Master Data Management Philosophy
Last week I did a presentation that included a slide on my philosophy for MDM so I decided to expand that slide into a blog post. While this is my philosophy, I think it comes pretty close to the way the rest of the MDM product team looks at MDM. As always, I welcome any comments and feedback.
• Multi-domain hub – while there are definite advantages to specialized MDM applications that handle data quality, match-merge, and standardization for a particular type of data, once you have cleaned up your incoming data the processes to maintain the data are common across all domains. There are definite advantages to a single point of management and single set of tools and processes for managing master data. Some vendors approach cross domain master data management by implementing relationships that span domains stored in different repositories but this often means that different data must be managed in different ways by data stewards. I think there’s a real advantage to maintaining all master data in the same hub because the same processes and techniques can be applied to all types of master data. This means a hub with enough flexibility in the data model to allow any master data domain to be modeled and managed.
• Open interfaces – there are so many kinds of master data that no single vendor can provide a toolset that spans all domains. That’s why it’s important for an MDM hub to have open interfaces for domain specific tools to plug into. If your hub vendor provides the data management and data stewardship facilities you’re looking for but doesn’t data import and data quality tools that specialize in the kind of data you want to store, you can find best-of breed tools (or write your own) that interface to the MDM hub through the open interfaces provided. In these days of SOA, web services interfaces are probably the most useful.
• Incremental implementation – while a single source for all your organization’s master data is the goal of Master Data Management, not many organizations have the resources and patience to consolidate all their data in a single project. For most companies, starting with a single domain and a subset of data sources to learn how MDM works and demonstrate early success is a much better approach than a “big science” MDM project.
• Partner for domain specific solutions – as I said earlier when discussing open interfaces, it’s not realistic for a general-purpose MDM product to handle all the possible variations of Master Data domains. For this reason, we plan to cultivate a rich partner ecosystem to provide the expertise in the specialized domains we don’t have the resources to develop ourselves.
• Use existing integration capabilities – before we started the MDM product team, I spent about a year researching how to build MDM systems with existing Microsoft technologies. What I found was the Microsoft has a wealth of data integration technologies – SSIS, BizTalk, FRx, etc. so when we looked for an MDM product to buy we looked first for a product that excelled at managing data, data stewardship, business rules, workflow, etc. with the assumption that our current data integration capabilities would handle the rest. By using data integration, transformation, standardization, orchestration and profiling capabilities that other teams continue to develop we can have world-class capabilities in this area while we concentrate our resources on core Master Data Management capabilities.
• Tight integration with Microsoft Products – when we started talking about MDM around Microsoft, we found that quite a few Microsoft products had requirements that an MDM product could meet. Developing tight integrations with Microsoft products will not only be a great benefit to our customers but will give us a chance to “dogfood” the integration interfaces we plan to ship with the product.
• Hierarchy Management a critical capability – the structure of master data is often as important as the data itself. While this is obvious if you are using MDM to manage your chart of accounts, organization structures and product hierarchies are also critically important data. Stratature has some unique capabilities in hierarchy management that are proving very useful to MDM users.
• Data Stewardship is a key success factor – MDM systems make data quality and accuracy more important than ever because a mistake in master data can cause issues in all the systems that consume the data. While automated match-merge, standardization and data quality tools are becoming more capable all the time, at some point real human beings who are passionate about the data are required to make decisions that tools can make and monitor the processes to ensure that business rules and data standards are being enforced correctly. This is just one more example of the people aspects of MDM being as important as the technology. While processes, policies, governance, and standards are the real success factors, a good MDM hub can provide tools and capabilities that make a data steward’s job easier. Some of the more useful capabilities are business rule enforcement, workflow, versioning, searching, auditing, and eventing. The combination of the Microsoft MDM system and SharePoint supplies all of these capabilities and more.
• Analytical and Operational MDM just two uses for the same data – I did a whole blog post on this a couple months ago so I won’t spend a lot of time on it here but I wanted to point out that all the data management, stewardship, and quality capabilities I have been talking about so far apply equally whether you are using you master data to build cubes in a warehouse or to provide clean data to your operational systems. I don’t think an MDM system that doesn’t support both operational and analytical uses for master data is a good investment. Many companies start by using their master data for analytical purposes because it is generally easier to implement and shows positive results faster but investing the time, money and effort to create a clean source of master data without eventually using it to improve your operational systems can be a significant missed opportunity. Doing an analytical project to learn the technology and develop the policies and processes necessary to manage master data followed by another project to use the high quality master data obtained to improve operations is often a winning approach to MDM.