MDM: Buzz-Worthy But Not A Back-Breaker
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I saw an interesting post by Thomas Wailgum the other day called “MDM: Buzz-Worthy Since 2000, But Still a Back-Breaker”.
While I don’t agree that “there’s ongoing uncertainty as to when to take [MDM] seriously”, he does make some good points. The software vendors who’ve flocked to MDM and put the MDM label on everything under the sun have certainly confused the market.
Even so, the MDM software market grew 24% from 2007 to 2008. In spite of the tough economic times we’re currently in, that rapid growth rate should continue for the next several years.
One area I don’t agree with is the statement “it’s just plain hard to do … and even harder to do well”.
Don’t get me wrong, I’m not saying Master Data Management is easy. But I think it’s eminently “doable” if you:
- get yourself and your team educated on what MDM is all about and what it can do for your company
- develop a compelling MDM strategy that aligns well with your organization’s long term strategy
- get folks from the business and management on board through education, communication and evangelization
- create a strong business case and use it to manage expectations throughout the lifecycle of the project
- thoughtfully select the essential components (hub, integration, data quality, external content) and plan for data governance
- after starting your data governance program and selecting the technology components, follow some best practices for MDM implementation
Of course, there are going to be some failures along the way. But I come from the Enterprise Resource Planning (ERP) world, where a typical project was 1-2 years in length and cost in the tens of millions of dollars. To me, MDM doesn’t seem like a back-breaker. It seems like a great way of breaking down the walls of the typical corporate silos, complying more easily with ever-growing government regulations, increasing revenue by becoming more customer-centric (which in a recession, can make a big difference), and saving money through more efficient processes and consolidating out-dated systems.
What do you think? Please let us know via a comment here.
November Column in DM Review
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Here’s a brief excerpt from my latest “MDM Insights” column in DM Review.
In the past, I‘ve outlined the five essential elements of master data management: (1) an MDM hub of some type, (2) data integration or middleware, (3) data quality capabilities, (4) external content and (5) data governance.
One of the challenges of MDM is trying to make progress in all five of these areas at once, while simultaneously working across the spectrum of people, process, technology and information.
Think of it as the “big bang” approach to MDM. You evaluate and select an MDM hub, and in the process, you discover that your organization doesn’t have adequate data integration or data quality tools available. And while working with the business on what internal source systems and external content providers need to be integrated with the new hub, you realize that your organization finally needs to get serious about data governance as well. It can be a little overwhelming.
Click on “Easing Into Master Data Management” to continue reading.
As always, please let us know what you think by commenting here …
Keynote at Oracle BI SIG Conference
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The Oracle Business Intelligence Special Interest Group, which is part of the Oracle Applications User Group, is hosting Desktop Conference 2008, its annual online conference, in mid-November.
Here’s a brief description:
“Join the Oracle Business Intelligence community in the only global, online business intelligence conference that addresses business intelligence and data warehousing topics related to the Oracle technology stack.”
The SIG president, Faun deHenry of FMT Systems, asked me to do one of the keynote sessions.
It’s titled “Master Data Management 101″ and will be covering:
- what is Master Data Management (MDM)?
- some useful MDM and Data Governance best practices
- what works and what doesn’t
- importance of a holistic approach to MDM
- how to get the political aspects right
- the relationship between MDM and Business Intelligence
The session will be held online on Wed. November 12th at 2:45 pm Eastern, 11:45 am Pacific. Click here to see the agenda and here to register.
MDM in Tough Economic Times
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It’s too early to tell exactly what effect the current economic downturn will have on the Master Data Management (MDM) space.
Software vendors are probably going to see at least a short term slowdown in orders, and consulting firms may already be feeling the effects in terms of canceled or delayed projects.
But unless you’re at one of the financial giants whose troubles are front page headlines, stay the course.
Master Data Management projects are typically so compelling that canceling them is like “burning the furniture”.
A good MDM strategy typically includes a strong business case, with “quick win” elements like increased revenue due to capturing currently missed cross-sell and up-sell opportunities, support for improved analytical marketing, and cost reductions through increased productivity and consolidation of systems and applications not needed after implementing the MDM hub.
I came across a great quote by John Radcliffe at Gartner:
“But there are many other pieces of MDM — like compliance, risk management, cost reduction — that aren’t nice to have, but are essential even if the economic climate is poor. We should see growth in these areas of MDM. They still need to get done and they actually help people during an economic slowdown.”
Perhaps I’m whistling past the graveyard a bit here, but although I’m sure MDM will be affected by global economic conditions, I’m hoping the effects will be less severe and of a shorter duration than other, more hard hit areas of the economy.
I’m planning to attend the Gartner MDM Summit on Nov. 17-19 in Chicago, and I spoke at the SourceMedia / MDM Institute event last week.
Those two events should give me a pretty good “pulse check” on how much impact current economic conditions are having on the MDM space. I’ll report back to you here on what I find, and please comment here to let us know your views and opinions on this question.
What’s in a Name?
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As part of implementing Master Data Management (MDM) for customer information, one needs to define the “customer data model” that will be deployed in the hub.
To do this, quite often, a company will conduct workshops to get agreement on the common definition of “the customer”. The participants are all the groups or departments that touch and use customer data. These may include Marketing, Sales, Finance, Customer Service (and sometimes Legal).
The objective of these workshops is to list out the entities that are in scope for the MDM project, identify the attributes which define an entity, the possible sources of data for that entity, the business purpose of the entity and the consumers of the entity. As a secondary objective, the next step is to define the relationships among the entities and if there is any need for hierarchical representation of these relationships in the hub. But all this is definitely not an easy task to accomplish.
As an example, take the “company name” attribute for a corporation. The Sales function defines the “company name” as the name on the customer’s business card. Legal, however, needs the legal entity’s name and any alternative names, DBAs or tradestyles. Finance may want to identify the corporation with its D&B-provided name (since credit reports may use that). Tax folks may need the previous names under which this customer has transacted. Customer Service gets the “customer name” from the installed base and Marketing gets it from an external list vendor.
So there you go. These are several different potential views just for “company name”. And you thought, agreeing on the “name” definition would be easy!
Similar issues surface when defining the address-related attributes.
By now, you may be asking yourself, “So, does this end up like spaghetti, with no easy way out?”
A better approach is to gather the customer data from various systems and profile that data before the workshops. Observe the variances in “company name” from various systems and build rules based on those variances. Typos can be weeded out. Standards can be designed and proposed to eliminate the “name duplicates”. Use examples proactively. Then based on these findings and the proposed standards, conducting these workshops will be a much smoother task.
Even after this, if there is no agreement, your data model may need multiple “company name” fields to represent the “name” attribute. The objective is to minimize the number of such occurrences.
September Column in DM Review
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Here’s an excerpt from my latest “MDM Insights” column in DM Review.
After watching both the Democratic and Republican National Conventions I saw a pattern playing out that (believe it or not) applies to master data management (MDM) projects and ongoing data governance initiatives.
Just as a strong business case is usually important in getting initial funding, communicating your successes is critical to retaining it. But, it’s usually better to let someone else tell your story.
Click on “Taking Credit for Your MDM Success” to continue reading.
And please let us know your thoughts by commenting here …
Structured vs. Ad Hoc Data Governance
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I was struck recently by the difference between companies that have a formal, structured approach to data governance, versus an informal, unstructured or “ad hoc” approach.
In many cases, companies with an ad hoc approach already have the right people, in the right places, doing the right things.
But it’s not formally part of their job description. They just do it because they know it’s the right thing to do, or that the company really needs it.
So they act as unsung heroes of data stewardship, cleaning up data manually, writing scripts to make data corrections in bulk, even working together in teams to do data governance tasks, without ever formalizing it into a data governance program.
I wrote yesterday about whether data governance should be located in the business (with support from IT) or in IT (with support from the business). It’s a natural tendency of business people to think that data management, since it involves computers, should be part of IT. And it’s a natural tendency of the IT people to think that only the business knows the subject matter well enough to manage it.
But wherever you stand on this question, I think it’s better to have a structured approach to data governance. Set up a data governance committee or team, define its mission and processes, and give them the technology tools they’ll need to achieve the mission.
Relying on an ad hoc or informal approach is risky. People take new jobs, go on vacation, or get burned out. So you can’t rely forever on the unsung heroes of data stewardship.
I’ve said many times that if companies treated their physical assets (like inventory or cash) the same way they treated their information assets (particularly customer data, for some reason), then people would be going to jail.
Start thinking about how your organization can improve its data governance maturity, or start a data governance function, if you don’t already have one. You’ll find that “when the student is ready, the teacher will appear”. In other words, once you start, if you remain diligent and patient, the rest of the organization will ultimately see the value of adding data governance to “how we do things here”.
Here are some good resources for further reading:
- “So You Want to be a Data Champion?” by Tom Carlock
- Wikipedia article on Data Governance
- The Data Governance Institute’s Data Governance Framework
- The Master Data Management Institute
- Data Governance Blog
Please let us know via a comment if you have any other resources on data governance you’d like to suggest.
Where Data Governance Belongs
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Both IT people and business people usually realize when data management issues are having an impact on the company. And senior executives are usually at least aware of the issues with important master data domains like customer, supplier and product, because they live with the end results of data quality issues every day.
But sometimes the business is reluctant to hire anyone to work on data quality or data governance. So here’s my question: is it better for the IT team to take that on, if the business doesn’t step up to the plate?
I usually recommend that Master Data Management (MDM) and data governance programs be driven by the business, and in a perfect world, that probably is the best route.
But even if the business is driving, they usually need a lot of IT support. And if the business doesn’t want to take on the issue at all, perhaps it’s better to have IT doing it than have no one doing it.
Please share your thoughts via a quick comment here.
One Year Anniversary of This Blog
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We’ve been writing this blog for a year now, with a total of 83 posts so far.
It’s been a very positive experience. We’ve had clients tell us “the reason we hired your firm was because of your blog”. And we’ve gotten lots of great feedback from our partners (Oracle, IBM, SAP, Initiate Systems and Siperian).
What we’ve tried to do is to write for people who are new to Master Data Management (MDM) and looking for basic information (like “Useful Definitions for MDM”,“Five Essential Elements of MDM” and “Ten Best Practices for Master Data Management”).
But we’ve also tried to cover more advanced topics too (such as “Master Data Management and the Art of Politics”, “The Key Requirement in Choosing a Product MDM Hub”, and “Data Governance Critical to MDM Success”).
We thought that by presenting a mix of basic and advanced topics, and highlighting key milestones in the development of the firm, we could keep your interest, and hopefully keep you coming back.
The numbers tell a good story. We’ve had a total of 8,100 hits in the past year, with an average of 32 hits per day (over the last 30 days), 200 hits per week and 835 per month (over the last 6 months).
Our “Top 10″ posts have been:
- Ten Best Practices for Master Data Management
- Our MDM Partnership Strategy
- How Master Data Management is Similar to ERP
- Different Styles of MDM Hub
- Metadata and Master Data Management
- Five Essential Elements of MDM
- Critical Data Quality Questions
- The Key Requirement in Choosing a Product MDM Hub
- Master Data Management and the Art of Politics
- MDM Business Case Creation & ROI Analysis
We get most of our traffic from our web site at www.hubdesigns.com (there’s a prominent “Blog” link there), and from the “Master Data Management” and “Customer Data Integration” tags at WordPress.com. We also get a fair amount from Google Reader, My Yahoo, and my LinkedIn profile.
Our Top 10 search terms that people are using to get to the blog are: “Hub Solution Designs”, “Dan Power”, “Gaurav Arora”, “data quality questions”, “MDM vendors”, “Master Data Management best practices”, “critical to quality”, “Oracle MDM”, “ERP and MDM” and “Master Data best practices”.
We’ve tried to keep the blog vendor-neutral, and have resisted the temptation (so far at least) to accept any form of advertising.
In the coming year, we’re looking forward to more in-depth coverage of the leading MDM and data quality platforms, more insights gleaned from working with our clients, more pointers to other places where our writing appears (like my monthly column in the online edition of DM Review), and continuing to try to break new ground and be thought leaders on MDM.
If there’s anything in particular you’d like to see us cover here, please let us know via a comment. It’s been an honor to write for you over the past year, and we’ll work hard to make this a useful resource for you in the coming year.
July Column in DM Review
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Here’s a quick excerpt from my latest “MDM Insights” column in DM Review.
It’s a long journey from the first efforts of “customer cleanup” to a full-fledged data governance program. But that’s where many companies start. They gradually accept that there are issues with their customer data such as:
- A lack of consistently applied standards and controls,
- Problems arising from conversion of customer data from acquired companies,
- Lack of ownership of customer data,
- Invalid addresses leading to undelivered and returned mail or
- Customer service problems caused by large numbers of duplicate and inaccurate records.
So they form a committee, hire a consulting firm, and involve their internal IT folks. That’s a great start, but it’s important to realize that this is not a once-and-done project.
Click on “From Customer Cleanup to Data Governance” to continue reading.
And please let us know your thoughts by commenting here …
Building a Data Governance Organization
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I’m often asked how many data stewards a company needs.
A rule of thumb I’ve used for quite a while is “one data steward per $1 billion in annual revenue”, but of course that can’t be a hard and fast rule.
Depending on the company, the degree of automation, what data quality tools are available, and the major processes around the enterprise, that number is going to vary.
Here are some thoughts to keep in mind as you build your data governance organization:
- Build the business case and secure your funding. Don’t forget this is a multi-year effort. Several times, I’ve seen data governance teams and MDM initiatives that never thought beyond their first year’s funding, and who were surprised when funding for Year 2 and beyond wasn’t forthcoming.
- You’ll probably need a multi-layer, cross-functional approach. Start with a high level group that sets data policy, usually called a Data Governance Steering Committee or Data Governance Council (DGC). Your executive sponsor(s) should be involved with the DGC. Then you’ll probably have a small group of data stewards which handle day-to-day data management, which reports to the Data Governance Council.
- Focus on your data governance processes, including relevant metrics that show the impact to the business, and on communicating those metrics and their impact to the rest of the enterprise. Create a communications and education strategy to get the word out.
- Look for IT people who “speak business” and business people who “speak IT”. These liaison-type people are going to be crucial to your data governance organization. Although I believe that data governance should be driven by the business, there’s no question that you’re going to need a lot of support from IT. So look for the “bi-lingual” folks in your company – they’re out there and they can be incredibly valuable in understanding the business impact of data issues, and in providing the technology support that the data stewards will need.
- Don’t forget about metadata. In your travels as a data governance organization, you’re going to run into a lot of metadata (information about information). Develop a disciplined approach to cataloging and maintaining the metadata you come across. In many ways, that will probably be one of the most useful outputs of your data governance initiative.
We’ll have more to say on data governance in this blog in the future, but let me leave you with one idea. Instead of describing your MDM initiative as (hopefully) including data governance, think of it as a data governance initiative that has an MDM component.
You’ll be doing data governance in your enterprise long after your current MDM hub implementation is over.
If you don’t agree, or you have a perspective on data governance you’d like to share, please comment here on this blog.
Building the Business Case (Part 4) – Gaining Alignment
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In the previous installments of this series, we covered three key drivers for building your business case: Risk Management, Cost Reduction, and Revenue Growth.
Now, we’ll review the importance of and the process for gaining organizational alignment with your strategy.
When you’re building support for your business case, it’s critical to gain alignment at all levels of the organization throughout the whole process. Making the case for an information management strategy cannot rest with only one executive. And it can’t be the brainchild of IT only, or lack executive sponsorship altogether.
You need as many areas aligned as possible. More than likely, a comprehensive information management strategy needs to consider all of the data streams across the enterprise (at some point), and will therefore be fairly time and resource intensive.
If you take a three-pronged approach to gaining alignment, then you’re well on your way to obtaining approval to implement your strategy:
(1) Get front-line employees and customers to identify the problems with the data. You should have been gathering their feedback and facts as you built your case. So when you can readily articulate that customers are frustrated with your company, or that your employees are performing workarounds, rework, or aren’t as effective as they could be, then you’ve got your “first-level buy in”.
(2) You need their individual management teams to agree that these issues exist. They need to agree that they’d be more effective in achieving their goals if they had a solution to their information problems. And most importantly, if you can get them to agree that an information management strategy should be a priority and they support the contents of the business case (which shouldn’t be too difficult if you had their support during the development of the business case), then these folks become your best allies.
(3) Have those management teams bring this message forward to their leadership. When the leaders hear, directly from their own people, that they should understand and support the business case, then your business case has achieved a level of credibility you wouldn’t have gained on your own. And your role then becomes one of subject matter expert, business case developer, and valued business partner.
I do recommend getting several executives aligned to the strategy. Because of the size of the undertaking, you’ll need several leaders to prioritize and support the effort. Providing headcount, funding and the time to deliver on the plan will be crucial from these leaders.
Once again, the more compelling your business case (whether it’s to comply with regulations, reduce costs or improve revenues), the more chance you’ll have in gaining attention and alignment.
Which brings us to the final point: set up your program so that small wins are achieved throughout.
Whether you need to set up prototypes, pilots, or small projects while you are driving the entire strategy over time (probably several years), you need to prove results. Otherwise, no matter how great your plan, the organization will lose interest along the way.
So, if you set expectations appropriately, have a good measurement plan in place, and keep communicating constantly with all levels of the organization, then you’ve got a great chance of succeeding!
Interview at MDM Summit on DMRadio
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I was interviewed recently at the Spring 2008 MDM Summit in San Francisco.
DMRadio (which broadcasts weekly on DMReview.com) did a broadcast from the exhibit floor, featuring:
- Marty Moseley from Initiate Systems
- Christopher Dwight from Oracle
- Dan Power from Hub Solution Designs
- Justin Magruder from Freddie Mac
- Richard Pilkington from SyncSort
- John Smolarski & Anshuman Sindhar from Countrywide
- David Codelli, Sun Microsystems
To hear the interview, just click http://www.dmreview.com/dmradio/10001100-1.html, then click on the third “Play” button from the top.
Keys to a Successful MDM Program
Master Data Management (MDM) initiatives often seem to begin with the CIO and consequently, the implementation takes on a strong technology focus.
But in today’s article, we want to suggest an approach that’s more likely to succeed in the long run – tying the MDM project to solving an important business problem, and then getting the business to not only sponsor the initiative, but to “own” it.
Depending on your industry, there are key business drivers frequently seen in that industry. For example, in manufacturing, the key drivers are usually margin analysis, supply chain analysis, product profitability and customer satisfaction. In the software industry, license revenue analysis, maintenance contract revenue (new and renewals), support margins and customer satisfaction are the key drivers.
When you talk about how MDM may improve results in these areas, the business owners perk up and listen. So invest some time in understanding the corporation’s strategic priorities for the next few fiscal years, and then choose a small number of these strategic priorities as the key drivers to be tied to MDM.
At a leading software company, Marketing had recently undergone a radical overhaul. The new head of Marketing was swamped by the number of “mini-databases” that had sprung up, both within the department itself and within IT. For their launch of their new software product, he needed to know who his customers were – on a particular version, at a particular support level, and in a particular geography.
It took the Marketing department weeks to get that final list. As a result, the CIO stepped up and linked the MDM initiative’s success to specific metrics used by Marketing.
Marketing was then totally engaged in the MDM project, and that momentum carried right through the product launch. And Marketing even hired a data steward for ongoing data management.
Had it only been the Technology group carrying the burden of doing the MDM project, I’d bet the project would have fallen by the wayside and there would not be any surviving MDM program there.
The key takeaway is to link your Data Quality and MDM initiatives to your enterprise’s key business drivers and your executives’ priorities. Only then you will get the business to put their money where their mouth is. And only then will you be assured of a successful ongoing MDM program.
Data Governance Critical to MDM Success
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I was reading a whitepaper by Aaron Zornes today (“2007-2008 Scorecards for Data Governance in the Global 5000 Enterprise”) and came across an interesting quote:
“Although many organizations have improved end-to-end business processes through CRM implementations, the challenge of developing a unified customer or product view has not been fully addressed by the application suite vendors. For example, enterprise CRM solutions such as Siebel Systems supposedly were to integrate sales, marketing and service functions but in reality provided mostly automation of the sales force with arduous and fragile interfaces between sales, service and marketing. Concurrently, enterprise resource planning (ERP) was marketed as the integration among accounting, manufacturing and distribution. In practice, large enterprises are now turning to MDM as the service-oriented architecture means of unifying both CRM and ERP individually as well to integrate the front office (CRM) and the back office (ERP) together. “
In my experiences over the past twenty years or so, the enterprise software implementations I’ve been part of have treated data and process integration as a “necessary evil”. Almost like a difficult proof in a college math class, where the professor takes you up to a certain point, and then as the class ends, calls out that “the rest of the proof is left as an exercise for the class”.
I’ve seen projects where several systems were supposed to be integrated but never were, or where the front office and back office were integrated through “manual integration”, i.e. manual re-keying of key customer and product information between the two systems.
Little wonder, then, that ERP and CRM investments in many cases failed to deliver their expected return on investment. And now large enterprises are turning to Master Data Management (MDM). Given that successful MDM implementations requires five essential elements (data governance, a hub platform, integration, data quality and external enrichment capabilities), the temptation is there for people to de-scope important aspects of MDM.
Just as critical interfaces were de-scoped from earlier ERP and CRM projects, we’ve started to see people trying to do MDM without data quality, and even without adequate integration.
But let’s collectively resist these temptations. MDM and data governance are “hot” right now because they offer the promise of accurate, complete, timely and consistent information across the enterprise.
If we start to compromise on the essential elements of MDM, or fail to address MDM’s interconnected nature of people, processes, technology and information by focusing only on the technology, then in the not-too-distant future, MDM will not only go through Gartner’s “Trough of Disillusionment”, but it will be largely discredited. The industry will miss out on some huge future opportunities, and global enterprises will miss out on the ability to invest in their people, redesign their processes, implement new technology for MDM and service-oriented architecture, and weave in external information to supplement their internal data.
We all understand the pressure in the corporate world to deliver results in one quarter or less, but let’s make sure our short term approach doesn’t compromise the long term vision so much that the longer term return on investment becomes unachievable.
I think we’ll see data governance leading the way. In the conclusion of Aaron’s white paper (which was published by The MDM Institute on behalf of Purisma, by the way), he says:
“Data governance is critical to these master data management efforts and ultimately is the tipping point as to whether the MDM program’s business outcome achieves its intended ROI and long-term sustainability.”
So resist the temptation to identify the need for Master Data Management, and then immediately run out and engage a systems integrator to help you evaluate, select and deploy some MDM technology. Remember to invest (either up front or in parallel with your MDM selection and deployment) in defining a workable data governance organization with accompanying business processes.
By paying attention to the integration between data governance (i.e. the people and processes) and the MDM techology (hub platform, integration, data quality and external enrichment), you’ll dramatically increase your chances for the successful delivery of expected functionality and ROI, on time and on budget.
Cover Story in “DM Review”
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We wrote the cover story in this month’s issue of DM Review magazine.
The article is on “The Politics of Master Data Management & Data Governance”, and you can find it at: http://www.dmreview.com/issues/2007_45/10000894-1.html.
Please let us know what you think!
Upcoming Speaking Engagements and Magazine Articles
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It never rains but it pours. It looks like we’re going to be doing two speaking engagements in March & April – at the Spring 2008 MDM Summit in San Francisco, CA, and the Oracle Applications Users Group COLLABORATE 08 conference in Denver, CO.
The MDM Summit conference has grown to be one of the largest gatherings of data integration professionals, with 600+ visionaries and vendors in one location. The session, which will be presented jointly by myself and our client, Shirlee Collins from ADP Dealer Services, is entitled “Real World Data Governance”, and will be presented on Monday, March 31st from 3:05-4:05 pm PDT. We’ll talk about establishing a data governance organization, improving underlying customer data quality, and creating a robust process to enrich customer data in Oracle Customer Hub with D&B information. The ADP Dealer Services story reflects a pragmatic approach, weaving together Sales Operations and Finance, and balancing each group’s needs and priorities in managing customer master data.
I’m also speaking at COLLABORATE 08 in Denver, CO, which is the annual conference of the Oracle Applications Users Group (OAUG), and which will have a Master Data Management track for the first time. I’m a member of the OAUG Education Committee, and helped to plan & organize the MDM track of the upcoming conference. The session, “Best Practices in Master Data Management and Data Governance”, will be presented on Tuesday, April 15th from 9:45-10:45 am MDT. It will present some useful MDM and Data Governance best practices, and will also cover what works and what doesn’t, the importance of a holistic approach, how to get the political aspects right, and how to address more than just the technology elements.
DM Review magazine is publishing an article I wrote on “The Political Aspects of Master Data Management and Data Governance” in its upcoming March issue. I was published back in February 2007 in a DM Review Special Report. The new article suggests several ways to deal with the difficult political aspects of MDM projects to make those initiatives more successful.
Our VP & Partner, Tim O’Sullivan, also wrote an article that will be published as the key feature of an upcoming MDM supplement in the June issue of DM Review. The article on “Project Management Challenges within a Changing Landscape” explores project management best practices for MDM initiatives, and provides a framework for addressing MDM’s typical political, technological and data stewardship challenges.
Is There Such a Thing as a “Quick MDM Strategy”?
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Well, the short answer is – “it depends”! Putting aside the conventional answer for the moment, I’d say that a critical first step in achieving a pragmatic MDM strategy is that your company must agree and commit to developing a 3-5 year MDM strategy.
This is not as simple as it may sound. Successfully executing an MDM strategy with a 3-5 year vision requires a considerable cross-functional effort and substantial agreement on some vexing political, business and technology issues. More on this later.
The following situation is not uncommon. A senior executive at the company decrees that the company needs an MDM solution to remain competitive or to fix nagging data issues. The vendor selection team jumps into action, solicits requirements from business owners, puts together a vendor questionnaire and contacts vendors.
The next few months are taken up with defining selection criteria, sitting through vendor demonstrations and digesting different vendors’ current approach to MDM and their future roadmaps.
The promise of a business intelligence solution, with a Single View of Customers, across CRM and ERP systems, through a federated or persistent hub is now only months away.
The demos were impressive and installation is promised to take only 8-12 weeks. After all, the company has already implemented CRM and ERP solutions, so the next solution should be much quicker – right?
Which brings us back to “it depends” The reality is that this approach may work for certain companies. Those companies who have matured through CRM and ERP implementations, who have well defined business needs and change management projects under their belts, with well documented business processes and a Project Management Office already in place stand the best chance to succeed.
But then these are the very companies who already know that they need to develop an MDM strategy, often with the help of consultants.
And this leads us to the question of developing an MDM strategy. How comprehensive does the strategy need to be? How much time is reasonable to spend developing a strategy? Can a staged approach be taken on the strategy, for example just start with customers and worry about other domains once that is working? Can we just bring a hub up with our current customer data and go from there? These are all valid questions.
One of the biggest factors that will help answer these questions is an organizational readiness assessment.
The strategy process should develop a vision for data governance, business outcomes, business processes and technology for the company. The process will touch on situation analysis, goals and objectives, strategy development, implementation plans and change management. Involving consultants through the process can greatly speed the process for many companies.
Our recommendation is that no matter how tempting a “quick win” approach to implementing MDM may seem – make sure you take some time up front to develop your strategy with both short and long term goals. Make sure that strategy is accepted throughout the organization and that the short term goals are on the correct path to solving the longer term business objectives.
This can help you avoid having to “rip & replace” your MDM solution shortly after building it, and help prevent you from creating MDM “data silos” because your strategy didn’t take into account other critical enterprise data domains beyond the immediate situation.
MDM can be a “game changing” initiative, giving the enterprise clean, consolidated, complete data for the first time, driving increased revenue, decreased costs and improved compliance. But as Stephen Covey says, make sure you “begin with the end in mind”.
