For a growing number of organizations, master data is required to provide competitive advantage, increase customer service levels, and drive new products or service offerings. In response, master data management must embrace big data analytics in order to deliver such capabilities, as well as cloud-enablement, deployment and integration.

Unfortunately, across both the private and public sectors, many organizations still struggle to provide a single view of the “truth.” This too often holds true whether for "party" (customer, citizen, supplier, etc.) or "thing" (product, location, measurements, etc.).

Data governance is critical to achieving sustainable and effective MDM.  Failure to execute data governance concurrently with an MDM program will greatly decrease the probability of success and economic sustainability of MDM programs.

MDM is no longer a "fast follower" technology, but is now a mature solution providing tangible benefits for organizations of all types. For organizations focused on MDM, the goals are new ways to drive down costs; to enable better regulatory compliance; to provide higher levels of customer satisfaction; and to provide increased agility -- whether to add new channels or products, or to prepare for and execute on mergers and acquisitions.

The thirst for knowledge on MDM is also clear. At our 11th annual San Francisco MDM & Data Governance Summit (June 8-10), we predict increased attendance as organizations across all industries and geographies grapple with fundamental CDI and PIM requirements (which still drive the majority of MDM procurements).  Yet clearly, big data, reference data, cloud deployment and architectures, and social MDM also continue to push the envelope. 

Moreover, we are experiencing an ongoing battle of intrinsic MDM and data governance programs (that will help enterprises compete, market, grow and regulate their business) versus the oncoming wave of IoT , e-commerce, and social networking related big data analytic requirements (that will help gestate the next generation of digital-driven industries).  

Not surprisingly, there is real angst or conflict in the ranks of data and solution architects as to where to prioritize their company’s priorities, as well as their own career focus. 

These worlds will continue to collide, yet innovative and successful organizations will continue to mix pragmatic MDM (onboarding customers, suppliers, and products) with next-gen sources and targets (such as mobile commerce and IoT). 

The bottom line is that the more agile enterprises and individuals will have minimal conflict between mixing intrinsic MDM capabilities with next-gen MDM requirements. 


2016-17 “MDM & Data Governance Road Map”  

Part of the deliverables for the MDM Institute’s customer advisory council is an annual set of milestones to serve as a "road map" to help Global 5000 enterprises focus efforts for their own MDM programs. 

For planning purposes, we thus annually identify the top milestones which we then explore, refine and publish via our MDM Alert research newsletter. This set of "strategic planning assumptions" presents an experience-based view of the key trends and issues facing IT organizations by highlighting:

  1. Next-Gen MDM (master relationship management)
  2. Pervasive MDM-enabled cognitive apps (Master data as a service)
  3. Data governance
  4. Cloud MDM (Cloud-enablement, architecture and integration)
  5. Reference data management (RDM)
  6. Business-critical MDM
  7. Social and mobile MDM
  8. Big data (and ‘in memory’)
  9. Business process hubs
  10. Budgets and skills

 
Next-gen MDM (master relationship management)

During 2016 MDM evaluation teams will assume (and insist) that all MDM software platforms targeted for enterprise-level deployment or major role in mission-critical systems fully support both ‘party’ and ‘thing’ entity types. This dogma remains known as Universal MDM or Multi-Domain MDM.

Through 2016 and 2017, mega vendors will continue to deploy separate CDI and PIM stacks while nouveau MDM vendors attempt to position this as a "legacy MDM" failing. MDM-ready data models will continue to bolster mega vendor MDM sales, especially for IBM, Oracle and SAP.

Given that the majority of operational CDI hub vendors have added "PIM light" capabilities, and concurrently that most PIM vendors have added B2C PARTY entity domain, the degree of out-of-the-box support for reference data remains a key differentiation. This lack of lightweight system footprint, combined with inflexible pricing by mega vendors, is resulting in “MDM duology” for many enterprises. That is, wherein mega vendor (IBM, INFA, Oracle, SAP) MDM hub deployed for customer, product, or supplier with lesser priced 2nd or 3rd tier MDM solution for "everything else" (RDM, location, et al).

By 2017 to 2018, graph database technology will increasingly provide a “hub of hubs” MDM layer to rationalize complex relationships across and within domains to provide “master relationship management” modeling and analytics. This next-gen MDM capability will enable the cognitive data-driven apps that in turn will drive the next generation of business e-transformation and industrial re-tooling.

 

Pervasive MDM-enabled cognitive apps (Master data as a service)

During 2016, mega MDM vendors will increasingly build and market data-driven applications that compete directly with mega ERP and vertical industry app providers.

Through 2016 and 2017, SaaS vendors will struggle to provide integrated and native MDM; and select SaaS providers will finesse this issue via strategic partnerships and investments in MDM. Graph DB technology is one area that all vendors are focused on in to support the need for managing and analyzing increasingly complex relationships and hierarchies. That, in turn, will enable data-driven cognitive apps of all sizes and shapes.

By 2018, the market for data-driven cognitive (MDM-innate) applications will exceed that for MDM platform software.

 

Data Governance

Through 2016, most enterprises will struggle with enterprise DG while they initially focus on their customers, vendors, or products. Integrated enterprise-strength DG that includes E2E data lifecycle will remain elusive, as most organizations turn to lightweight glossaries with modest data steward workflows to support devolved autonomy and multi-disciplinary, bi-modal teams.

During 2016 and 2017 the majority of MDM software and service providers will focus on productizing such lightweight DG frameworks while mega MDM software providers struggle to link governance process with process and data hub technologies.

Finally, by 2018, mega vendor DG solutions will finally move from “passive-aggressive" mode to “proactive" data governance mode.

(In part two: Predictions for the cloud; reference data management; business-critical MDM; skill needs; and more)

(About the author: Aaron Zornes is the chief research officer at The MDM Institute, the world's leading research and advisory consultancy exclusively focused on master data management. The MDM Institute provides authoritative, independent and relevant consulting advice to senior IT leaders in corporations and government agencies, to business leaders in high-tech enterprises and professional services firms, and to technology investors.  For more information, visit www.the-mdm-institute.com.)

 

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