Managing Data GovernanceIn the first of a series of three blogs, we offer a primer for deploying process-driven data governance, which is rapidly becoming critical to maintaining competitiveness.

Data is all around us; it comes from internal processes and external sources and streams through our enterprises like a spring flood, overflowing our databases. New data flows in from increasingly complex and disparate computing environments and the device-driven Internet of Things (IoT); seismic data center shifts result from mergers and acquisitions.

All of this reinforces the business-critical need to create, standardize, govern and enrich good, consistent, sharable enterprise-wide data – or data governance.

It sounds simple. It is, in fact, extremely complex. The Data Governance Institute describes the process of data governance with an old adage: “You can’t manage what you don’t name. And then there’s its corollary: You can’t manage well what you don’t define explicitly.”

Once a company recognizes and acknowledges the potential— and sometimes process-crippling downside— of poor data quality, it is more apt to embrace some kind of data governance program. But how do you get started?

When you attend a data governance seminar or conference, you will often find a vendor exhibition hall where you can grab a coffee and check out the latest technologies available for executing data governance procedures.

There you will likely find tools including data modelers, data cleansing and standardizing engines, governance workflow solutions and reference data management software among other things. One of the most important tools you will see is a Master Data Management (MDM) system, which provides a centralized and holistic approach to the creation, governance and deployment of shared enterprise data.

A versatile, multi-domain MDM solution is often referred to as a data governance tool. In actuality, however, MDM should be considered a data governance platform because of its ability to combine and orchestrate so many of the capabilities in our exhibition hall, which are essential, data-quality components.

But, with so many moving parts and integrated functionality, how do you control the process of MDM? First, the challenges.

Typical challenges to successful MDM implementations include:

  • An overly, data centric approach that underestimates process complexity and its business relationship
  • Unclear project scope due to requirements not being clearly defined
  • Viewing MDM projects only as an implementation process of the actual tool(s)
  • Unclear roles and responsibilities related to master data

Your business has to pinpoint how good quality, consistent data will improve process performance, help enable major initiatives by removing bad data bottlenecks and increase the bottom line. In turn, IT is tasked to find and evaluate the best technology platform for the job.

In our next blog we will discuss how the implementation of MDM must be tied to three pillars of success and how an MDM solution can be implemented as part of the technology process.

For more information please download our free white paper: A Primer for Process-Driven Data Governance.

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