APRIL TOP READER PICK The key elements of successful data governance programs

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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.

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. Poor data quality can cost organizations millions. This internal tax on data reduces everyone’s confidence in the data, costs time and money to mitigate, and slows insight development and as a result, slows down decision making.

Further new regulations, such as the European Union’s GDPR and now California’s version, the California Consumer Privacy Act (AB 375), which goes into effect in 2020, are raising the data governance bar for everyone.

So, no one should be resting on their laurels.

IM: What are the key elements of an effective data governance program?

Eve: Getting serious about data governance is like getting serious about any critical business initiative. Start with clear goals and KPIs that measure progress towards them. Assign responsibilities to specific parties and hold them accountable. Use proven change management techniques to ensure effective implementation.

If your organization remembers the three key points outlined above when implementing data governance, good outcomes will follow.

IM: For those organizations that struggle to get data governance right, where are they falling short?

Eve: There are three main reasons organizations struggle with data governance.

The first goes back to philosophy. Are you all about control or freedom or some intelligent mix of the two? Commit to something and stick with it.

The second reason relates to the absence of needed “Management 101” disciplines such as goals, KPIs, accountability, and change management described above. Without this foundation, nothing significant can be achieved.

The third reason is failure to take advantage of modern data governance technologies. Multi-domain Master and Reference Data Management is fundamental. Data Virtualization is a great way to implement governed data access.

Adding to this list would be self-service visualization tools that allow business users to interact with the data in ways that can expose the business impact of data issues and thus increase business’s willingness to co-invest with IT in mitigating them.

IM: For those organizations that do a good job with data governance, what sets them apart?

Eve: In addition to doing a better job addressing the three items above, the leading organizations smartly embed data governance improvement efforts within larger, well-funded, must-have business initiatives.

For example, many firms were prompted by GDPR to strengthen one or more data governance aspects and will now use the California Data Protection law to again raise the bar. Other firms are mobilized by security breaches, or the fear of them, to fortify data security and other selective data controls. Other firms such as the insurer above, have leveraged their business’ insatiable demand for data to fund governed data access.

Every major digital transformation initiative is driven by data needs, so embed selective data governance improvements within them.

IM: What is your advice to IT and data professionals on how they can boost the effectiveness of their data governance programs?

Eve: The wisest thing that IT and data professionals can do to raise their data governance effectiveness is to increase collaboration with their business colleagues.

When they do, their conversations about data governance issues will become far more productive. They will align better on data governance goals and strategies that balance control versus freedom and properly consider risk. They will make smarter choices about data governance policies, processes, and enabling data governance technology. And most of all, their shared vision and combined efforts will drive higher-impact, data-driven business outcomes.

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