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Archive for April 2009

30
Apr

Heading to OAUG

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I’m heading to Orlando, FL this Sunday to attend and speak at the annual Oracle Applications Users Group (OAUG) conference.

I’m a volunteer member of the OAUG Education Committee, managing the Master Data Management track.  As such, I get to work closely with the Special Interest Group coordinators, and have a lot of fun planning the the MDM part of the conference.

This year, I’m very interested in hearing what all of our great MDM track speakers will have to say, and catching some of the Oracle executive presentations on their progress towards the Fusion applications suite.

As you might expect, I’m particularly interested in the Fusion MDM Hub, and Pascal Laik from Oracle will be doing a session on that.

I’ll try to write a few “dispatches from the front lines” here during the conference to share my thoughts on the various sessions.

Hope to see you in Orlando!

23
Apr

May Column in Information Management

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Here’s a brief except from my monthly column in the May 2009 issue of Information Management magazine.

Master data management for product data (known as PIM, for product information management) is a different kettle of fish altogether from MDM for customer data (also known as customer data integration, or CDI). It is important to recognize and consider the fundamental differences between the two.

Click on “Product Information Challenges” to continue reading.

Please let me know what you think of the article by commenting here.

18
Apr

Modeling the MDM Blueprint – Part 5

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er_modelIn this series, we’ve discussed developing the MDM blueprint by creating the Common Information (Part 2), Canonical (Part 3), and Operating (Part 4) models in our work streams. We’ve introduced the Operating Model into the mix to communicate with the business how the solution will be adopted and used to realize the expected benefits. And hopefully we’ve set reasonable expectations with our business partners as to what this solution will look like when deployed.

Now, it’s time to model and apply the technical infrastructure or patterns we plan on using. The blueprint now moves from being computation and platform independent to one of expressing intent through the use of more concrete platform-specific models.

Reference Architecture

After the initial (CIM, Canonical, and Operating models) work is completed, then, and only then, are we ready to move on to the computation and platform specific models. We know how to do this – for example see Information ServicePatterns, Part 4: Master Data Management architecture patterns.

At this point, we now have enough information to create the reference architecture. One way (there are several) to organize this content is to use the Rozanski and Woods extensions to the classic 4+1 view model introduced by Philippe Kruchten. The views are used to describe the system in the viewpoint of different stakeholders (end-users, developers and project managers). The four views of the model are logical, development, process and physical view. In addition, selected use cases or scenarios are used to demonstrate or show the architecture’s intent. Which is why the model contains 4+1 views (the +1 being the selected scenarios).

41views1

Rozanski and Woods extended this idea by introducing a catalog of six core viewpoints for information systems architecture: the Functional, Information, Concurrency, Development, Deployment, and Operational viewpoints and related perspectives. This is elaborated in detail in their book titled “Software Systems Architecture: Working with Stakeholders Using Viewpoints and Perspectives”.  There is much to learn from their work, I encourage you to visit the book’s web site for more information.

What we are describing here is how MDM leadership within very large-scale organizations can eventually realize the five key “markers” or characteristics in the reference architecture to include:

  • Shared services architecture evolving to process hubs;
  • Sophisticated hierarchy management;
  • High-performance identity management;
  • Data governance-ready framework; and
  • Registry, persisted or hybrid design options in the selected architecture.

This is an exceptional way to tie the technical models back to the stakeholders needs, as reflected in the viewpoints, perspectives, guidelines, principles, and template models used in the reference architecture. Grady Booch said “… the 4+1 view model has proven to be both necessary and sufficient for most interesting systems”, and there is no doubt that MDM is interesting. Once this work has been accomplished and agreed to as part of a common vision, we have several different options to proceed with. One interesting approach is leveraging this effort into a Service Orientated Modeling Framework introduced by Michael Bell at Methodologies Corporation.

Service-Oriented Modeling

The service-oriented modeling framework (SOMF) is a development life cycle methodology. It somf_v_2_0offers a number of modeling practices and disciplines that contribute to a successful service-oriented life cycle management and modeling. It illustrates the major elements that identify the “what to do” aspects of a service development scheme.

These are the modeling pillars that will enable practitioners to craft an effective project plan and to identify the milestones of a service-oriented initiative—in this case crafting an effective MDM solution.  SOMF provides four major SOA modeling styles that are useful throughout a service life cycle (conceptualization, discovery and analysis, business integration, logical design, conceptual and logical architecture).

These modeling styles: Circular, Hierarchical, Network, and Star, can assist us with the following modeling aspects:

  • Identify service relationships: contextual and technological affiliations
  • Establish message routes between consumers and services
  • Provide efficient service orchestration and choreography methods
  • Create powerful service transaction and behavioral patterns
  • Offer valuable service packaging solutions

SOMF Modeling Styles

SOMF offers four major service-oriented modeling styles. Each pattern identifies the various approaches and strategies that one should consider employing when modeling MDM services in a SOA environment.

Circular Modeling Style: enables message exchange in a circular fashion, rather than employing a controller to carry out the distribution of messages. The Circular Style also offers a way to affiliate services.

Hierarchical Modeling Style: offers a relationship pattern between services for the purpose of establishing transactions and message exchange routes between consumers and services. The Hierarchical pattern enforces parent/child associations between services and lends itself to a well known taxonomy.

somf_stylesNetwork Modeling Style: this pattern establishes “many to many” relationship between services, their peer services, and consumers similar to RDF. The Network pattern accentuates on distributed environments and interoperable computing networks.

Star Modeling Style: the Star pattern advocates arranging services in a star formation, in which the central service passes messages to its extending arms. The Star modeling style is often used in “multi casting” or “publish and subscribe” instances, where “solicitation” or “fire and forget” message styles are involved.

There is much more to this method, so I encourage you to visit the Methodologies Corporation site and download the tools, power point presentations, and articles they’ve shared.

Summary

Based on my experience, we have to get this modeling effort completed to improve the probability we’ll be successful. MDM is really just another set of tools and processes for modeling and managing business knowledge of data in a sustainable way. Take the time to develop a robust blueprint to include the Common Information (semantic, pragmatic and logical modeling), Canonical (business rules and format specifications), and Operating Models to ensure completeness. Use these models to drive a suitable Reference Architecture to guide design choices in the technical implementation.

This is hard, difficult work. Anything worthwhile usually is. Why put the business at risk to solve this important and urgent need without our stakeholders understanding and real enthusiasm for shared success? A key differentiator and the difference between success and failure on an MDM journey is taking the time to model the blueprint and share this early and often with the business. This is after all a business project, not an elegant technical exercise. Creating and sharing a common vision through our modeling efforts helps ensure success from inception through adoption by communicating clearly the business and technical intent of each element of the MDM program.

In the last part of the series, I’ll discuss where all this fits into the larger MDM program and how to plan, organize, and complete this work.

Continue with Part 6 or go back to Part 4.

14
Apr

SmartCo

Editor’s note: another in an occasional series where the Hub Designs Blog profiles companies and solutions you may not have heard of that are relevant to master data management (MDM).

SmartCo Logo

Company & location: SmartCo, headquartered in Paris, France, with an office in Boston MA, provides a product called the SmartCo DataHub, a master data management solution for financial institutions.

Value proposition: SmartCo DataHub consists of several data management modules including a Security Master, which handles every type of asset class and manages reference data, market data and corporate actions data. The product can receive information from many different internal or external sources, and then cleanse it, enhance it and distribute it to all departments and systems, so everyone shares the same data.

SmartCo DataHub also provides other modules such as Indices and Benchmarks, and Business Entity Management, which centralizes and consolidates all information about third parties with which the financial institution is directly or indirectly in business. This is linked to the Security Master for monitoring and mitigating credit and operational risks.

SmartCo DataHub has built-in connectors to data sources like Bloomberg, Thomson/Reuters, Factset, Interactive Data, Markit, Six Telekurs / Fininfo, and several others. SmartCo DataHub is designed using the latest SOA technology in order to provide users with more flexibility.

What point in MDM lifecycle: this would be most appropriate for banks and other financial institutions looking to replace one or more internally built security masters. Most financial services companies don’t regard creating their own custom security master as a competitive advantage any more. So a “commercial off-the-shelf” (COTS) solution might be a good fit for companies looking to reduce the number of security masters they’ve got to maintain, and save money vs. developing a new security master internally.

Relevance to MDM: the financial services industry is going through its biggest upheaval in more than 75 years. But consolidating multiple custom built systems that are expensive to maintain can save a lot of money and provide a very strong return on investment.

If you’re in the financial services industry and are investigating master data management as a strategy for cost savings, revenue enhancement or regulatory compliance, SmartCo is an interesting company that is growing its presence in the North American market.

13
Apr

Silver Creek Systems

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Editor’s note: another installment in an ongoing series where the Hub Designs Blog profiles companies and solutions which are relevant to master data management (MDM).

Silver Creek Systems

Company & location: Silver Creek Systems, headquartered in Westminster, Colorado, provides automated data mastering solutions which enable enterprise-wide standardization and integration of product information.

Value proposition: I recently had a briefing with several Silver Creek people. Their core product, DataLens™, applies semantic technology to standardize, enrich, match, repurpose and govern product information. I think of it as data quality for product information on steroids.

The semantic approach makes a lot of sense. I remember from my ERP days how painful dealing with product information can be (requiring endless massaging in Excel or complex SQL queries to extract and reformat it). Silver Creek seems to have an intelligent solution to one of the thorniest issues in MDM.

What point in MDM lifecycle: if your MDM initiative involves product information, you’ll quickly find out that Product MDM is very different from Customer MDM. It’s common for product data to have dozens or even hundreds of required attributes. The hierarchy management requirements for product data are typically more complex. And because a lot of product data is unstructured or semi-structured, you need a specialized parsing engine if you want to automate the standardization of your data.

Relevance to MDM: data quality tools designed for customer information have a hard time handling the widespread variability of product data, its relative lack of structure, the dearth of referential data from third-party sources, the overloading of the “description” field, the classification and categorization requirements and the added complexity in hierarchy management.

As I do more work in the Product MDM area, I’m impressed with Silver Creek Systems and its DataLens solution.

Update on 04/14/09: Silver Creek Systems announced today that its DataLens™ System was named the top Data Quality product by SearchDataManagement.com’s 2008 Products of the Year program. The awards were judged by a team of industry analysts and consultants and presented by the editors of TechTarget’s Enterprise Applications Media Group. For more information, please visit http://www.silvercreeksystems.com/PR_SDMPOY2008/.

9
Apr

New Columns in Information Management

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Usually, when I’ve written a magazine article, I’ll post a brief excerpt here, with a link to the full article. When I moved from the online edition of DM Review (now known as Information Management) to writing a monthly column in the print edition, somehow I forgot to keep doing that.

So here are brief excepts and links to the full articles for the past few months, in case you haven’t already seen them.

Feb. 2009: For years I’ve been recommending that companies investigating or implementing MDM should include business process management in their plans. BPM allows an organization to model, deploy and manage mission-critical processes that span multiple applications, departments and business partners – behind the firewall and over the Internet.

Click on “Business Process Management and MDM” to continue reading.

Mar. 2009: I recently came across a great quote on data quality by Ken Orr in “The Good, the Bad and the Data Quality” from the Cutter Consortium: “Ultimately, poor data quality is like dirt on the windshield. You may be able to drive for a long time with slowly degrading vision, but at some point, you either have to stop and clear the windshield or risk everything.”

Click on “Data Quality and Master Data Management” to continue reading.

Apr. 2009: Third party content is an area that’s close to my heart. I started working intensively with customer and product information more than 20 years ago and was one of the first consultants to integrate Dun & Bradstreet data with Oracle’s applications suite (about seven years ago).

Click on “Filling in the Gaps” to continue reading.

As always, please let me know what you think by commenting here.

7
Apr

Interview in Data Quality Pro

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Data Quality ProTM is a free, independent community resource dedicated to helping data quality professionals take their career or business to the next level. Founded and managed by data quality professionals, its mission is to create the most beneficial data quality resource that is freely available to members around the world. 

Dylan Jones, founder & editor, interviewed me recently, and the interview appears on the Data Quality Pro site today.

Please click here to read the full interview.

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