The key elements of successful data governance programs

The growing adoption of advanced and disruptive technologies, along with greater focus on data value and insights, is putting the spotlight on successful data governance strategies.

To better understand just what that means, Information Management spoke with Bob Eve, senior director of data management thought leadership at TIBCO Software, about what his company is seeing among leading customers when it comes to data governance best practices.

Information Management: With all of the so-called disruptive technologies that are grabbing headlines (such as artificial intelligence, machine learning and automation), more attention is being paid to data governance. Exactly what are we talking about when we discuss data governance?

Bob Eve: Everyone seems to agree that data is a highly valued asset because it fuels compelling customer engagement, optimized operations, breakthrough digital products, and more. Like any important assets it should be managed with care. In summary, data governance is the collective set of policies, processes, and technology that control data across the enterprise and throughout its lifecycle.

IM: What is your take on the state of data governance efforts today?

Eve: Today, data governance is a battleground between two philosophies.

On one side there are those who strongly believe in a control-centric approach to data governance. With sensitive private data such as credit cards, health records, and more, as well as in regulated industries, such as financial services and healthcare, this approach seems valid. However, with an increasing amount of new data, distributed across so many locations, it is hard for organizations to “lock down” everything. As a result, organizations are increasingly directing this control philosophy to their most important and most often used shared data assets such as master and reference data.

On the other side are those who expose greater data freedom in order to put as much data as possible into the hands of their business analysts, partners, and even customers in hopes of unleashing innovation and digital transformation at scale. One of our customers, one of the world’s largest multi-line insurers, is an advocate of this philosophy.

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Both sides present valid arguments. Like Goldilocks and the Three Bears, identifying what is “just right” is often an iterative process that may involve a bit of “too hot” and “too cold” along the way.

IM: What impact are the many new technologies/trends having on data governance efforts?

Eve: With the rise in demand for widespread data access, the advent of device data, and the growth in cloud data, organizations that might have done a good job governing their centralized data warehouse and transactions systems are struggling to do the same across new and distributed data sources.

With the proliferation of self-service data preparation and visualization tools on the business side, IT is having to stretch its data governance methods and tools to support new users and use cases.

Fortunately, new technologies like artificial intelligence and machine learning are functioning as a data governance enabler by simplifying difficult governance tasks such as discovering and relating metadata, as well as profiling, cleansing, linking, and semantically reconciling the data itself.

IM: Do most organizations do a good, fair or poor job with data governance?

Eve: It’s a mixed bag of good, fair and poor. However, every organization can improve their approach to data governance. There are myriad opportunities organizations can pursue to improve. Take data quality for instance.