When MDM Becomes "Plan B"
There are more constituents, more lines of business, and more business uses for customer data than ever before. But as quickly as solutions for data movement are introduced, problems with the data itself becomes more visible: two (or more) data fields that mean the same thing nevertheless look different and are interpreted very differently. This is true for established data types like Social Security Number that imply strict formatting rules, but also for data types like Address where the rules might be less rigorous. Ironically, the trend of master data management (MDM)the automation of data reconciliation across and between systems for various types of reference datawas greeted heartily by business and IT executives who were still fundamentally change-averse. Yet, many didnt know what to do or where to start.
MDM is not a new solution to an old problem, but rather a new solution to a new problem. Simply put, its a paradigm shift. The reasons for this are fundamental to what MDM is. The true value of any MDM solution is its ability to acquire, asynchronously or in real-time, enterprise data from heterogeneous systems and online stores where that data originates. Whereas many vendors rushed to hang the MDM shingle, or re-craft their marketing messages to retrofit their products into the MDM rubric, the best MDM solutions are demand-driven: that is they match and correct data as its used and, not to put too fine a point on it, they dont touch the data if its not being used.
As data is used for different purposes in different ways by different constituencies it erodes to the point where it can become generally regarded as untrustworthy. Even direct-source system reports, usually considered more reliable, are difficult to interpret. And, as you add more source systems, ETL programmers need to assess the impact to their expanding collection of ETL jobs and modify them. Point-to-point code that worked before stops working. This becomes even more unwieldy when you begin to consider subscribing to third-party data.
Bad Data, Bad Business
A sad turning point happens when mistrust of the data becomes part of the companys culture. Following the inevitable pressure from business colleagues, CIOs eventually come around to the idea of going to Plan B and fixing the data once and for all. CIOs generally try to resist Plan Bs because they almost always involve disruptive technology. So, the search begins for a solution that needs to be flexible, authoritative and permanent. Yet because existing systems and users still needed data from the companys data warehouse, CRM and EII environments, any solution needs to cause minimal system or business disruption.
Mindful of these requirements, the CIOs take a hard look at MDM for a single point of reference for data matching, integration and two-way propagation. A new MDM hub prevents one off, custom solutions for every new system linkage. It also eases the burden of having to propagate data from new sources and support different integration rules within and between operational systems.
With Plan B, the MDM hub assumes the role of a data reference system. Systems in need of data call the MDM hub for the reference record, and then retrieve the actual data. In tandem, you have the continued propagation of data to and from different systems. In this way, companies finally have access to authoritative versions of data for a range of applications while avoiding drastic, disruptive change.Because it works so well, the MDM hub becomes affectionately known as the master chef for enterprise data. It ensures that all systems and users have standard ingredientsand standard mixing instructions. If the ingredients changesay, a source system redefined its dataother ingredients could be affected, and the chef regulates the entire recipe. The MDM teams unofficial but oft-quoted motto becomes: Never cook with rotten eggs.
MDM applies consistent business rules to data that ensures its reliability. That reliability is informed by the datas ability to reflect real-world facts. To be truly useful across business processes, technologies and decisions, data changes when real-world facts change, with as little latency as possible.
The need to retrofit existing processes to new tools confronts an historical resistance to change. But MDM isnt a substitute for these technologies, each of which addresses a very specific set of needs. Rather, MDM is nothing less than a disruptive innovation that solves new problems with a new solution. But that doesnt mean your existing solutions arent still important. Indeed, MDM can enhance their performance, their accuracy and their overall business value.
As companies increasingly recognize MDMs promise as a solution to their most urgent business problems, research leads them to a range of vendor solutions. As you do your own research, you should first understand your companys unique requirements for accurate, meaningful master data. Then, consider those requirements as you evaluate your MDM options.
MDM is at its most effective when linking multiple systems for the purposes of once-and-done data reconciliation. You not only will be able to capitalize on MDMs economies of scale each time systems are added or changed, but reap the benefits of data consistency, validity and accuracy. Then, when you get asked about your "Plan B", youll give it an A+.
Jill Dyché is partner and co-founder of Baseline Consulting. She is responsible for key client work and industry analysis in the areas of data governance, business intelligence, and master data management. Jills work has been featured in major publications such as Computerworld, the Wall Street Journal, and Newsweek.com. She is a regular columnist for Information Management magazine, an Ask the Expert contributor for SearchDataManagement.com and SearchCRM.com, and a blogger for B-Eye-Network. She can be reached at firstname.lastname@example.org.