It’s too early to say definitively how sales for the analytical tool market increased in 2014. Certainly, based on Gartner’s analysis of 2013, (an 8 percent increase from 2012 revenue of $13.3 billion), plus continuing emphasis on new solutions managing Big Data and streaming analytics, it would be very surprising to see this market actually contract. Binary planet over circuit board and surrounded by circles of binary code stream

Regardless, companies are excitedly evaluating and following the evolution of new products for comprehensive analytics. Smart devices and the explosion of the Internet-of-Things have made analytics a business imperative, compelling digital enterprises to find new ways to organize real-time, structured and unstructured actionable data.

Beneath the energetic innovation and sizzle produced by cutting-edge analytics, however, is the nagging, age-old, question: Can you trust the data feeding your new analytic tools? And if you can’t, would it make sense considering “Analytical MDM”? Which, of course, nicely tees-up the question, What is Analytical MDM?

Firstly, let’s assert what Analytical MDM is not. It’s not a holistic data quality platform that also includes resident, business-operational analytics. Yes: it enables IT data management analysis and metrics supporting your understanding of the data quality/MDM life-cycle. No: it does not directly provide analytics for understanding your business activity reporting, or how to improve (for example), order-to-cash processes, or customer 360-type initiatives. But what it does do, is help guarantee the integrity of new (and current) generations of analytical tools, supporting them with good and consistent data.

Gartner’s definition for analytical MDM is very straight forward. Simply put, a single-view of customer, product, location reference data – and hierarchies - is just as important, and as necessary for corporate analytical tools as it is for operational business processes. In other words, MDM’s mission includes providing a single version of truth for business intelligence tools, data warehousing applications and Intelligent Business Operations, thereby substantially improving the analytical processes supporting critical, real-time decision making and compliance reporting.

webMethods OneData MDM has long supported both operational and analytical styles of Master Data Management. Though the two styles offer distinctly different advantages and use cases (and while operational MDM might precede the implementation of analytical MDM), the success of either style is dependent and benefited by very similar MDM functionality and system architecture.

Because the end result or outcome of operational and analytical MDM is so different, one is initially inclined to believe that both styles should be supported by two distinctly, different MDM technologies. But this perspective overlooks the essential collaborative nature of MDM. Indeed, OneData’s deep level of collaborative functionality enables data stewards, system administrators and business users internally to join as one team to manage compliance and implement prescribed data management rules that support data entry, data creation, data cleansing and enrichment, or data governance in general.

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