data management include very large enterprises and government agencies such as Citigroup, AT&T, U.S. Homeland Security, and FedEx. Facebook recently became a customer for location-based intelligence to integrate with its complex social network.

Vision for MDM

To understand the positioning of the Pitney Bowes Spectrum platform for MDM and data governance, it’s useful to take a quick look at how Pitney Bowes has evolved its data and digital communications business units. Many of its services over the years have revolved around customer, product, location and related activities, bolstered by technology acquisitions.

The Pitney Bowes focus on helping their client companies work with accurate customer data means that Pitney Bowes understands the importance of the customer for any enterprise. Pitney Bowes is also in a great position to really understand how their client companies want to operate their businesses and the challenges that they face.

MDM is a logical step for Pitney Bowes data management. To decide on the direction and technologies for the Spectrum platform, Pitney Bowes took their time to study the current vendor landscape for MDM solutions, and to consider what needs and pain points their customers have had over the years.

Pitney Bowes didn’t agree with most of the other vendor approaches, which Navin termed as too big, too complex, too expensive, and too dependent on infrastructure. His other comments on current vendor offerings: limited views of the master data, and lack of movement towards the deft handling of big data volumes especially complex social media data / content.

So Pitney Bowes decided to do something different that could handle the new aspects of data today and where it may be heading in the future. After all, master data management should reflect how businesses operate and evolve.

Customers and Relationships

Navin noted that for many enterprises data-related processes need to align with the real world intricacies that companies face, especially in terms of complex relationships and hierarchies with respect to customers and their extended networks. He went on to express that MDM should help the enterprise see the customer’s network of relationships as well as reveal non-obvious relationships and spheres of influence – these views frequently are not possible with traditional CRM systems.

Pitney Bowes looked at many different infrastructure approaches to data hubs: Hadoop, columnar, document, relational – but chose something else: the graph database. At the heart of Pitney Bowes Spectrum is a NoSQL graph database engine to create hubs that better enable agility and responsiveness to enterprise needs and constant business volatility.

As Navin pointed out, the graph database structure reflects the increasingly connected nature of data and the relationship networks underlying customer behavior. It allows for the rapid modeling of real world entities and their connections across roles, processes and interactions

The choice of a graph database infrastructure reflects RDF principles for organizing data: model the data to the actual business. The high level structure of a graph database consists of nodes, properties and edges. Graph databases function without indexing since the relationships between data elements “point the way”. Nodes pertain to entities such as people or activities; properties provide information regarding nodes; and edges connect nodes to other nodes or properties. It’s through edges that patterns of data relationships and interconnectivity can be discovered.

The Spectrum graph database runs on Linux as well as other flavors of Unix, and Windows. Spectrum enables high scalability and fast performance when crunching through large data volumes. Performance is optimized for queries, especially for complex networks of data. Additionally, Spectrum is agnostic regarding hardware and OS environments, and supports multi-domain data management.

Expose complex hierarchies and relationships through visual analytics

Source: Pitney Bowes

Spectrum Capabilities

For Spectrum, Pitney Bowes is pursuing this mantra: Simple. The offering is self-contained, with no dependency on third-party app servers. Leveraging many years of providing data integration solutions, Spectrum covers just about any way to connect to sources. Data virtualization capabilities are also available to work with complex data types.

Spectrum is a SOA platform (Service-Oriented Architecture) – every capability is available as a service, with extensive support for APIs and external web services. The platform has very clean UIs for the design tools and easy-to-use, responsive modeling tools. Visual design elements can be added by applying themes to make flows and models easier to understand and manage.

Data quality and matching are well integrated into the offering. Again, Pitney Bowes has logged a lot of miles in the data quality world, with a long history of services for managing names, addresses, and identity through extensive and sophisticated capabilities:

• Rules and algorithm-based matching strategies
• Match-rule analysis
• Best of breed record creation
• Open Parser – define grammar by domain, country, language
• Multi-cultural name recognition
• Identity matching algorithms with global name variation data
• Transliteration
• CASS, SERP, AMAS, SNA, UPU standards based global address verification
• Extensive data enrichment; interface with multiple third-party tools

Data governance capabilities include role-appropriate user portals for business stewards (with browser-based access) and IT stewards. Dashboards are provided for monitoring current issues, to-dos and scoring of data quality. Data stewards have access to detailed profiling and monitoring for low-level data quality issues. Since data governance benefits from team effort, Spectrum supports collaborative discovery and issue management

A key capability that has been brought into Spectrum is based on Pitney Bowes location-aware intelligence and global geocoding capabilities. Pitney Bowes asserts that the ability to visualize spatial data and understand relationships between specific locations will allow organizations to make more strategic business decisions.

Other features: design tools include an automated bi-directional metadata flow when new components are added; and analysis capabilities are available for data in the hub. Spectrum supports flexible MDM deployment styles: Consolidated, Centralized or Co-existence.

 Source: Pitney Bowes

Customer Insight: Analytics and Big Data

The Pitney Bowes focus on customer comes through their articulation of the value of Spectrum: it’s less about data management per se, and more about the impact on the customer’s business. Pitney Bowes sees actionable insight as the real purpose of data management infrastructures. They appear to be taking care to provide a high performance infrastructure to bring reliable data into processes to derive that insight, as well as the subsequent decisions and actions.

Analytics capabilities include support for predictive analytics, BI reporting and data visualization.

Spectrum also handles big data mining and analytics, leveraging MapReduce-based implementations for large batch processing. In Spectrum, Pitney Bowes combines social and spatial analytics with the more traditional BI and analytics options, making the claim that it is the only MDM vendor that “can add Spatial, Social and Predictive Analytics dimensions to the master record”. Such an approach can provide greater depth for customer intelligence that can aid product, marketing and sales strategies and execution.

Predictive modeling can be used for customer activities such as the propensity to make purchases under certain circumstances or to identify the interest of particular customer segments for certain products that they have not yet purchased. Such analytics are fueled by the Spectrum ability to navigate social networks and spheres of influence. This type of modeling can also work well for processes that handle risk, fraud, and security analytics.

The Future of Data at Pitney Bowes

Pitney Bowes software solutions have been included in a wide range of Gartner & Forrester benchmark reports for several years: from Managed Print Services to Data Quality Tools to ETL and Data Integration. And Pitney Bowes applications and services run the gamut from call center optimization to customer communications and digital marketing.

This puts Pitney Bowes in a unique position to truly understand the data pain points experienced by many kinds of companies. It also means that they understand a lot about how different companies do business, and why a sharp customer focus is a top competitive differentiator, both for themselves and their customers.

Pitney Bowes has set out a new way to offer master data management capabilities. This company has the chops to understand why their approach should work well for many enterprises. It’s likely that Pitney Bowes will continue to innovate their approach to MDM and that the platform will continue to be responsive to customer needs as organizations and business conditions evolve over time. Pitney Bowes already offers cloud services for other solution domains – this could well be the next move for the Spectrum platform.

About the Author

Julie Hunt is the editor of Hub Designs Magazine, and an independent software industry analyst and consultant for solution and customer strategies. 

Due to recent trends, executives are finally getting serious about treating data as a corporate asset. In the financial services and healthcare industry, there are new trends and regulations. In the insurance industry, there are widespread moves towards IT modernization.

But executives are busy – how do you get their attention? How do you get them to understand the role that data plays in running the organization better. People still tend to think it’s an IT issue. How executives lead informs their perspective on IT and its business value.

Some executives see anything relating to data governance as a bureaucratic exercise. Jill referred to the “kick-off and cold cuts” approach. Everyone sits around the table, eats lunch and complains about the data. Six weeks later, there’s a meeting with no free lunch, fewer people show up, and it again turns into a complaint session. It turns into a people-driven exercise, at the expense of a process-driven approach that is focused on the need, pain or problem that we’re trying to solve.

As far as Return on Investment (ROI), Jill agrees that we can monetize data. But we often don’t have the “before” measures in place to measure the “before and after” ROI picture.

With the time and money that simple efforts like inventorying our data, what could we have done?

Part of the problem with data governance efforts that have failed, part of the problem is that data governance is a “squishy” term, with a lot of variation in terms and no unified vocabulary. We need to apply leadership to fix this, because it’s a cultural issue.

Jill had a great chart showing the evolution of data awareness, from the initial value proposition of data warehouses as “mainframe data offload” to CRM, to data quality and data integration, to data stewardship and organizational efforts at data governance, to process and policy driven efforts at data governance.

Because a lot of knowledge about our data is tied up in “tribal knowledge”, we need to move more towards a policy and process driven approach, so we’re not so dependent on a few people with in-depth knowledge of the business and how it uses data.

The first few stages in the evolution of data awareness are marked by a focus on Platform. The next few are focused on Integration. Only the last few stages in the evolution are process and policy oriented.

It’s all about selling the idea (and how hard this is) to the C-level executives. But it’s an uphill challenge, because the executives sometimes don’t have the attention span.

When you turn the conversation to “where is the business going?”, then it’s harder for executives to say “No”.

Jill gave an example of how a CEO at an insurance company defined their three corporate strategic goals. But it’s one thing to define corporate goals and objectives, but it’s another to execute on them.

If we take corporate objectives and distill them down into initiatives and strategies, we have a better chance of achieving them.

The map is from the corporate vision, to the overall strategies, to the key focus areas required to achieve those strategic objectives, which give rise to the initiatives required in each of those focus areas.

Then you look at the initiatives and see where you will require better master data to carry them out. So you wind up creating a strategy map for data that corresponds to the overall corporate strategy map.

This makes it very hard to say “no”, because if you agree that the corporate objectives are important and that certain initiatives require better access to data, then you have to agree that strategic initiatives around data are necessary.

But a data governance framework can really highlight capability gaps – things the organization doesn’t currently know how to do.

The question is “can we do data management without data governance?”. The answer is yes, we’re already doing a lot of those things (although it’s not ideal).

But you cannot do data governance without data management (if you did, it would turn into an academic exercise very quickly).

So the question is – if you had a good strategic roadmap and data governance framework, would you be able to execute?

The data stewards are the ones in the middle that are the interface between the business and IT.

Jill gave a good example of Harrah’s from the gaming industry. They were lucky in that they had the metrics from the “before” time period, and from after improving their data capabilities.

In terms of valuing data, you need to look at that in the context of its usage.

So where do we go from here? Data governance is not a series of meetings – interest erodes and people lose interest. There’s a trend towards “regimes” – where only a part of the group has to work on a particular problem. You can use the Responsible / Accountable / Consulted / Informed (RACI) approach to figure out who needs to be involved in any particular issue.

This brings people to the table. They realize that they’ll have a say in the data that is meaningful to them, without having to be involved in everybody else’s issues.

The “aha” moment is when you’re able to tie improving information management capabilities directly to corporate strategy. It makes it very hard for executives to say “no”.

Also, business intelligence is becoming a commodity. So BI is becoming easier and cheaper than its ever been. But data is getting harder and harder. Not only are we getting more and more data, but it’s coming from more and more places. Five exabytes were generated by business in 2008. Now, business are generating that amount every two days. Data is getting harder, while reporting and analytics getting easier.

Jill moved on to talking about building a comprehensive, sustained data strategy. Understand what’s important to management now. Tie it to your corporate goals, make it an ongoing program (not a project), look at where the company is investing externally – that’s a good clue about what the strategy is.

Work within your culture to avoid saboteurs. Some companies have the “culture of no”, where people aren’t rewarded for saying “yes” to new things. Data governance looks different at organizations that are top down vs. those that are bottom up.

Understand your current state before making the pitch for data governance.

Choose sponsors based on who owns the business problem. Focus on the need, pain or problem that we’re trying to solve. Don’t automatically go to the “friends of the data warehouse”.

Tie the operational metrics for data to the context of the business initiative.

In closing, let’s talk about criteria you can use to see whether your company treats data as a corporate asset:

1. the asset has value
2. the value is quantifiable
3. the asset helps the company meet its strategic objectives
4. it requires specialized skills to build and maintain

At a lot of companies, one or more of those criteria are in place. But the tough questions are: are you giving it resources comparable to your other corporate assets? Are you dedicating technology comparable to your other corporate assets? Are you allocating funding to data relative to other assets (does data have its own budget)? Are you measuring the cost of poor, missing or inaccurate data? Do you understand the opportunity cost of not delivering timely and relevant data to the business?

If not now, when? When we’re managing exabytes instead of petabytes?

Somebody’s got to do something, and it’s sad that it has to be us. But we’re the ones that know how to solve these problems.

I’ve known Bill for a long time, about 7 years now, and I respect him more than almost any other MDM professional I know.

Bill was originally at Siebel and came to Oracle as part of its acquisition of Siebel in January of 2006. When I was still at D&B, managing its alliance with Oracle, he invited my colleague Jay Daly and I over to his house after a day of meetings at Oracle headquarters.

We had a great time, and I was struck by someone going out of their way to make a partner welcome in their hometown.

I’ve stayed in touch with Bill since leaving D&B in 2007. We mostly see each other at various conferences, and we co-presented at Oracle OpenWorld one year.

The reason Bill called me today was that he wanted to let me know that he had made a move. For the last few years, Bill has been part of Oracle’s internal IT team, focusing on improving how Oracle used its own MDM technology to manage its customer information.

But at Siebel Systems, Bill was involved in the original development of what was called Universal Customer Master (UCM). That product was renamed Oracle Customer Hub after Oracle acquired Siebel. While Bill was lately focused on implementing UCM for Oracle internally, he continued to work closely with the product’s development and product management teams.

And now he has “gone over to the dark side” and joined the product management team for Oracle Customer Hub. I don’t know his exact title but he seems to be the new “owner” of the product from a product strategy perspective.

I’m very happy for Bill, and I think Oracle has made a great decision to bring his leadership into its MDM product team.

And it was nice that I was his first call outside of Oracle to announce the move today.

Good luck in your new role, Bill. Wonder if you’ll be able to attend the Oracle Applications Users Group COLLABORATE 13 conference in Denver in a few weeks?

Dan Power From Hub Designs Presenting at 2013 Gartner Master Data Management Summit

Boston, MA, March 14, 2013 – Hub Designs (http://hubdesigns.com), a global management and technology consulting firm, announced today that its President, Dan Power, has been invited by Stibo Systems to present on its behalf at the annual Gartner MDM Summit on March 21, in Grapevine, Texas.

Mr. Power will be presenting during the Solution Provider Sessions on Thursday morning (March 21, 2013) at 9:15 am in Texas Ballroom D at the Gaylord Texan Hotel & Convention Center. Mr. Power’s presentation will discuss how to establish successful master data management and data governance programs, and the accompanying benefits to revenue, process efficiency, compliance, and profitability.
With over 25 years in the master data management and data governance space, Mr. Power is a highly sought after thought leader, who speaks frequently at conferences, writes white papers, produces webinars, and advises companies on how to be successful with their data governance practices.

About Hub Designs

Hub Designs is a global leader in the development and delivery of high impact master data management (MDM) and data governance strategies. The company publishes Hub Designs Magazine, one of the first online publications specifically for the information governance industry. The firm’s Thought Leadership practice produces white papers and webinars, and Hub Designs’ President, Dan Power, is a frequent presenter at conferences and trade shows. For more information, please visit http://hubdesigns.com.

About Stibo Systems

Stibo Systems gives data direction by providing organizations with a leading multidomain master data management (MDM) solution. Stibo Systems enables its customers to better manage enterprise intelligence on a global scale, improve sales, and quickly adjust to changes in business requirements. Stibo Systems’ STEP technology is a flexible, uniform MDM solution that provides a single trusted source of operational information for the entire enterprise. Stibo Systems is a subsidiary of the privately held Stibo A/S group, originally founded in 1794 with corporate headquarters in Aarhus, Denmark. For more information, visit http://www.stibosystems.com.

About Gartner Master Data Management Summit 2013

The Gartner Master Data Management Summit is specifically designed to drive organizations of all MDM maturity levels toward realizing these benefits: from those just getting started with MDM, to those looking to move up the maturity curve, to those seeking advanced insight on the future of master data. At the Summit, Gartner analysts help organizations establish a solid justification for MDM and identify where and how MDM can increase service levels, drive growth and fuel transformation. More information can be found at http://www.gartner.com/us/mdm.

About Gartner

Gartner, Inc. (NYSE: IT) is the world’s leading information technology research and advisory company. Gartner delivers the technology-related insight necessary for its clients to make the right decisions, every day. From CIOs and senior IT leaders in corporations and government agencies, to business leaders in high-tech and telecom enterprises and professional services firms, to technology investors, Gartner is the valuable partner in over 13,000 distinct organizations. Through the resources of Gartner Research, Gartner Executive Programs, Gartner Consulting and Gartner Events, Gartner works with every client to research, analyze and interpret the business of IT within the context of their individual role. Founded in 1979, Gartner is headquartered in Stamford, Connecticut, U.S.A., and has 5,500 associates, including 1,400 research analysts and consultants, and clients in 85 countries. For more information, visit http://www.gartner.com.

###

Contacts:
Amy van Aarem
Hub Designs
+1 781 749 8910
amy@hubdesigns.com

Defining high quality data is often a tricky task.  Sometimes, it is as obvious as a field left blank, but often it’s not so clear.

That’s why we need standards, to help us see the good data from the bad. Just like in manufacturing, it’s difficult to claim a part has quality problems unless there are specifications that spell out the standard that the part must meet.  Equally true for our master data. We need to create the standards, communicate them, measure to them, and ultimately manage the data to meet those standards.  Setting the standards is critical and must include input from all who use the data, so that all areas’ needs are represented.

For example, consider item description data. What’s a “good” description? That depends on the purpose for which you want to use it. A description can simply be someone’s attempt to label the item or it can follow standards that can then be used for many different purposes.

At Zebra, our item description could be used to help validate the correct classification of items, such as the type of printer – mobile, desktop, card, and others.  For parts, that description might be used to help validate the correct commodity grouping, hazard class assignment, etc.  But we need to create and maintain description standards in order to use it for these multiple purposes.

The Data Governance Office, with the help of IT, has implemented an exciting new tool to help us profile our master data for items, so we can identify standards, as well as concerns.  But the real excitement is that our new Data Quality tool can be used to prevent data errors.  We have been working with a couple of different groups to define validation steps to prevent data errors in Agile, which is the source of much of our item master data.  So for those of you who use Agile, be on the lookout for further communications to the Agile user community on these validation rules as they are implemented.

In our next Zebra data governance article, we’ll discuss the important role of the Data Steward.

On Tuesday and Wednesday of this week, I attended the 2013 Informatica Analysts Conference in Menlo Park, CA. It was a beautiful venue, and very well conducted and organized. On top of that, I learned a lot about Informatica’s product lineup and strategy for 2013.

Last year – 2012 – was an exciting year for Informatica’s MDM business. Most customers are now mastering multiple domains. Informatica won more than 75% of deals against its Tier 1 competitors. And, customers are now leveraging the Informatica Virtual Data Machine in a majority of MDM deals.

Informatica doubled its customer reference base and established the Informatica MDM Customer Success team.

There were a lot of impressive customer wins – ranging from very large companies to medium-sized companies. Many new industries – telecommunications, construction, and entertainment – were represented. And Informatica focused on delivering more value to existing accounts.

One positive outcome was a global MDM product advisory council held at a French customer, which highlighted how to treat a company’s best customers better through master data management. For example, this makes it possible to drive special offers by customer segment. The French customer showed its people running a custom Apple iPad app that connects to the Informatica MDM “back end”. And customers were helping each other and sharing their experiences at the product advisory council.

Informatica acquired a company called DataScout, which is now called Informatica Cloud MDM. Informatica now has truly hybrid customers using Cloud MDM with Informatica Multidomain MDM (referring to the combination as Universal MDM).

Informatica also announced its intent to acquire Heiler, a prominent product information management (PIM) vendor. Heiler is a German public company, so the acquisition takes 6-9 months and won’t be done until late 2013. Heiler grew most rapidly after the acquisition was announced. Heiler is most prominent in the retail, distribution and manufacturing industries.

Informatica had a noteworthy win at a major consumer electronics company. It was a highly competitive situation, dealing with a very large record count (1.5 billion records, including 300 million D&B records). The client ultimately selected INFA for three reasons. The company successfully completed a stringent proof-of-concept process, with 13 of 13 use cases in 1-2 weeks. Secondly, MDM is a big part of the customer’s overall strategic IT transformation, with only a 3-month implementation period. Lastly, Informatica provided connectivity to salesforce.com in the cloud.

Informatica launched MDM 9.5.1 late last year, with social media support, mobile client support, big data matching, effective date support, unified data stewardship, and other new features.

The company also introduced Informatica Cloud MDM “Winter 2013”, with multi-dimensional hierarchy support, drag and drop hierarchy management, and enhanced matching. This is built on the Force.com platform, so it runs natively on the cloud. The product sets itself up automatically within Salesforce.com. There aren’t a lot of other choices in the Salesforce.com AppExchange for MDM (besides Informatica Cloud MDM).

Here are some trends Informatica highlighted in the market place:

• More than half of the Informatica MDM “go lives” were multiple domains in 2012
• There was a significant increase in high volume, real time, transactional MDM use cases
• Rapid time-to-value and adaptability are key requirements
• Cloud applications like Salesforce.com, WorkDay and NetSuite, plus mobile apps, are leading to application migrations and hybrid application landscapes
• Customers are looking to vendors to provide technology, solutions and expertise
• Universal MDM, as Informatica calls it, is able to cover both business and IT needs.

As MDM is becoming more pervasively used in the enterprise, customers are asking Informatica to plug into all of their various MDM solutions and make sense of them, while co-existing with them. Informatica calls this “Universal MDM”.

Informatica now has the former Siperian MDM hub, plus soon the Heiler Product Information Management system, and the Data Scout “Cloud MDM” system. Because of the Informatica Multidomain MDM hub, you can bring those other domains in and create relationships across domains.

Informatica and Heiler are still operating as two separate companies, but they are partnering well. With its acquisition of Active Endpoints, Informatica can add process automation to its MDM applications – it will be selling more MDM applications this year.

There is a lot of product innovation coming from Informatica, driven by themes such as the Cloud, Social, Mobile, Big Data, core MDM and solutions. Based on what I saw at Informatica Analyst Conference 2013, I expect strong growth, competitive strength and customer success in 2013 and beyond.

Hub Designs Magazine published an article in Nov ’12 on INFA MDM Release 9.5, to read this article by Julie Hunt, please click here.)

Are you thinking about attending the upcoming Gartner Master Data Management Summit, taking place March 20-22, in Grapevine, TX? Our friends at Stibo Systems and Gartner are offering a $300 discount on registration for the upcoming summit.

The Launch Pad for Assured Business Outcomes & Transformation

Mobile, social, cloud and analytics are transforming the playing field. Businesses must leverage these new technologies to remain relevant, build partnerships, optimize costs and stay competitive. Yet these rapidly advancing technology trends also mean unprecedented potential for business growth and change, bringing new hurdles to maintaining a trusted, unified view of data assets.

Join us this spring at the Gartner Master Data Management Summit and discover everything you need to know to implement an effective MDM program, advance existing MDM efforts and ensure your MDM initiatives are on the right track.

We’re also excited to announce that Dan Power from Hub Designs will be speaking Thursday, March 21st at 9:15 am, on behalf of Stibo Systems. He’ll discuss what it takes to be successful with master data management and data governance programs.

Here are the details:

• Gartner MDM Summit 2013, March 20-22 2013 in Grapevine, TX (Dallas area)
• Register with Priority Code MDMSP1 to receive your $300 discount
• Click here to register, or call 1 866 405 2511.

Best regards — Dan

Dan Power
President, Hub Designs

This position is with a fast growing consulting firm, Hub Solution Designs, Inc.  It’s a challenging position with significant growth potential for an experienced person.

This integration architect will be responsible for driving the evaluation and selection process for an Enterprise Service Bus (ESB) at one of our Boston area healthcare technology clients, as well as building and managing relationships with client project managers, business owners and project teams.

Qualifications

Hub Designs is looking for a top-notch Boston-area person to join our team for a client engagement. We’re looking for someone with strong experience in enterprise architecture and data integration. This person needs to be “hands on”, with previous practical experience driving the evaluation and selection process for an enterprise service bus.

The project will involve preliminary requirements gathering and vendor assessment through a Request for Information or Request for Proposal process, probably followed by a Proof of Concept. There is potential to then move into detailed requirements, design and implementation. Excellent verbal and written communication skills are required.

The client is in the healthcare technology industry and will be selecting and implementing a Master Data Management (MDM) hub and data quality tool as part of a related initiative. So experience in MDM, data integration, and data quality tools is a big plus. A past track record in implementing applications based on MS SQL Server is helpful, as is familiarity with service oriented architecture initiatives, developing web services and using Agile development methods.

Next Steps

If you are from the Boston area, meet the above requirements and have the legal right to work permanently in the United States, please send your resume (in Word or PDF format) with a brief introduction including your name, complete address, best phone number and e-mail address to info@hubdesigns.com.

Help Us Spread The Word

You can cut-and-paste the following into Twitter: “Opportunity for Boston-Based Consultant with Significant ESB Experience” on @HubDesigns at http://wp.me/p5Tdn-SP (pls retweet)

While there has been growth in Sofware-as-a-Service (SaaS) and Cloud software offerings for MDM, there hasn’t been as much activity for open source software options (OSS) for enterprise MDM.

For several years, MDM OSS offerings have emerged that, for the most part, provide basic though limited capabilities. However, for MDM-related solutions such as data integration and data quality, stronger OSS options have been increasing over the years. So eventually, OSS may become more viable for fulfilling the requirements of an end-to-end enterprise MDM solution.

Talend, a vendor for OSS data management tools, makes the argument that OSS options become more attractive when a software solution space achieves maturity:

The industry having evolved and matured, there are solid definitions of the requirements placed on an MDM system. A standard definition of requirements marks the beginning of the commoditization of the market and the entry of open source.

The idea of commoditization of a software solution space marking the debut of OSS options works fine, but it doesn’t mean that OSS will be a successful option for all solution domains. MDM for many enterprises requires sophisticated and well-defined software capabilities that support many areas including the data management practices, processes, and roles of people that must be handled well to achieve success with MDM initiatives.

The Right Fit – And The Right Solution

For a while, open source software was touted as a ‘free’ option for software solutions. Time and experience have revealed many of the “hidden” costs for OSS implementations. The community approach to the OSS code base has been successful for many vendors and does indeed offer free access to the code. However, things change when OSS vendors seek to make money from the software. Most vendors have added separate for-fee “enterprise” versions of the software that in some ways provide little difference from typical on-premises licensed or SaaS versions of enterprise software.

It is essential then to understand what it takes to use OSS for data management and how to assess whether OSS is the right fit for a particular organization. Lyndsay Wise tackles this serious task for the Business Intelligence (BI) software domain in her new book Using Open Source Platforms for Business Intelligence – Avoid Pitfalls and Maximize ROI. Lyndsay is an industry analyst and advisor for organizations with regard to the selection of BI software solutions that will meet their needs.

Lyndsay does a thorough job of dissecting the kind of technology resource commitment that it takes to travel the OSS path for Business Intelligence (OSBI) solutions. Lyndsay rigorously considers why an organization might consider OSBI, with thorough substantiation of her thinking. She draws on her many years of advising organizations of all sizes, to provide a fair and honest survey of the challenges, drawbacks and benefits of OSBI (Chapter 7 is particularly strong in this area).

Lyndsay makes clear what the overall costs are for OSBI and that many times, it is not a ‘free’ option for organizations. In fact, OSS may not be a good choice for most small businesses, and frequently may not be suitable for many mid-sized companies – due to the on-going IT resources that implementations and maintenance can consume. Often larger enterprises are better suited to the adoption of OSS, as long as software development is a strength and key strategy for the direction of IT, in support of business objectives. It may well be that OSBI will emerge as preferable to traditional BI solutions for certain organizations. Lyndsay provides the guidance to find out if that is the case.

To help an organization understand if OSS is a good fit, Lyndsay has created very useful tools through items like “readiness checklists” (Chapter 18), as well as frequent assessment lists at the end of many chapters. She continuously asks all the right questions.

An important point is that much of what Lyndsay details in her book for evaluating OSS for BI, including the checklists and assessment lists, can be applied to other software domains and solution requirements.

Most of Lyndsay’s OSBI assessments are focused on technology and the in-house IT team, simply because OSS is technology-intensive. There is less value from the pure business perspective for OSS in and of itself. For the business side of the story, the value comes from solving business problems, ease-of-use, excellent performance, reasonable cost, just as it would for any kind of BI – or other – software solution.

Convergence and Business Value

It’s not a stretch to take the guidance for assessing the viability of OSS for BI and apply it to other data management solutions when evaluating OSS options. For some time, there has been parallel development and convergence of the many solutions in the data management domain anyway. These solutions include MDM, data integration, data quality, BI, and business process management (BPM).

Separately these solutions can bring value and successful outcomes to the enterprise, but frequently at the cost of duplicated effort and resources.

A good starting place is finding the points of commonality across data-related technologies, where lessons learned in one solution area can be applied to other solutions, in support of unified goals. MDM itself is a reflection of how an enterprise uses data for business purposes. A corporate data strategy is critical to facilitate the integration of data infrastructure, technologies and processes to ensure that enterprises make the best use of data and the business and IT teams that make it all happen.

The question of using OSS for a single domain such as BI or MDM takes on larger importance for enterprises that want to better integrate the components of the data infrastructure and reduce duplicate effort and silos. For each organization, the question becomes: does OSS have a fit for the desired symbiosis of data-related technology solutions?

About The Author

Julie Hunt is the editor of Hub Designs Magazine and an independent software industry analyst and consultant for solution and customer strategies. She has worked in the B2B software industry on the vendor side for more than 25 years in roles from the very technical (developer, SE, solutions consultant) to customer-focused work in strategies for products and solutions, future trends, and go-to-market initiatives. Julie also writes extensively for several online publications, covering software industry topics.

One common deliverable for most Data Governance groups is building and delivering internal communications that effectively communicate and define strategy and process.  Zebra’s communication below is an excellent template for clear, concise communications.

Recently, Mike Smiley, Zebra’s CFO, was named one of the Top 25 Information Managers for 2012. 

Assembling the components of an MDM and Data Governance program at Zebra are the Data Governance Office personnel.

The Data Governance Office (DGO) at Zebra Technologies is a small, strategically focused group that monitors the management of critical information, such as customer addresses and e-mails or whether an item is available for order. The office plays a lead role in ensuring that all the information—known as master data—that we use to run our business is of the highest quality. This is critical to our success, as this master data is shared across systems and used by many different functional groups to do their work.  Without the highest quality master data, we cannot delight our customers or maximize operational efficiency.

Data governance is a relatively new and emerging discipline. Many companies are recognizing that master data is a competitive asset, and like any asset, it needs to be managed and protected by establishing and maintaining standards of use for everyone to follow.

The DGO partners with various business units to set data standards and policies. This includes how data is formatted, stored and accessed. It also monitors data usage to ensure compliance with data standards and policies, and drives the continuous improvement of data quality.

Thanks to one of the DGO’s many cross-company partnerships, Zebra ensures the correct use of data fields.  For example, DGO’s partnership with Zebra’s Global Trade Compliance Office ensures, among other things, that the Country of Origin attribute, necessary for compliant and timely Customs clearance—and the Harmonized Tariff Schedule Numbers of NALA, EMEA and APAC attributes required for all import and export clearances of the articles across the globe—are entered, populated and maintained accurately.  When such data is not in parity with the government’s requirements, the implications to Zebra’s ability to do business are significant, ranging from delays with deliveries to substantial monetary penalties.

The Data Governance Office is also working with several groups on product lifecycle status. This data drives many business processes through our ERP system indicating when products can be ordered and shipped to customers and how the supply chain should plan and inventory products. Incorrect information here translates into poor service for our customers and internal confusion and frustration for Zebra employees. It can also result in misguided inventory investments.

While there is a great deal of work to do in launching the Data Governance program, the effort is critical to Zebra’s success. In 2013, the Data Governance Office plans to continue working closely with our business partners to make significant changes in how we manage our data. We will update you in future articles.

Why do we pay so much attention to things rather than the information about them? “Things” are what we can see. “Information” or “Data” about things is what we know about them. One thing may have different definitions, and its Information / Data may vary. These ideas really belong in a philosophy course. The better definition you have, the better you understand the thing itself.

Data or Information about things is as important as the things themselves. If you have things but you don’t have information about them, you may have to consider that you don’t really have these things.

This presentation is for people who think that Data or Information is an issue for them. For those who think they can own and not understand. This is Data Management for Dummies.

With customer MDM, organizations strive to assemble core information about customers, such as names, addresses, phone numbers, preferences, identifiers, and other demographic data. The process of aggregating this indispensable data about customers is crucial (yet challenging), and this endeavor helps in achieving an accurate 360-degree view of the customers.

Over the last few years, there has been significant advancement in the way we look at MDM. Today’s implementations have evolved to a great extent and typically include one or more of the following themes:

• Multidomain MDM
• Integration between Business Process Management (BPM) and MDM
• MDM in the Cloud
• Impact of Big Data on MDM

All these trends focus on going beyond just managing basic customer information. Using these, organizations can add more intelligence and agility around the master data they have, so that they can predict the moves they can make to enhance their profitability.

Social MDM, which is developing into a new trend, may be the most appropriate way to go about adding this additional intelligence.

The idea behind Social MDM is to connect the organization’s internal customer information with external data about these customers from social networks.

With hundreds of millions of people joining sites such as Facebook, Twitter, YouTube, Pinterest, LinkedIn, Google+ and so on, social media platforms have become extremely powerful tools in allowing business owners to interact with customers in ways that simply weren’t possible before.

In the age of social media, everyone has the potential to be heard. For example, let’s think about a “feedback and review” perspective. Many of us today are going online, checking out the profile of organizations selling products and services that we want, and seeing what our friends and colleagues on our social connections have to say about these companies.

What this means is that today’s organizations cannot afford to ignore the importance of social media unless they want to fuel their own demise. So there’s a very obvious and powerful connection between MDM and the customer data available on social platforms.

While a well established MDM system provides organizations with the ability to create exactly what their customer information looks like at any point in time, Social MDM helps in connecting these customer records with their real world activities. This connection in turn allows a closer look into detailed and real time information about customers, such as their likes, dislikes, questions, problems, opinions, where they are, who they are with etc.

By listening to this information, organizations can apply the right strategy in real time to make key decisions to improve their service to customers, and to offer customized, timely products and services.

Social MDM comes with variety of challenges, including:

• Identification of true customers on different social platforms
• Efficiently linking customer’s social profiles with the internal MDM profile
• Extracting real time data from the social sites and turning it into useful information

In order to best serve customers and listen to them, it is essential to identify who are your customers on social media platforms. Although this sounds basic, it’s an essential step and has many challenges associated with it. Listening to what your customers are saying, engaging them and responding to their needs is only possible if you know how to link the true customers of the organizations stored in a master data repository with their profiles on social platforms.

This poses a definite challenge as we have to deal with challenges such as privacy of customers, misleading (fake) profiles, different avatars people use on social networks, numerous platforms where customers are active and inactive etc.

Just to give an example, a recent CNN Tech blog suggests that there are 83 million fake Facebook accounts.

Finding an efficient way to link the social profiles with the MDM system is a very important step and needs to be done in a way that allows easier integration of real time social data with customer data in the MDM system. One of the ways this can be achieved is by creating an extension to the core customer record in MDM where you can store key social information such as customer IDs and access tokens.

This information can then be used to invoke generic APIs provided by the social platforms. For example, Facebook’s Graph API does exactly this. It takes the user profile and access token to retrieve wall posts in real time. There are numerous ways to achieve this integration and the design of the solution depends on how this information is consumed by downstream applications such as a customer service dashboard.

Extracting real time updates from customer profiles on social platform is a critical aspect of social MDM. Today’s customers are highly social with a myriad of tools available that allow them to share their experiences with a particular organization with the entire world.

With the advancement in mobile devices and connectivity to the Web on the go, customers have multiple communication channels and social platforms for expressing their opinions.

What this means is that we are up against a huge amount of data that needs to be extracted, managed and analyzed. Although the big data tools available today can handle the quantity of data, what is more challenging is that this social data also contains “multiple versions of truth” as it’s gathered from different social platforms.

This is a huge challenge in itself and requires an innovative approach to deliver value.

MDM today is better poised than ever not only to provide accurate information about customer data, but to deliver broad, sustainable business impact by leveraging available social information. Although in its current form we have many more questions than answers regarding the Social MDM approach, recent articles and a new LinkedIn group are providing lots of insights and healthy discussions around this idea, which is promising.

There are a few challenges such as data privacy and misleading / fake profiles on social media platforms. As we get better at handling these issues , I strongly believe we will have a major winner here. Organizations that capitalize on Social MDM will reap big rewards as more and more people participate through social platforms.

Bottom line: Businesses that exploit Social Data outperform their competition, and MDM isn’t an exception.

About the Author

Prashanta Chandramohan is a Technical Solution Architect with extensive experience in implementing Master Data Management solutions. He has worked with several large enterprise customers around the globe and has delivered successful MDM-powered solutions. Prashanta’s experience spans across retail, financial, insurance, telecommunication, healthcare and automotive industries. He is the creator of www.mdmgeek.com, where he shares his insights on implementing successful MDM projects. You can follow him on Twitter at @MDMGeek or contact him via email at prashant@mdmgeek.com.

One constant truth in the business of data is change. The most critical factor in master data management is agility, within both process and software design. Agility means responding to and managing never-ending changes in the critical data used to support operational decision-making. I firmly believe the ability to respond to changes in the data is a must in the world of Product Information Management, or PIM, as the specialty is sometimes known.

Master Data Management is a broad classification of processes, governance, and software tools used to manage information such as customer and/or product data. CRM (customer relationship management) applications can support the organization’s interactions with employees, members, clients, customers and supply base for marketing, customer services and technical support. Typical data elements in CRM include names, titles, email addresses, physical addresses, phone and fax numbers, etc.

One crucial note is that there is a different expertise required to manage and structure a data governance policy for customer (CRM) data as compared to product (PIM) related data.

Product Data

The definition of PIM is managing information about products, which may also include services. Product data can include equipment, assemblies, spare part components, and commodity type items such as office supplies or hardware items, i.e. bolts, screws, nuts, etc.

The data management and governance challenges escalate with the vast number of variations used to describe a single product. Adding further complexity to product data management is that of sell-side vs. buy-side. Sell-side data is a controlled data set of product information; as a result, the data and the governance is structured and owned internally by the manufacturer or supplier. If the governance is structured intelligently, the structure enables multiple data uses such as exports to web catalogs, print catalogs, engineering libraries, and more.

Buy Side of Production Information Management

Buy side is more complex, depending on the size of the operation. Buy side is the collection of transaction data (product or service) to support the operation of the plants and facilities. A critical aspect of collection and management of spare part information is to support the planning of physical inventory to enable the uptime of the facility. There is a fine balance of inventory cost versus ensuring the maximum uptime, which can be very challenging. It is not uncommon for a large global manufacturer to exceed 15,000 suppliers / OEMs equating over half a million submitted data records a year. These data records need to be reviewed, verified, classified, structured and referenced to ensure there aren’t any duplicate records created in the ERP system.

There are many variations in the data sources to support the operations of a facility. For instance, spare parts data is submitted from the equipment designers via engineering bills of material, or through individual purchasing requests from maintenance staff. The spare parts data includes the manufacturer name, part number, classification name (noun), unit of measure representing how it is sold, and additional information to describe the physical characteristics. However that same OEM part could be submitted as a supplier part with a different supplier part number, a different classification name and no mention of the OEM part number.

The result is the same part data setup with two different master records, each with different contracts for purchases at different prices and stored in inventory multiple times. This results in either excess cost and inflated inventory or a spare part that is not available resulting in the shutdown of the plant line. This is why the data governance and master data management business processes are so critical to an efficient and streamlined operation.

PIM Data Governance

A product information governance project may appear to be a daunting effort when you’re beginning to structure the data rules.

My best advice is to take the time to develop a data roadmap to provide a clear and precise understanding of the data and its use within the organization. The road map should detail how data is required and submitted for use within the enterprise, account for the multiple uses of the data (purchasing, engineering, marketing, and maintenance), plus the required data elements and structure needed to accommodate each software system.

As an example, let’s explore how the data is provided to the enterprise. There are multiple sources of data, from engineering drawings created internally or provided by suppliers, maintenance requirements, buyer requests, and more. With an understanding of the source data, a clear data requirement enables an improvement in the quality of the data provided to the organization.

Starting at the initial contract to source a new piece of equipment for the plant, you may include with the equipment specification a spare part data requirement plus a template for the supplier to provide the spare part information. The contract deliverable should include the completed spare parts list that was required per data governance. Now your master data management process has been simplified and data quality has improved.

In the roadmap, the required data elements are defined to support the business requirements. For a large global manufacturer the governance may include a structure for equipment numbers, location structure for the equipment and basic data governance elements specific to the master record.

Commonly required elements will include:

• Manufacturer Name,
• Manufacturer’s Part Number,
• Noun classifications,
• Technical descriptive attributes,
• Sequencing of attribute display order,
• Units of measure (typically a purchasing rather than a use UOM, sometimes referred to as a disbursement UOM),
• Price
• Volume purchase prices,
• Purchasing category,
• Lead time,
• Warranty information,
• Language translation requirements,
• Other classifications such as ECCN, UNSPSC
• Any other descriptive elements to ensure smart purchasing decisions and stock strategy for items inventoried.

Benefits as a Result of Data Governance

There are many benefits of implementing an innovative data governance and master data management system. Many of the basic benefits, both in process and cost, are:

• Reducing inventory through identification of duplicate items,
• Facilitation of inventory sharing and internal purchasing programs,
• Reduced employee time spent searching for items,
• Common spare part usage strategies,
• Reduced downtime in manufacturing equipment due to lack of information availability,
• Ability to manage inventory using a just in-time model.

Data Governance supports both indirect and direct cost savings. Businesses can begin to embrace the definition of operational data as an asset of the corporation, ensuring improved data accuracy and confidence of the data users.

About the Author

Jacqueline Roberts is the VP at DATAForge™. She is a data enthusiast and firmly believes a successfully implemented MDM program will improve engineering, maintenance and purchasing data processes, which enables inventory management and cost-saving initiatives. You can follow her on Twitter at http://twitter.com/jackiemroberts or visit www.dataforge.com.

The Hub Designs MDM Think Tank met with Teradata MDM team members Robert Rich, Global Program Director, and Greg Thomas, Solutions Consultant – Center of Excellence, for a thorough and highly interactive session to take a look at the 3.2 release of the Teradata offering with “two” names: Aprimo Master Data Manager and Teradata MDM.

Teradata has distinct visibility as a large enterprise vendor for data management and analytics solutions. Teradata has had a MDM solution for some time that was proprietary to its own solution platform. Teradata entered the MDM market in 2006, with core MDM technology purchased from i2 Technologies.

Augmentation for that initial MDM solution has come from the Aprimo acquisition of January 2011. While Aprimo is best known as a marketing resource management platform, it has also developed master data management capabilities. The 3.2 release of Teradata MDM merges Aprimo MDM capabilities with Teradata’s original MDM technology, all of which draws on the power of the overall Teradata database platform.

There is some difficulty in positioning Teradata Aprimo MDM among the array of MDM vendors. While Teradata itself is a large vendor, its MDM offering has somewhat limited scope when compared to other vendors, primarily because it is meant only for the Teradata technology platform.

Gartner shares its view in the 2012 Magic Quadrant for Master Data Management of Customer Data Solutions, where Teradata MDM is mentioned in the “Other Vendors” note and is not included in the Magic Quadrant itself:

Teradata sees MDM as an integral part of data warehouse solutions, and views data mart consolidation as an ideal opportunity to achieve data synchronization with analytical MDM; however, it also supports workflow-oriented operational MDM use cases with central authoring. Teradata MDM has the most experience in managing product and supplier data, not customer data…

We estimate that Teradata has a total of 23 MDM customers, including five managing customer data. In addition, over half of Teradata’s 1,400 data warehouse customers use Teradata for aspects of customer data management. We estimate its 2011 MDM software revenue at $3 million.

Teradata has gained traction in managing customer data, which has been augmented with the 3.2 release of Aprimo MDM. It will be interesting to see the Gartner analysis of Teradata MDM in the next Magic Quadrants for customer and product data.

The current target market for the MDM offering is Teradata’s installed base. The MDM team is also seeking out ‘net new’ customers who are interested in the overall Teradata infrastructure, as well as MDM capabilities. Existing Aprimo customers who are not currently Teradata customers also present potential opportunities.

The Teradata approach of providing MDM capabilities only for the Teradata data management environment cuts two ways. In one direction, there are vendor lock-in issues – not allowing potential customers to do much mix-and-match for solution architecture – although integrations with other systems and database platforms are possible through various methods.

But cutting the other way, a key competitive aspect of Teradata Aprimo MDM centers on the power and performance enhancements that come from using the Teradata platform. Using the Teradata database instead of in-memory processing for MDM functions usually leads to better performance and decreases in processing time, especially for large datasets and/or complex processing requirements.

Current Focus of Teradata MDM

Teradata is interested in solving problems for large enterprises such as redundant data in multiple systems with no cross-reference:

• Source systems not in sync – some having stale data
• No single version or source of truth
• Cannot do analytics like “Sales by Customer across business lines”
• Cannot optimize business processes across function

Teradata sees these benefits in its approach to MDM:

• Enables ‘Single View’ of Customers, Products, Locations and the relationships between them
• Supports consistent organization and governance of business data
• Coordinates introduction / onboarding of new Products, Suppliers, Customers
• Drives a single accurate view of master / reference data into analytics and business processes

From the Teradata perspective, enterprises should be able to leverage a common data model both for MDM and the enterprise data warehouse (EDW), and for all domains including party, product, location, financial, and reference data.

Current target industries include: manufacturing, media and entertainment, finance, retail, healthcare, telecom, and government sectors. Large organizations that have adopted Teradata MDM include: Daimler, Intel, Hershey’s, Telstra, Vodafone, and Miller-Coors.

Customer use cases include:

• Achieving unified views of customer and sales data
• Organization of business hierarchy data for the sales channel
• Supply chain data management

As is the case for data integration software solutions, the number one ‘competitor’ that Teradata encounters for MDM is custom-coded / in-house solutions. Top vendor competitors include Informatica, IBM, Oracle, and SAP.

MDM in the EDW

The Teradata Aprimo MDM offering is based on the premise that the Teradata platform (and the enterprise data warehouse) is the right place for MDM, with the notion that most of what is considered master data is already in the EDW.

As such, master data is co-located with transactional data in the EDW, with each set defined by separate schemas, and with no separate hub for MDM.

The Teradata platform enables fast interaction between MDM and EDW schemas. Data that starts on the Teradata platform can then be registered into MDM; a reference data view in the EDW can fold into master data constructs. This approach is based on complex architecture but also provides a lot of power, and many tools and capabilities to optimize MDM for Teradata platform users.

An application server architecture underscores MDM functions and processes, since the EDW doesn’t provide all that is needed for MDM and data governance. The applications layer provides: staging to validate data, create error tables; event-driven processes around data validation; hierarchy management; third party data services; and other relevant services. Teradata can push master data back into transactional systems and databases through ETL layers, using any of the usual methods of data integration available to sophisticated data management platforms.

For the most part, the Teradata platform is a high end technology solution — but a lower cost entry point for MDM can come through a Teradata appliance, which may help attract ‘net new’ customers.

MDM Capabilities

The Teradata offering includes many of the expected capabilities for MDM and data governance, particularly for the needs of large enterprises. Teradata claims a flexible data model to encompass any MDM data configuration or use case.

The workflow engine provides configurable processes, with extensive capabilities for leveraging business rules for fine-tuned management. Through the Teradata platform, geospatial capabilities for location intelligence are available to MDM projects.

Data management functions such as data quality are handled in the MDM application layer, and then persisted to the EDW. MDM processes can be exposed as services to provide real time integration with other applications. There are no native data quality capabilities, but Teradata MDM integrates with third party data quality tools. Typical partners include Trillium, DataFlux, and SAP Data Services.

Data profiling is still a work-in-progress for Teradata MDM, and is accomplished primarily through the rules validation framework. More capabilities for data profiling are coming in the next release.

Teradata MDM is agnostic as to third party tools for BI reporting, dashboards and data visualizations – Tableau and BIRT were both mentioned as frequent choices for Teradata users.

The demo ran through a data-cleansing, match-merge process, with iterations through various profiles to cleanse and validate data. For such iterative processes managing multiple profiles, the Teradata platform brings performance enhancements, particularly to reduce processing time.

Teradata MDM provides an Eclipse-based environment for the design and deployment of MDM applications. Another interface is browser-based which Teradata sees as a faster, more flexible choice for ‘power business users’ for handling certain MDM and data governance tasks without intervention from IT or data management technology specialists.

Teradata qualified the ‘power business user’ as more than data domain experts — basic knowledge of statistical analysis and SQL are also required. This actually means a very technology-adept user, not just a typical business user who is a line-of-business data expert.

As with other data management vendors, the reality is that most interfaces are still too technical for ‘regular’ business users. The notion of making certain activities of MDM and data governance available to business users is a good one – but there is still work to be done to achieve greater interface usability, which is true for any vendor at this point.

What’s Next for Teradata Aprimo MDM

The 3.3 release is slated for mid-2013, and continues the Aprimo Master Data Manager name. New capabilities will include:

Out of the Box data quality services and advanced workflow services:

• Integrated Data Profiling for improved Data Cleansing and Data Quality visibility
• Integrated Address Verification as part of Data Cleansing for CDI
• Customizable workflow created and managed directly by data stewards

Customizable Services and Features:

• Integrated Data Profiler for Values, Frequency, Histogram and Descriptive Stats visualization
• Global Address Verification support as extension to CDI Cleansing Rules
• Integration with D&B and Acxiom data enrichment services
• Runtime extensible workflow framework

About the Author

Julie Hunt is the editor of Hub Designs Magazine, and an independent software industry analyst and consultant for solution and customer strategies. 

With more than 500 million users generating 340 million tweets a day, it can be hard to pick out the true industry experts. When it comes to data governance, here are some of the most insightful and informative people active on Twitter. Check out this list of top data governance pros offering commentaries in 140 characters or less.

The folks at Information Management do a great job, and I’ve had the pleasure of working closely with them, both as a writer and a webinar presenter. In particular, I suggest you follow these great folks at IM:

• Jim Ericson, @jimericson
• Julie Langenkamp-Muenkel, @JulieLangenkamp
• Tony Carrini, @TonyCarrini

And continue to check out the up-to-the-minute content at http://information-management.com.

This valuable Web Seminar is now available for replay.You can revisit this informational Web Seminar for a limited time and pass along the content to any of your colleagues.

The presentation covers:

• Foundational aspects of data governance including the “people” and “process” aspects of starting up a new data governance organization
• Technology and information aspects of bringing the discipline of data governance to your organization
• The impact of trends in Mobile Computing, Social Media and Big Data on MDM and data governance

Everyone at Hub Designs, IBM and Information Management hopes you enjoy this educational session.

To access the replay, please visit http://bit.ly/HubDesignsIBMwebinar.

The webinar will be held on Tuesday, December 4th at noon Eastern / 11:00 am Central / 10:00 am Mountain / 9:00 am Pacific.

If you’re implementing (or thinking about implementing) a Master Data Management or Reference Data Management solution, you’re going to need a data governance organization to:

• proactively define strategy, policies and rules;
• provide ongoing protection and services to data stakeholders across the enterprise; and
• resolve issues arising from non-compliance with policies.

But where do you start in creating a data governance organization? How do you navigate the considerable political challenges and handle the organizational changes, staff the group, define the processes, implement complementary technology, and keep the focus on improving information quality?

This session will cover the steps needed to start up and build out a DG organization, and provide real world examples to illustrate best practices. You’ll learn:

• Foundational aspects of data governance including the “people” and “process” aspects of starting up a new data governance organization
• Technology and information aspects of bringing the discipline of data governance to your organization
• The impact of trends in Mobile Computing, Social Media and Big Data on MDM and data governance

Speaker: Dan Power is the founder of Hub Designs, a global consulting firm specializing in developing and delivering high impact master data management (MDM) and data governance strategies.

To register, just click on the following link: http://bit.ly/HubDesignsWebinar.

Please share this on Twitter by cutting and pasting: On @HubDesigns: “#DataGovernance Webinar with Hub Designs and IBM”, by @Dan_Power at http://wp.me/p5Tdn-Rr #MDM

If we think that master data is a problem, then we think about costs, responsibilities, efforts and other issues of our own making, and as a result, master data becomes a problem. As soon as we put out a fire in one place, another one flares up. I call this the Reactive approach to MDM.

If we think that master data is an advantage, then we think about the business benefits of consolidating and centralizing master data: increasing revenue, more confident decision-making, improved efficiency, lower costs by optimizing business processes, etc. And in the end we will achieve all of these benefits because master data will become an advantage. I call this the Proactive approach to MDM.

Below I compare these two approaches. Each individual can decide which approach is better for their organization.

In this table, the Reactive MDM approach is represented as very pessimistic for master data. Unfortunately, we do run into some of these things in real life.  In fact, Reactive MDM is not true MDM — it’s mere ‘master data’ problem solving.

The Proactive MDM approach is the real MDM. It’s what we should seek to accomplish for the enterprise.

About the Author

With over ten years experience with MDM implementations and data governance, Dmitry Kovalchuk has worked in multiple industries and organizations in Russia including Rosatom, United Metallurgical Company, Gazprom, and the Federal Treasury of Russia. Dmitry has worked for several years as an independent consultant, business analyst and architect, and has experience with solutions from SAP, Oracle, IBM and Informatica, as well as in-house systems. Dmitry is currently in the Ukraine as an MDM Team Leader at Citia Business & Technology Consulting, where he is building the first-ever MDM practice for Ukrainian organizations.

Please share this on Twitter by cutting and pasting: On @HubDesigns: “Two Approaches to #MDM”, by Dmitry Kovalchuk at  http://wp.me/p5Tdn-Rj #DataGovernance

As market forces continue to fluctuate, business leaders are searching for the best ways to work with uncertainty and still deliver improved performance. As a result, organizations are transforming into adaptive enterprises.

Performance for the adaptive enterprise means agile, dynamic, and responsive to continuous change. But this kind of performance requires enterprise data that is reliable, usable and timely, so that analytics and decision-making processes are quickly on target with relevant recommendations.

Information management has an important new challenge for provisioning right-time data to emerging adaptive enterprises. MDM is a reflection of how an enterprise uses data and information for business purposes. The creation and management of master data touches more than the information itself. By creating data repositories and processes that reflect business functions, enterprises should now have access to the information that is so vital to effectively and agilely adapting to constant change.

Agile Processes, Agile Data

In 2011, Gartner surveyed 300 user organizations: 49 percent said process improvements are the key business priority driving interest in MDM. Gartner’s Andrew White commented:

“MDM treats information as assets that indirectly contribute to enterprise performance by improving process execution via data consistency,” White wrote in his presentation slides. “BPM is a management discipline that treats processes as assets that directly contribute to enterprise performance.”

MDM has a clear role in improving business processes; both MDM and optimized processes contribute to better performance management. The increasing focus on master data in relation to business processes extends usage across the enterprise to more business roles and functions. For many enterprises, data governance policies are the means for the orchestration of master data management, practices, processes, and people, so that compliance with data governance policies assures that master data is available as a trusted and vital asset for the enterprise. A new direction and challenge for data governance will be creating policies that reflect the continuously changing needs of the adaptive enterprise.

Dynamic Business Processes: Adaptive Case Management

Adaptive case management is an interesting example of adaptive and responsive processes that depend on reliable data. Forrester Research defines adaptive case management (ACM) this way (Forrester calls it dynamic case management):

A highly structured but also collaborative, dynamic and information-intensive process that is driven by outside events and requires incremental and progressive responses from the business domain handling the case. Examples of a case folder include a patient record, a lawsuit, an insurance claim, or a contract, and a case folder would include all of the documents, data, collaboration artifacts, policies, rules analytics, and other information needed to process and manage the case.

Immediate access to the right information is essential to ACM due to the unpredictable nature of case management knowledge work. ‘Unpredictable’ is also a driving force of the adaptive enterprise, where there is increasing need for organizations to adapt to the volatile nature of business as it happens. ACM may evolve beyond its ‘roots’ in business process management to become a much-needed internal platform of process orchestration, practices and technology to more relevantly and quickly surface the most-needed information, content, and data, wherever they exist in the enterprise — all in the context of situational business processes related to a particular employee’s responsibilities.

Frequent mandates for ACM include managing risks and compliance, containing costs, applying a systems approach to non-conforming processes, and the ability to respond well to the unexpected. MDM parallels these mandates with a number of its value areas for enterprises:

• Compliance and risk management
• Revenue growth
• Customer interactions
• Cost and performance efficiency
• Business agility

Both MDM and ACM can contribute positively to customer-focused activities, along the lines of real-time analytics, decision-making, dynamic process, and timely responsiveness.

Performance, Outcomes, Metrics

Obviously, ineffective business processes and data bottlenecks are obstacles to the adaptive enterprise that needs to nimbly change in response to events and new insight, and to rapidly make decisions to make the most of new opportunities and challenges. Achieving performance improvements starts with identifying how data-fueled processes can be optimized, and how MDM and BPM can work together to do so. Well-functioning business processes, whether explicitly defined or adaptive (as in case management), are an essential contributor to agile business performance.

A classic example of setting an improvement objective aligned with MDM is the reconciliation of customer identity across systems of record and systems of engagement, since both domains must be integrated to support and capture customer interactions. Many business functions – and strategies – greatly benefit from a unified view of the customer. The organization can quickly and accurately interact with customers. Now metrics can be applied to measure outcomes and performance relative to customers, based on the organization’s KPI preferences, and tied to corporate and line-of-business objectives.

Processes and performance improvement initiatives can be decomposed into master data elements that can be sorted and aggregated properly for metrics to quantify value. Focus is put on the data assets that are the most essential for overall business improvement. MDM makes such a focus repeatable across the enterprise to integrate business processes and metrics around the same master data elements.

The Successful Adaptive Enterprise

Success factors for enterprises almost always include effective leaders, the right strategies, and high quality products and services that meet customer needs. But more and more demands are pushing enterprises to constantly monitor and respond to changes in markets, customers and competitors. These changes will then alter other success factors: strategies now have a shorter shelf life. Enterprises are challenged to:

• Transform to an organization comfortable with continuous adaptive behavior while relying on business processes and data systems to increase flexibility at tactical and strategic levels
• Make better use of data, processes, analytics to improve decisions, to predict future trends and markets, and to proactively manage change
• Develop responsive risk management and compliance processes that are ready for the uncertainties of business, both good and bad

Data analytics are seeing wider adoption as important tools throughout the enterprise, reflecting the convergence of BI, BPM and MDM. Analytics are also being used to evaluate enterprise data to improve both information management and business processes, more frequently in real time. Analytics-fueled data management capabilities bring improvements and evolution to data integration and data quality approaches. More enterprises are looking for analytics embedded in a wide variety of systems, including operational systems that now make use of real-time analytics and decision-making technology.

MDM initiatives have the right capabilities and strategic purpose to underpin business agility and the adaptive enterprise. Master data can be extended to provide improved use of information systems and repositories throughout the enterprise. MDM can stretch its data muscles to take on new business requirements and objectives where data and process are key components, and now venture into new categories that need MDM to better serve the enterprise. To achieve agile business actions takes highly disciplined approaches to MDM and data governance; and it takes flexibility bred into information management DNA to be able to quickly but reliably handle change.

About the author: Julie Hunt is the Editor of Hub Designs Magazine. As a software solution strategist, she understands the overlap and convergence of many business processes and software solutions that once were thought of as “separate” – and how this impacts both software vendors and buyers, as well as the strategies that enterprises implement for how technology supports the business and its customers. Julie shares her take on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt. For more information, please visit Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective.

Please share this on Twitter by cutting and pasting: New on @HubDesigns Magazine: “The Adaptive Enterprise: New Directions for MDM?” by Julie Hunt at http://wp.me/p5Tdn-R2

George started off with a good introduction to what governance organizations require in order to be successful:

• understanding the big picture and how the pieces fit together,
• designing the right processes with auditing, rules and controls,
• creating measurements and proof points,
• implementing the right tools, and
• having the right people in place and the right organizational support.

Whether people are starting from a blank sheet of paper, or are trying to build on something they’ve already got, they need to get to a point where they’ve got something people can use, with the right systems and tools for the enterprise.

At Procter & Gamble, after being on SAP for 25 years, they realized they were maintaining integrated master data across their landscape and any field that was “off” was going to break something downstream.

Enterprise architecture supported data governance through:

• Impact analysis – when a new business requirement comes up, the impact needs to be investigated with concrete metrics to understand what applications and business processes need to be changed.
• Documentation that data is being used according to standards, and recording where exceptions and violations are taking place
• Metrics to the level of governance in place and a roadmap for change

It’s very common, for example, to have two different groups using the same field in different ways. So a “customer channel” field might start out being used for financial analysis in North America, but it can change over time to support pricing in Europe. So it’s important to understand those dependencies when you go to make changes.

It’s important to put frameworks in place as part of the roadmap process for the next few years, and to know from where you’re starting.

George’s definition of Enterprise Architecture was great: “City Planning for IT”. Where does the business want to go? What capabilities are they going to need in terms of:

• Business processes are the processes the business uses to meet its goals and to transform the business.
• Applications are what users actually “touch” to perform the business processes.
• Information is how the enterprise’s data is organized, processed and used in applications.
• Technology is the underlying building blocks for all of the above.

Architectural frameworks help by providing a “big picture”, as well as standards for organizing and presenting business and technical architecture components.

Architecture governance is the collected principles, decision rights, rules and methods to drive architecture development and alignment in the organization.

Architecture value is defining, measuring and communicating the value and impact of architecture to the business.

Architecture planning defines the vision and roadmap for IT domains by anticipating business needs and trends, and developing the necessary architecture components.

Strategic planning uses architecture principles to align business needs with IT capabilities, define portfolio strategy and allocate resources.

Communication and stakeholder management is critical as well.

A framework means you don’t have to start from scratch. SCOR (Supply Chain Operations Reference) goes down to Level 3. The American Productivity and Quality Center (APQC) takes it down one level further. And a newer group (Value Chain Group, VCG) is even more detailed in terms of low-level process design.

This gives you a communication structure internally between organizations and externally to validate best-in-class processes.

An enterprise architect can help by providing an Enterprise Conceptual Data Model, which enables an accountability structure. For example, raw material, product, order – all have high-level owners, and may also have sub-owners – for example, basic data, sales data, plant data, etc.

If you don’t document – at least at a medium level – seeing the relationships between the data gets impossible. If you’re just managing a portion of a domain, that’s just where you’re starting. You know that you’ll have supply chain, HR, Finance, etc. dropped on you at some point.

Figure out what are those critical business fields, and put together the documentation in the form of a conceptual data model and metadata. Even in a system such as SAP that might have hundreds or thousands of tables, there’s usually less than a dozen tables that are really critical.

The data model also helps you see the big picture. You can grow your conceptual data models into a metadata repository, perhaps by working at the “data group” level. The high level domain (or subject area) can be broken down into 7-10 data groups or “data chunks”, which can be broken down into the critical fields in each data group.

Knowing what the 25-30 fields are that are really critical to your business will pay many different types of benefits. So data modeling (not necessarily at the physical level) will be very helpful. And managing it in Excel just isn’t going to work.

At P&G, the Excel model was 26,000 lines long, just for the Material domain (and they have 38 different domains). But putting that into a conceptual data model makes it so much easier for executives and business partners to understand. And this drives more accountability, because you can more easily define the scope of what people own.

And the world of big data is here. It won’t be long before people are trying to use big data to make decisions, and maybe make them incorrectly because they don’t understand the underlying models.

It’s important to establish a culture and process where projects have architecture and governance reviews at each stage gate, to:

• Identify what data is needed
• What business processes will produce the data
• What business processes will consume the data
• Is data being used according to standards – if not, change the project’s use of the data to conform, get an exception, modify the standard, or stop the project temporarily

This will provide a jumpstart for governance projects to understand how data is being used according to standards.

Some of the tools that can be used to manage the models and documentation include:

• PowerDesigner from SAP (formerly Sybase)
• ARIS
• MEGA
• Erwin
• etc.

Tools not sufficient for enterprise architecture documentation:

• Excel
• PowerPoint

This allows you to drill down, much like with Google Earth, to see the world of data in ways you never thought you could do before.

Some key learnings from Procter & Gamble:

• Your enterprise architects can play a big part in lending their brain power to your program and ensuring the success of your data governance projects
• If you don’t know who they are, find them and see how you can collaborate with them
• Enterprise architects can help you convert your ideas into something people can use, with the right systems and tools for use in your enterprise.

A special note: George Bryce contributed to the book “Enterprise Information Management with SAP” (available from SAP Press), which helps people understand products like SAP Information Steward, SAP NetWeaver Information Lifecycle Management, SAP Master Data Governance, etc. All royalties are donated to Doctors without Borders. I’m planning to write a review of this book as soon as time permits.

What are these fatal errors?

1. Calling it MDM!
2. Looking in existing data for Master Entities.
3. Using misnomers like Customer, Supplier, etc. for the Master Entity of “Party”.
4. The Curse of the Systems SILO.
5. Mistaking Unique Codes for Unique Identifiers.
6. Mistaking Address for an attribute of Party.
7. Not using the Logical Data Model.

In this article, I’ll address each of these fatal errors in turn and, more importantly, describe how they can be easily avoided.

Error 1: Calling it MDM

The first fatal error is calling it ‘Master Data Management’ at all.  Calling this core set of activities by the wrong name starts it off in the wrong place and heads it in the wrong direction. The fact is that there is no such thing as ‘master data’!

What is actually being managed are what I call the Master Entities of the enterprise and, for that reason, this set of activities should be termed Master Entity Management (MEM).  True, we do hold data about these, but it is the management of the entities, not of the data, that is key to success.  The quality of the data of the entities will need to be managed, but that is a subsidiary part of managing the Master Entities themselves.

This misnaming is historical and came about because this whole area has been mismanaged for some time now and is probably worse off that it was 20 years ago.

Many of the core skills and techniques that are essential for Master Entity Management have been forgotten. Indeed, many practitioners have never even learned them.  For that reason, most enterprises start in the wrong place by trying to sort out databases full of error-laden data relating to Master Entities and believe, erroneously, that this will solve the problem.

Solution?

The solution starts by calling this core set of activities by the correct term, Master Entity Management.  Will this solve all of the problems? No.  But it is the foundation for a solution.

Using the wrong name makes a solution impossible to achieve as all efforts are pointed in the wrong direction.  By using the correct name, it becomes possible to identify the true faults and, more importantly, ways of rectifying them.

Error 2: Looking in the Wrong Place

Having started off by calling Master Entity Management by entirely the wrong name, practitioners further compound their error by looking in entirely the wrong place for the Master Entities of the enterprise.

And where is this? In the existing data!  Why should they not look there?

Unless Master Entity Management is already well established in your enterprise, then your existing data on your Master Entities is probably as far from what it ought to be as it is possible to get. So, looking there for your Master Entities makes about as much sense as looking in a scrap yard for the components for a Formula 1 racing car.

Solution?

The only way to find out what the Master Entities of the enterprise are is to ask the Senior Executive team the six “Multi Dimensional MEM” questions.

These six questions, which will lead you to the Master Entities for any enterprise, are:

1. What types of things do we make, buy, sell, improve or trade?
2. Who do we buy from and sell to?
3. Who benefits from our products and services?
4. Where are our customers, suppliers, beneficiaries, etc. located?
5. What resources do we use to make, buy, sell, improve or trade our products and services?

Error 3: Muddled Misnomers

Using misnomers like Customer, Supplier, etc. for the core Master Entity of the Enterprise is our next MEM fatal error.

Customer is NOT a Master Entity! This will come as a shock to all of those enterprises that see Customer as their most important Master Entity and spend so much time, energy and money trying to achieve things like a ‘Single Customer View‘.

The truth is that they’ve gotten it wrong, and their approach is a major cause of fragmentation of the Master Entities in enterprises around the world.

If Customer is not a Master Entity, with whom does the enterprise do business?

They do business with legal entities or parties other than the enterprise itself.  For this reason, a more appropriate name for this crucial Master Entity is ‘Party’.

If Party is the correct name for the Master Entity, what is ‘Customer’?  It is a Role played by a Party in a commercial transaction with the enterprise.

Solution?

Be aware that one Party can play many roles, such as Customer, Supplier, Guarantor, Beneficiary, even Employee!  Although all of these roles are crucial to the survival of the enterprise, they are NOT Master Entities and mistaking them to be such, will cost the enterprise dearly.

Error 4: The Curse of the Systems Silos

One of the major causes of fragmentation in MEM is the ‘Silo Mentality’ that pervades nearly every enterprise, due to the widespread use of third party applications or packages – also called Commercial Off-the-Shelf (COTS) software.

A typical package of this type would be a Customer Relationship Management (CRM) application.

In the silo world of the CRM application there is only one Role for Party and that is Customer, so it is called ‘Customer’.

Everybody who works with these packages gets sucked into the Silo and they too start to believe that Party is really Customer.

It’s similar in the silo world of Supplier Management, where Party is mistakenly called Supplier and where all concerned believe it to be so.

The same error is repeated around the enterprise in the worlds of HR, Marketing, etc.

Everywhere that you have a “silo application”, you will have a “silo mentality”, bringing fragmentation and duplication.

Solution?

What is required is central management of the Master Entity of Party.

This requires unified and integrated thinking and a centralised system that holds a single view of Party that can be used by all other satellite systems.

The starting point for such integrated thinking is a Logical Data Model that shows the Master Entity of Party in its true light and Customer, Supplier, Employee, etc. for what they really are – Roles.  Important Roles. Vital Roles. Yet just Roles – all played by the core Master Entity of Party.

MEM Error 5: Mistaking Unique codes for unique identifiers

This error is perhaps the single greatest cause of duplication of Master Entities in systems around the globe. The most common example of this is the belief that Customer Number is the unique identifier of a Customer.

What is wrong with doing that? Several things.

As we saw in MEM fatal error 4, there is no such Master Entity as Customer.  The actual entity is Party.

Having this ‘unique code’ mentality results in one part of the enterprise giving Party the unique identifier of Customer Number, another part giving it Supplier Number, and in yet another part giving it Employee Number and so on.

So here we have (at least) three different unique codes posing as the unique identifier of the same entity in the same enterprise.

So, what is the solution? A Party Number?  Well, Party Number would definitely be a useful mechanism for removing the proliferation of other numbers around the enterprise, however, it would not be the unique identifier of Party.

The fact is that unique codes are never unique identifiers.

Imagine we had three Parties with unique Party Numbers as follows:

P00123 John Owens

P00124 John Owens

P00125 John Owens

Are these three different Parties? Well, according to the Party Codes they are.  However, the Party Name suggests quite the opposite.  It does not prove it, but is strongly suggests it.

Solution?

How would we find out?  We would actually look for more information on Party, such as age, gender, address, etc.

From this we can see that the information that we need to uniquely identify a Party has nothing to do with the Party Number, it has to do with other key data about Party.

The question we must ask to guide us to the defining the unique identifier of Party is, “With regard to this enterprise, what is it that makes one occurrence of Party uniquely different from every other occurrence of Party?”

This will never be a code! The fact is that codes do not identify entities, they are merely a mechanism for referring to them. Because they do not identify them, they can never uniquely identify anything.

Unique identifiers for Party are a challenging subject and are covered in much greater detail in Module 2 of the Multi-Dimensional MEM Online Training Course.

MEM Error 6: Modelling Address as an Attribute of Party

One of the major misunderstandings that pervade Master Entity Management is that of modelling address as an attribute of party.

How many times have you seen a data entity or database table with the structure shown on the right?

But why is this structure wrong? Ask yourself, ‘What happens if the Party changes address?’ Do the values for house number, etc. all get overwritten?

If so, how do you know where the party lived before the address currently displayed?  How long did they live there? How often did they move?

Also ask, ‘Who else lives at this address?’

This structure will not allow you to answer any of these questions.

It is only the attributes above the line that actually belong to Party.  Those below the line belong to another entity entirely.  The proper name for this entity is Location.

Solution?

Move all address details out of Party into an entity called Location.  Because a Party can be legitimately associated with many Locations over time, the relationship between Party and Locations is a many-to-many.  This means that it needs to be resolved by an intersection entity called Party Location Usage (or Site, as some MDM vendors refer to it).  This is a very powerful entity that shows what use a Party makes of Locations, for how long and what other Parties also make use of these Locations.

MEM Error 7: Not using the Logical Data Model

This is a case of the last being first and the first being last.

The fact is that if enterprises were to model the structures of their Master Entities in the form of Logical Data Models (LDMs), then all of the preceding six errors could be avoided!

Amazing as it may seem, there are comparatively few MEM practitioners, who use, or can use, Logical Data Models in the course of their work.  This fundamental skill has been lost over the last few years and has given the world very many, far too many, MEM practitioners without the necessary skills to successfully implement Master Entity Management in any enterprise.

Because they have lost the core skill they try to push forward a whole raft of technology based solutions, such as Big Data, Real World Alignment, MEM in the Cloud, etc.

All of these technologies have a lot to bring to MEM, however, unless the fundamental structures of the Master Entities of the enterprise have been robustly modelled at the logical level, they will bring absolutely no benefit – quite the reverse.

The use of these technologies actually presents a significant risk to any enterprise that has not carried out proper logical modeling.

Solution?

There are five essential actions to take in order to assure the quality of, not just your Master Entities, but of all of your data. These are:

1. Bring Logical Data Modelling to the heart of all Master Entity Management and Data Quality and activities.
2. Train a core group of people (this would include analysts and business managers) in the fundamental skills of Logical Data Modelling and locate this team in the business, NOT in IT.
3. Build a Corporate Business Functional Model for the enterprise, as this defines all of the data that is required by the enterprise.
4. Build a Corporate Logical Data Model showing the elements and structures of the data required by the Business Functions.
5. Use these two models as the foundation on which you make all of your decisions on systems development and procurement for all parts of the enterprise.

About the Author

John is a Thought Leader, Consultant, Mentor, Practitioner and Writer in the worlds of Strategic Requirements, Business Process and Data Modelling, Data Quality and Master Entity Management.

He has built an international reputation as a highly innovative specialist in these areas and has worked in and led multi-million dollar projects in a wide range of industries across the UK, Ireland, Europe and New Zealand.

Resources

• View a free Webinar of Multi-Dimensional MEM.
• Preview the modules from an online course on Multi-Dimensional MEM.
• Subscribe to John’s MEM newsletter and get all of the latest latest Hints and Tips.

Hub Designs Magazine last covered Informatica’s MDM offering shortly after Informatica acquired Siperian (April 2010 and December 2010). To update the Hub Designs MDM Think Tank on the evolution of the Information MDM solution since the acquisition, Ravi Shankar, VP of Product Marketing, recently presented a high level overview of the new capabilities that are part of the new 9.5 release of Informatica MDM.

Informatica MDM draws on the extensive functionality available in the overall Informatica Platform for data quality, data integration, connectivity, B2B data exchanges (financial services data, third party data feeds), and data governance. Informatica also offers support for multidomain MDM and identity resolution.

Ravi interspersed presentation slides with live looks at the software – a very effective way to illustrate how different MDM capabilities are actually handled. This approach kept the briefing fresh and fast-paced.

Business Applications for MDM

Rapid customer deployments and the proliferation of over 75 business solutions and partner-built solutions are also aspects of the Informatica MDM go-to-market strategy. MDM applications include: reference data management, counter-party risk management, physician spend management, healthcare analytics, and pharma R&D. Informatica MDM currently has hundreds of customers across 30 industries.

Informatica and its partners have been providing business applications built on MDM capabilities as a way to connect with business users in the enterprise to show the value of MDM and data governance.

Partner solutions based on MDM have addressed: ICD-10 (Internal Classification of Diseases), Legal Entity Identifier, Customer 3-D, Consumer Packaged Goods (CPG), and Securities Master.

Four Major Focal Points

More than buzzwords or hype, the focal points of the 9.5 release are significant business technologies:

Cloud – Social – Mobile – Big Data

In this briefing, Ravi detailed the MDM capabilities that map to these focal points.

Cloud MDM

Informatica continues to see an increase in inquiries for overall data management capabilities for data in the cloud, as well as for MDM functionality as cloud services.

Informatica connectivity and management for data in the cloud has had an ongoing evolution:

• Cloud-based sources and targets such as Salesforce.com and NetSuite
• Cloud integration to-and-from on-premises systems
• Public, private cloud
• Cloud consumption – marketplace apps built on Informatica Platform
• MDM application: Cloud MDM for Salesforce.com
• Soon: MDM-as-a-Service (utilizing purpose-built apps, partner ecosystem)

Source: Informatica

As an example of support for SaaS partnerships, Informatica has developed specific functionality for Salesforce.com, such as a widget that pulls in data from the MDM instance to populate fields in Salesforce records, reducing errors and speeding up data entry. Updates between Salesforce and MDM are bi-directional, and data quality capabilities improve the usability of the data. Hierarchy data from the MDM instance can be used to reconcile entries in Salesforce.com org charts and contact relationship charts.

Informatica recently announced Informatica Cloud MDM that is based on the Data Scout acquisition in September 2012 and encompasses several aspects of using MDM to manage data in Salesforce.com. The solution is a native Force.com solution fully integrated with Salesforce.com, is deployed in the Cloud, and doesn’t require additional hardware or software. MDM capabilities help cleanse and enrich customer data, de-duplicate and reconcile data, and integrate Salesforce.com data with data from enterprise systems.

Social Media MDM

Particularly valuable to B2C organizations, customer activity on social media sites is generating large volumes of data. Enterprises want to capture, reconcile and use this data for improved customer interactions and marketing initiatives. Most individuals have a proliferating presence in social sites – MDM plays an important part in reconciling social data with corporate data (enterprise systems), data from cloud applications, and third party data services (Reuters, D&B). Informatica’s Identity Resolution capability has an important function here as well.

Ravi ran through an example of how a retailer might utilize Informatica MDM in a Social eCommerce app where the retailer is selling products on Facebook. The retailer promotes a “VIP Club” that provides additional perks to members (specials, personal shopper, recommendations from friends). Customers on Facebook can opt in and agree to allow the retailer to use their Facebook information solely for the purpose of VIP Club services. The corporate profile is then enriched with social data.

The retailer can also walk the customer’s Facebook social graph to identify other customers in the network with an eye to influence and decision-making.

Mobile MDM

Informatica has extended the MDM platform to iPads and iPhones. Capabilities include proximity and simple searches, integrated cleansing, and hierarchy views.

Ravi brought his iPad into the demo and presented the example of an app that could be built on Informatica MDM for mobile to support the sales team in the field. From a mobile device, sales reps can locate customers that are in the rep’s current geographic area. The sales rep can display all transactions, CRM data and social content to build out current knowledge of the customer before a meeting or call. The sales rep is able to create and update MDM data. Once the app has been created it can be made available in the Apple Store.

MDM for Big Data

The Informatica Platform now has Hadoop integration that provides users with a single platform for leveraging Hadoop to access, integrate and manage large volume datasets, as well as multi-structured data from social media sites. The Hadoop framework processes large relational and/or non-relational datasets using cheap commodity hardware, greatly reducing the costs of big data mining and analytics.

Informatica PowerExchange for Hadoop provides native connectivity to the Hadoop Distributed File System (HDFS), and enhances performance when connecting to data being processed in the Hadoop framework. Informatica also supports connectivity to multiple Hadoop distributions including Apache, MapR and EMC Greenplum.

Informatica MDM can now be used in conjunction with Hadoop processing environments to provide master data entity resolution and de-duplication of today’s super-sized datasets:

• Resolve entities such as customer identities in big data, more quickly and at reduced cost
• Enable large batch data de-duplication when dataset partitioning is not possible
• Handle social media analysis at lower cost to correlate fragments of entities into complete entities to render data usable for analysis

Effective Date Versioning Capability

Ravi demonstrated a new core capability for Effective Date Versioning as part of the Informatica MDM offering. The concept is: when and what did we know about a certain event before it occurred – clearly useful for risk management, auditing and compliance needs. Users can view and manage versions at selected points in time, and can view changes made over time. The capability provides flexibility for dates: create future dates before the dates physically occur, as well as past and present.

Informatica MDM Today

From the briefing that we viewed, it looks like Informatica has robustly integrated the Siperian acquisition into the Informatica Platform.

Informatica’s interest in developing MDM business applications, especially through partners, seems to be an on-target approach to bringing MDM into line-of-business real-world needs and shows the understanding that business users are important partners for the successful use of MDM in the enterprise.

About the Author

Julie Hunt is the Editor of Hub Designs Magazine. She is an accomplished software industry analyst, providing strategic market and competitive insights. Her 25+ years as a software professional range from the very technical side to customer-centric work in solutions consulting, sales and marketing. Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt. For more information, please visit Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective.

About Baker Hughes

The company is a leading supplier of oilfield services, products, technology and systems to the worldwide oil and natural gas industry. Its 2011 revenue was $19.83 billion, with roughly 58,000 employees and operations in 80+ countries.

Their Enterprise Finance Organization reports into Finance and includes master data management, compliance and controls, automated expense reporting, statutory reporting oversight and training.

Where the Data Governance Journey Began

The company had seven divisions, with a decentralized business model, and multiple versions of data standards. This federated model worked well for a long time, but at some point the company wanted more centralization and integration, in part to reduce costs.

The company approached the project with a top-down approach, and all the right best practices. They had an ERP system being developed with a “divisional flair”. In 2007, there was an “association” with the Department of Justice, which reconfirmed the commitment to SAP.

In 2009, approximately 90% of the company was on SAP, but they hadn’t set up a data governance function.

The Reorganization that Changed Everything

In March 2009, there was a big reorganization. The operating structure went from divisional to geographical, with groups like Finance set up as a matrix model.

This change provided more visibility to inconsistencies in the master data. Finance, IT, HR and other support functions needed centralization.

And all of the new operating model executives needed to see their data in a different way.

The Data Governance group was established in the Finance organization, to help improve metrics such as month end close time, Days Sales Outstanding and Days Payables Outstanding.

They stood up the team in May 2010, and began by removing obsolete terminology and formalizing processes and data standards. Improvements included standardized processes to create master data.

The group started out as the “master data police” and it didn’t make people’s Christmas card list that year.

The organization includes a mix of the Finance business people and IT people on the Data Governance Council, which meets monthly to oversee changes proposed by the Data Governance team.

There’s also a separate working group, with the business owners and the requesters of new master data.

Why was this Master Data group put in Finance rather than IT? The Finance organization was the only one ready to take on this massive challenge. But they quickly learned that they needed a healthy working relationship with IT.

The group was able to streamline processes and improve internal customer satisfaction, as well as removing redundant data, which improved reporting in the business.

Two Years of Data Governance – Achievements

They reviewed and renamed cost centers, identifying 23% which could be inactivated as they were no longer needed. General Ledger accounts were also streamlined, which helped with reconciliation and month end closing.

The customer and vendor master data areas are now disciplined, including getting rid of the “Fix It Later” mentality. This was impacting vendor spend analysis and 1099 processing. The business is really seeing the benefits.

To integrate a newly acquired company, the group participated in a 12 month project to migrate them to SAP. They had 8 different systems at the time of acquisition, with lots of duplicate data.

Looking to the Future

Baker Hughes is currently building a 3 year roadmap, with a new Enterprise Data Governance Executive (EDGE) Council. They’ll be extending their domains to include HR and Supply Chain, and eventually Sales & Marketing, Engineering and Logistics.

The group plans to implement improved data governance tools such as data monitoring, data input controls, and believes that without implementing these automated tools, it won’t be able to achieve its ambitious goals.

If there’s a problem with one of the crucial fields that drive a business process, it has very wide impact. The group has great executive sponsors, including the Global Products & Services President. He’s very keen on getting the material master standards up to date with the rest of the data governance group.

The group is rethinking its structure to include more of a “follow the sun” approach, understanding that it needs people from the international side of the business to pick up the country-specific requirements.

Some of the business groups are just starting with their SAP implementations, and they are thirsty for knowledge. The Master Data Council is going to a higher level, to be more enterprise-wide. It took a long time to get to that point, however.

One audience member struggled with the amount of matrix reporting at her company, and asked how to find specific executives and data stewards across distinct products lines. Baker Hughes was lucky enough to find someone at the top who realized that data governance and data integrity are a competitive advantage – someone who drove the change, made the time, and made the people available. Not everyone is going to “win” – in other words, things are going to change for some people. The senior people drove the message that “this is important, this is something we need to do”, and the group needs to keep pushing and continue selling.

Sally Cheadle suggested looking at the influences between the different executives at the top . And to make sure the group is not trying to do everything at once. This approach also helps people to learn more about other parts of the company and other product lines.

The Finance master data group is now expanding to include other areas, and has a mix of part time and full time roles. People will still report to the same executives, even as they become part of the master data program (on a matrix basis). There was some back and forth between IT and Finance, because some of the functions had been performed by IT in the past. There was a key meeting that cleared the air and convinced everyone they were all on the same team. You need the change management and the buy-in in order to make these changes.

The group does regularly provide some metrics, starting each meeting with metrics like how many cost centers, profit centers and vendors have been set up, and how many master data items get rejected on the first review. The group has developed some training to reduce the number of rejects. The group focuses on trends, trying to figure out where things are stuck. The EDGE Council will have its own metrics at some point.

by Data Nerd, aka Carla Gentry

IBM defines the data scientist as:

A data scientist represents an evolution from the business or data analyst role. The formal training is similar, with a solid foundation typically in computer science and applications, modeling, statistics, analytics and math. What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization.

The data scientist role has been described as “part analyst, part artist.” Anjul Bhambhri, vice president of big data products at IBM, says, “A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.”

… A data scientist does not simply collect and report on data, but also looks at it from many angles, determines what it means, then recommends ways to apply the data.

Data scientists are inquisitive: exploring, asking questions, doing “what if” analysis, questioning existing assumptions and processes. Armed with data and analytical results, a top-tier data scientist will then communicate informed conclusions and recommendations across an organization’s leadership structure.

IBM hits the nail on the head with the above definition. Having worked with traditional data analysts as well as programmers, developers, architects, scrum masters, and data scientists — I can tell you they don’t all think alike. A data scientist could be a statistician but a statistician may not be completely ready to take on the role of data scientist, and the same goes for all the above titles as well.

Beth Schultz from All Analytics mentioned that we are like jacks of all trades but masters of none; I don’t completely agree with this comment, but do agree that my ETL skills are not as honed as my analysis skills, for example. My definition of the data scientist includes: knowledge of large databases and clones, slave, master, nodes, schemas, agile, scrum, data cleansing, ETL, SQL and other programming languages, presentation skills, Business Intelligence and Business Optimization — plus the ability to glean actionable insight from data.

I could go on and on about what the data scientists needs to be familiar with, but the analysis part has to be mastered knowledge and not just general knowledge.

When I start working with a new data set (it doesn’t matter whose or what kind), the first question I usually ask is, what kind of servers do you own?

Why would you need to know about the servers to work with data? I ask this question so I will know what kind of load it can handle – is it going to take me 9 hours to process or 15 minutes? How many servers do you have? I ask this because if I have 4 or 5 servers, I can toggle or load balance versus having only 1 that I have to babysit.

What kind of environment will I be working in? I ask this because I need to know if they have a test environment versus a live environment, so I can play without crashing every server in the house and ticking a lot of people off. If you are working with lots of data, lower peak times or low load times are better for live, as compared to test or staging environments where you can “play” without fear. This way, you won’t “bring down the house”.

It’s a good idea for you Chief Marketing Officers (CMOs) to let your Data Scientist work in the evening hours and/or on weekends, at their homes if applicable. This, of course, requires setting up a VPN connection and it also depends on how secure the data connections are, as well as how much processing I can do before I crash them, – um, I mean, what is the speed and capacity to process? If a dial-up connection is all that’s available, forget it.

As a side note, I’ve crashed many a server in my day – how do you think I learned all this stuff? Back in the Nineties, someone would crash the mainframe and we would all head to Einstein’s Deli in Oak Park, IL but today, this might be frowned upon. But I digress, back to more IT related things.

Another handy thing to find out is how the databases are joined. By that I mean, what variables do they have in common (i.e., “primary keys”)? Are the relationships one-to-one, one-to-many, or many-to-many? Why would you ask this? Some programmers (I don’t mean this in general) don’t completely understand relational databases, especially when it comes to transactional data and data that needs to be refreshed often. You have to set up a database like you would play chess: think at least three moves ahead.

Additionally, some programmers/developers use too many JOIN statements in their scripts, which cause large amounts of iterations. Since these tend to increase run time and are not very efficient, you don’t want to be linking too many of these babies together and then running complex algorithms or scripts.

Sometimes, it’s better to start from scratch and build your own data source. When writing scripts to extract or refresh data, don’t forget a few keys things: normalize, index, pick your design based on what you know about the data and what is being requested of it.

Servers are important, and if dealing with large databases, load balance or toggle whenever possible. Also, star schema versus snowflake schema is important, so please put some serious thought into this. Ask yourself, do I need it fast or efficient? Believe me, I always pick efficient (I am a nerd, after all) but if the client needs it ASAP, then the client shall have it ASAP.

With knowledge of the client’s IT setup from a data management/quality perspective, you’ll be equipped to handle most situations you run into when dealing with data, even if the Architect and Programmer are out sick. Your professional knowledge is going to be a big help in getting the assignment or job complete.

Happy data mining and please play with data responsibly!

About the Author

During the past 14 years, Carla Gentry has worked with Fortune 100 and 500 companies including but not limited to, Discover Financial Services, J&J, Hershey, Kraft, Kellogg’s, SCJ, McNeil and Firestone. Acting as a liaison between the IT department and the Executive staff, she is able to take huge complicated databases, decipher business needs and come back with intelligence that quantifies spending, profit and trends. Being called a data nerd is a badge of courage for this curious Mathematician/Economist because knowledge is power and companies are now acknowledging its importance. To find out more about what Carla does, please visit: http://Analytical-Solution.com.

Russia is the ninth largest country by population, with 143 million people.

The Russian economy is the world’s sixth largest by nominal GDP, with the 3rd largest nominal military budget. Russia is one of the world’s fastest growing major economies, and has a heavy dependence on raw materials (oil/gas) for its economy.

Russian MDM Market History

The MDM market started growing in Russia on the heels of major ERP implementations in the early 2000′s.

The first MDM implementations were in the oil and gas industry, followed by the mining and engineering industries. Initially, MDM implementations were Product MDM, used for purchased materials in procurement planning and economic logistics.

MDM was not used for tangible products since the oil and gas industry doesn’t deal in them per se. Customer MDM implementations were not common because customer master data was typically stored in the Enterprise Data Warehouse (EDW).

Early MDM implementations were handled by small Russian software vendors. Their software didn’t have a lot of capabilities and data integration functions were poor. But the software did create a golden record through a centralized schema.

Everything changed when SAP NetWeaver was introduced in Russia.

Russian MDM Market Today

Chart by Dmitry Kovalchuk

by Dmitry Kovalchuk

 The top Russian consulting companies offering MDM implementation services:

1. IBS, www.en.ibs.ru
2. LANIT, www.lanit.ru
3. KROK, http://business.croc.ru
4. I-Teco, www.i-teco.ru
5. Energy Consulting, www.ec-group.ru/en/
6. Intertech, http://www.intertech.ru/
7. EPAM Systems, http://www.epam.com/

Intertech has the most MDM implementations. IBS has the largest MDM implementations (in Gazprom and Rosatom).

The largest MDM implementations:

• Gazprom: Industry Database of Reference Information
• Rosatom: Single Industry Control System of Reference Data

Gazprom and Rosatom are monopolistic in their economic sectors, so they call their MDM systems “Industry”. Rosatom and Gazprom’s MDM systems are based on SAP MDM.

The overall Russian MDM market does not have many MDM software products available to it right now.

by Dmitry Kovalchuk

MDM Market barriers and other details

• Low level of mature systems standardization via the Government.
• Diverse languages and cultures cause barriers – Russia is very large and diverse which impedes standardization.
• Small budgets available for the development of IT in engineering, healthcare and other industries – many Russian industries are trailing other countries in IT sophistication.
• Low availability and low quality for technology education – affects the quality of professionals available.
• Master data is called “normativno-spravochnaya informatsiya” which means “normative-reference information”.
• Outdated knowledge about normative-reference information management in USSR.
• Complex ‘supply chain’ for providers of MDM solutions.
• High levels of business corruption.
• Many IT professionals in Russia end up immigrating to foreign countries

Russian MDM Market Future

Currently, there is strong growth in interest in MDM implementations in the following sectors of the economy:

• Government
• Banking
• Communications
• Manufacturing

MDM implementations in these areas will be more than 50% of all MDM implementations in 2012-2014.

A second wave of MDM implementations is expected in companies already using lightweight MDM solutions from small suppliers. They will begin to implement more functional and more expensive MDM products.

Russian system integrators will begin to offer MDM implementations in CIS, the Middle East and East Asia. (CIS is the Commonwealth of Independent States – former SSRs of the Soviet Union.)

Russia’s admission to the WTO (World Trade Organization) will generate a number of MDM projects, but no one can predict as yet what they will be.

1C Company is working on an inexpensive MDM software product that will satisfy the needs of small and medium-sized businesses. (1C Company is a large independent software vendor in Russia, with a broad range of offerings from video games to business software.)

Additionally, there will be major projects for cataloging products in Russian industry sectors.

IBS Group will bring Informatica MDM to the Russian market – Informatica MDM has been in great demand for the Russian government and banking sector. (IBS Group is a large provider of IT services in Russia, including software development, business technology consulting, and MIS implementations.)

System integrators in the CIS countries, after acquiring MDM expertise, will be able to compete with Russian system integrators in Russia.

Smaller vendors are likely to enter the Russian MDM market as well.

Microsoft is working on an inexpensive MDM product based on Microsoft SQL Server Master Data Services that will compete with 1C and other vendors.

About the Author

With over ten years experience in MDM implementations and data governance, Dmitry Kolvalchuk has worked in multiple industries and organizations in Russia including United Metallurgical Company, Gazprom, and the Federal Treasury of Russia. Dmitry has worked for several years as an independent consultant, business analyst and architect, and has experience with solutions from SAP, Oracle, IBM and Informatica, as well as in-house systems. Dmitry is currently in the Ukraine as a MDM Team Leader in Citia Business & Technology Consulting, where he is building the first-ever MDM practice for Ukrainian organizations.

The Call for Papers for the 2013 COLLABORATE conference is only open for a few more days!

Join Us in Denver as a COLLABORATE Speaker — Apply by This Friday, October 12

Do you have first-hand experience with Oracle Master Data Management that will benefit others in the Oracle MDM community? Can your insights save another company from learning an expensive lesson? Do you have a success story to tell?

The COLLABORATE 13 Call for Presentations is open now through October 12, 2012, and welcomes high-quality, objective presentations exploring Master Data Management, E-Business Suite (with an emphasis on R12), Fusion Technologies, Hyperion, Agile, Siebel, Enterprise Performance Management (EPM), Business Intelligence (BI), Primavera and more, to be presented live April 7-11, 2013 in Denver, Colorado.

Apply to become a COLLABORATE 13 speaker and, if selected, you’ll receive free conference registration. COLLABORATE is designed for users, by users and attracts over 5,000 participants for a week of exchanging ideas and techniques for maximizing the performance of Oracle Applications.

COLLABORATE 13 Education Tracks

COLLABORATE’s 1,000+ education sessions are organized into 23 tracks. Note that many tracks are accompanied by a “wish list” of session topics developed by the paper selection committee. These topics are of particular interest at this year’s conference.

Review the Education Tracks, the Presentation Selection Committee “Wish Lists” and the Detailed Guidelines for Speakers.

Deadline for Presentation Proposals: 11:59 pm US Eastern time on Friday, October 12, 2012

Don’t miss out on this great chance to share your knowledge with the Oracle MDM community!

In the quest for successful business intelligence, many organizations look at how they can access information through dashboards and scorecards, or interactive analysis and reporting. Self-service models and the increasing ability to interact with information more easily let more people access analytics and gain broader business insights. But this process also tends to overlook the increasing role of data management in delivering valuable, reliable, and relevant information to the business. After all, without a strong data infrastructure it is difficult to achieve confidence in the data being delivered.

Although front-end applications may provide organizations with the insights they need to identify opportunities and make better decisions, the reality is that the only way to successfully achieve this on a long-term basis is through a commitment to best practices in data management. And with an increasing focus on self-service BI and broader data discovery for business people, the reality is that BI initiative management is slowly shifting to become the responsibility of those outside traditional IT roles.

Consequently, this means that business decision makers need to have a better understanding of data management, how it works, and why it is important to overall success for both analytical and operational applications.

Why is data important – what happens without access

Historically, data was managed exclusively by IT departments. Any changes, additions, discrepancies, etc. that needed to be addressed, were done so behind the scenes. Business units submitted requests and then IT would implement the changes. With business intelligence becoming more user-driven, and with business decision makers wanting direct access to various types of data, the importance of information management has never been greater.

Having business analysts and other end users interact with and manipulate data in Excel to meet their business needs no longer works, and most likely never did. Although valid data may have been extracted from reliable data sources, once it was in the hands of an individual, its reliability and accuracy could not be guaranteed. Adding calculations, information from disparate data sources, and trying to balance discrepancies from multiple reports are just some of the ways in which spreadsheets have been used over time.

Unfortunately, the lack of security, data quality, ability to meet compliance requirements, and the like, are just some of the reasons why independent management of data does not work. Others include lack of trust in data, inability to consolidate information to get a broad view of the organization, inability to manage metrics effectively, and a lack of visibility into opportunities and challenges.

The bottom line for organizations is that in order to maintain valid, accurate, and reliable information access, data management challenges need to be addressed. Without access to the information needed to make better business decisions, companies cannot compete with the broader market landscape. With information requirements expanding, organizations cannot simply piece together their data sources randomly – they require a strong data management organization to ensure that data is transformed into valuable and actionable information. And luckily, the vendor landscape has expanded to provide data management platforms that address these challenges.

Introductory data management considerations for organizations

Although disparate industries may require different types of data to become better decision makers, there are some overarching considerations across vertical industries that all businesses need to look at in order to identify how to best manage their data.

Formalized data management initiatives are becoming an essential aspect for many organizations, with the management of data assets delegated to those within business units who understand the business rules behind the data itself.

This means that a wider variety of people within the organization require broader insights into their data. After all, data management involves more than databases, consolidated information, and master records. Business decision makers require an understanding of how business rules affect data flow and what areas of data management are essential versus “nice to have”.

In order to do so, companies should start by asking the following questions:

1. What are the three most important challenges we’re currently facing? Even though data management assumes that an organization will be managing large and/or diverse data sets, it is always important to make sure that any data-oriented initiative integrates tightly with current gaps in business visibility or performance. For instance, companies may look at Customer MDM (Customer Master Data Management) as a starting point due to the fact that they cannot currently understand the value of individual customers, how to target up-selling campaigns, or why retention levels are low.
2. Which stakeholders should be involved in the process? Taking the Customer MDM example one step further means identifying what information is needed to provide a more complete customer picture, and who are the various people who understand each of the aspects of a customer, for instance, accounts receivable, sales, customer service, etc.
3. What technologies are already in place, if any, to support the process? Although data management should be based on addressing business pains, the reality is that a strong IT infrastructure is still required. Organizations cannot overlook the potential need for more servers, better processing speeds, new types of storage and analysis, as well as how this may affect current or future resource requirements.
4. How should progress be tracked? There is a constant struggle to identify return on investment (ROI) within data-related projects. What organizations hope to achieve and how this will be measured ends up defining overall success. Developing metrics that include both short-term and longer-term goals can help sell the project to sponsors and ensure proper project management throughout the process.

Fitting all of the pieces together

The development and rollout of a data management program requires many pieces. The questions above provide a glimpse at what’s required to get an initiative off the ground. As organizations become more mature within their analytics applications, the ability to manage that data across the business to provide a consistent and accurate view of what’s happening becomes essential to continued success. This requires looking at how data can be used as a business asset by “connecting the dots” across disparate units to make sure that companies can support their supply chain, partners, and customers on a broader level.

Lyndsay Wise is the president and founder of WiseAnalytics. Lyndsay has ten years of IT experience in business systems analysis, software selection, and implementation of enterprise applications. She provides consulting services for small and mid-sized companies and conducts research into leading technologies, market trends, BI products and vendors, mid-market needs, and data visualization. For more information, visit http://www.wiseanalytics.com. 

We’ve had a couple of tough spots over the past five years, as you can imagine would be the case during the toughest economic downturn in the last eighty years. Fortunately, my family was very supportive and we made it through. Today, we’re on track to do triple the revenues we did last year. We just brought on Tom Atkinson, a great new employee to focus on business development and alliances.

We’ve got a couple of long term projects going on at great clients in Massachusetts and North Carolina, and interesting shorter term projects with clients in Chicago and Europe. We’ve worked with more than 35 different clients to date, in North America, Europe and Africa.

This online magazine continues to do very well, with readership up 16% vs. the same period last year. We’ve got 10-12 writers creating great original content for us, and a fantastic editor — Julie Hunt — who helps keep all the plates in the air.

The Hub Designs MDM Think Tank has done briefings over the past year or so with MDM and data quality vendors like Orchestra Networks, Collibra, IBM, Semarchy, Loqate, Liaison Technologies, and Varonis.

The MDM Community continues to grow, with more than 800 members now. It’s a great place for MDM practitioners to hang out and ask each other questions. I wish I had more time to devote to it, to ask and answer more questions myself. If you’re interested in joining, it’s at http://mdmcommunity.ning.com.

We created an MDM portal for the Life Sciences industry with our partner Advantage MS, in order to help life sciences professionals get up to speed on MDM quickly. If you’re interested, you can visit it at http://LifeSciencesMDM.com.

And our Thought Leadership practice continues to move forward, with activities over the last year like:

• a white paper published by Oracle Corporation,
• speaking engagement at Oracle OpenWorld 2011 last fall
• speaking engagement at the Oracle Health Sciences Innovation Forum this spring
• a series of blog articles sponsored by SAP
• a Data Governance Roundtable sponsored by Trillium
• a speaking engagement at the Oracle Applications Users Group COLLABORATE 2012 conference
• a webinar on “All Things Data” with Olmstead Associates and the Enterprise Data Management Council

So as we wrap up our fifth year, I want to thank my family – especially my wife and sons Conor and Brendan. I know there have been many sacrifices over the past five years; thanks for understanding.

To Hub Designs’ team members, thank you for your patience and hard work. To our clients, many of whom have become good friends, my sincere gratitude. Without you, none of the past five years would have been possible. To our partners, thank you for your contributions of knowledge, training, expertise and opportunities. I appreciate everyone who has read the online magazine, been a client, encouraged me in the tough times, or celebrated with me in the good times.

Tom Atkinson talked lately with one of our readers who said he reads this blog religiously. That’s a very nice complement, and one I take very seriously. We will continue to make strides in our Consulting and Thought Leadership practices, in the online magazine, with the Hub Designs MDM Think Tank, with the online MDM Community, with the Life Sciences MDM Learning Center, and with everything that we do. That’s how we do things here – we work hard and put our clients’ interests first.

I believe in karma, and doing the right thing for people – to help people find new jobs, to connect people looking for help with one another, to put good will out there into the universe, even when its hard. Because so many of you have returned that good karma to me over the past five years. Thank you.

Please “like” the Hub Designs Facebook page and follow us on Twitter at http://twitter.com/hubdesigns. 

Hingham, MA, September 5, 2012 – Dan Power, President of Hub Designs, announced that Tom Atkinson has joined the firm as Director of Business Development. He will be responsible for new client acquisition as well as supporting client engagements and strategic alliances.

Mr. Power said, “As our master data management (MDM) consulting practice continues to grow rapidly and focus on MDM strategy and data governance, we are very pleased to add such a senior, talented business development professional to our firm. Tom’s experience in management, information services and consulting is extensive.”

Mr. Atkinson has twenty-five years of experience in the information industry and brings a wealth of experience to Hub Designs in data management, data integration and master data management. He spent more than twenty-two years at Dun & Bradstreet as a principal consultant serving D&B’s global major customers.  He won D&B’s prestigious leadership award nine times and was consistently recognized by customers for his thought leadership and customer advocacy.  Most recently, Tom led his own consulting firm, helping his clients to maximize the value they derive from their information assets.

Tom resides in Northport, New York with his wife Pam and six children.  In his spare time, Tom coaches AAU basketball and follows the NBA’s Atlanta Hawks, where his brother is an assistant coach.

About Hub Designs

Hub Designs (http://hubdesigns.com) has been a leading consulting firm specializing in the development and delivery of high value master data management (MDM) and data governance strategies since its founding in 2007. Hub Designs is headquartered in Hingham, Massachusetts, and its clients include leading companies in the financial services, life sciences, high tech, information services, manufacturing & distribution, payroll, publishing and telecommunications industries.

*      *     *

CONTACT:

Dan Power
President, Hub Designs
781.749.8910
Contact Us