The MDM & data governance market 2019-20: Spotlighting the key trends

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As the “godfather of the MDM market,” and a regular sanguine commentator on the market’s solutions providers, my analyst career has often had to “call out” the hypsters on numerous occasions.

As recent feedback from MDM & Data Governance Summit attendees stated, “Zornes expresses himself in his MDM field reports with a forcefulness that makes even the most hostile restaurant review seem the model of restraint.”

The humorous among the audiences at my summits (14 years and still growing) know how true it is when I say “Vendors, please check your weapons at the door” when they attend my “Field Report” sessions that review the pros and cons of the Top 20 solutions in MDM (RDM, data governance, etc.).

Every company today is in the “data business” as businesses continue to collect volumes of information about their customers. When organized effectively – by integrating data from variety of different sources through MDM – this data can provide important and actionable insights.

Some enterprises are tackling this challenge with traditional approach to MDM, which limits what can be done and how quickly they can do it. Coming to an MDM hub near you soon, are two key enabling technologies that both augment and/or re-invent MDM as we know it ... “graph” and “machine learning” (ML).

With that perspective in mind, let’s unpack two trends that will provide good clean entertainment during 2019-20:

1. Graph database is conceptually threatening MDM due to its ability to simplify complexity, but is also augmenting MDM & DG via UI and Query.

  • Simpler modeling of complex relationships yields more humanistic user Interface for all concerned ... This model agility and extensibility enables users to easily and quickly add new data dimensions, hierarchies and linkages.
  • Querying of analytics via graph tech also simplifies and turbo-charges the ability to query (and discover) data relationships critical to “system of engagement” style systems.
  • In practice quite often, the majority of purist graph-specific implementations are either (a) unable to transact for high volume scalability in operational mode or (b) primarily being used as adjuncts to operational mode/transactional MDM hubs to cross-walk/analyze across domains.
  • Near term, graph will continue to enhance the delivery of data mastering.
  • Start-ups and others that are not mega vendors are more capable of introducing and leveraging such capabilities ... see Reltio and Semarchy for data modeling and querying (as well as other UI aspects).
  • As MDM evolves towards “master relationship management” via graph technology, analytical upstarts from the graph world will increasingly add operational capabilities for performance and robustness.

2. Machine Learning is stealing the spotlight at the MDM & DG party.

  • Scalability, complexity and agility are only certain of the problems increasingly being solved by machine learning (ML).
  • Start-ups and others that are not mega vendors are more capable of introducing and leveraging such capabilities ... see Tamr for scalable metadata mastering, Fresh Gravity for GDPR expert system guidance for enterprise-strength anonymization, etc.

Back Story (more on ML for MDM):

Traditional MDM has been around since the early 2000’s. As data volume has grown and the potential value of analytics has exploded, enterprises seeking to compete on analytics struggle to scale mastering efforts with the surfeit of available data sources.

Clearly, creating robust data engineering pipelines to unify this data at scale is more important -- and harder -- than ever. An “agile” approach, utilizing machine learning (ML), can cut time required for unification/ analytics projects (~90%) while scaling to more sources than other traditional approaches. Moreover, given the scale of enterprise data, automation is key to agility and scale. Such enterprise data automation can only be achieved with some human oversight to make sure the results are fast and accurate.

Not just raw data scalability, but also human process scalability is enabled by ML. While we all know to invest in active/integrated data governance for long-term sustainability and ROI of MDM, most all of the currently-marketed classic DG tools do not exist as integrated solutions and also are lagging in “ML-guided” stewardship.

As in many human endeavors over the next several years, expert systems (self-learning, etc.) will work side-by-side with human experts to facilitate, advise, correct and promote best practices in data governance/stewardship, and more data-related tasks for IT pros.

While we plan for most MDM vendors to deliver classical DG next 6-18 months, concurrently we project that innovative best-of-breed DG s/w to focus on ML as competitive differentiators. Specifically, mega vendors (IBM, INFA, ORCL, SAP) are focused on delivering DG capability in 2019-20, with resultant partner chaos – with every solution provider turning increasingly to ML acquisitions and partnerships to bolster their governance capabilities.

Clearly, ML will augment (more than replace) MDM+DG to provide increased agility and scalability. Areas where ML will be applied include: data discovery and mapping, entity resolution, relationship discovery and mapping, taxonomy and ontology; and governance and stewardship.

Bottom Line?

Plan now to realize economic value and competitive differentiation via MDM+graph+ML during next two to five years. Clearly the “astrological alignment” is happening as we speak.

p.s. “Application Data Management” as another MDM-related Gartner Magic Quadrant in the making? Given that it took Gartner 12 years to blend CDI and PIM into multi-domain MDM magic quadrant, the MQ marketing pattern remains the same. It is lagging the market rather than leading the market, in other words.

Meanwhile, it is safe to say that any data management professional understands that there has always been the dynamic tension between MDM-centric apps vs. application-ready MDM – sometimes characterized as model-driven vs. model-agnostic MDM (not quite the same analogy).

IBM, Oracle and SAP had tremendous success in the market with their MDM hubs based on their data models (IBM Banking Data Warehouse/Insurance Application Architecture, Oracle Siebel UCM and Oracle eBusiness Suite, and SAP MM, FI, et al.) In the current stage of MDM evolution, IBM (Patient Hub), Informatica (Product/Supplier/Customer 360), Reltio (Reference 360), et al are adding MDM-centric apps as systems software vendors move up the food chain to become app vendors... while app vendors enable and leverage their own MDM (e.g., Oracle’s SalesCloud, ServiceCloud and Marketing Cloud based on Cloud MDM, SAP MDG for Retail/Fashion, et al).

Does this mean we need another Magic Quadrant? To be safe, certain sycophant solution providers area already aping the “ADM” marketing term. Not us...

Summary Thoughts

Those attending my industry analyst keynotes this year will get to hear plenary research about strategic planning assumptions such as:

  • Determining what your organization should focus on in 2019-20 to initiate “master relationship management” (system of engagement vs. system of record)
  • Planning to leverage reference data and big data as part of an enterprise MDM program
  • Understanding where MDM, RDM and Data Governance are headed in the next 3-5 years

See you at the next MDM & Data Governance Summit in NYC, November 3-5?

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