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Big data intelligence increasingly a business, governance priority

In this Q&A, Jeffrey Ritter discusses how the quest for big data intelligence is forcing governance professionals to move beyond GRC gatekeeping.

At large organizations, a major part of information governance professionals' responsibilities has been ensuring enterprise data adheres to increasingly complex regulatory compliance rules. The role of governance has expanded in recent years, however, as enterprise leaders have started to realize that the vast amounts of information generated and stored by companies provides a wealth of readily available business data intelligence. Information governance professionals are increasingly asked to tap into big data analytics to determine new ways to create wealth for the company, while still finding time to maintain legal and regulatory compliance.

Frequent SearchCompliance contributor Jeffrey Ritter, an attorney and external lecturer at the University of Oxford, recently discussed how information governance professionals are being asked to go beyond maintaining data compliance with public laws and regulations. In this Q&A, Ritter explains how corporate boardrooms' increased appetite in for big data intelligence and analytics investments has influenced this change, and how governance professionals can adapt.

How are corporate demands on information governance shifting with the entry of new big data products and services?

Jeffrey Ritter: Successful big data intelligence requires historic corporate information that can be ingested into analytics engines. First, the inbound content must meet the rules for what type of data can be analyzed. When the inbound data does not meet the rules, the investments in big data are diluted because less data can be analyzed. As a result, information governance professionals are being assigned the responsibility to do more than assure good-faith compliance with public regulations. Information governance now must also assure the entire portfolio of corporate records can be validated against the rules that filter those records that can be used for big data analytics.

What do those big data rules require?

Ritter: It is critical that ingested data conform to structural rules such as the relevant information classifications and structural schemes used on databases. But many big data analytics engines do their work best when they are receiving and crunching data from lots of different sources. The engines really need to know where information has come from and how has that information been maintained.

Those rules also emphasize the provenance of the data. The information governance team must be engineering compliance with those rules at the front end of any IT project. If they don't, the resulting output data may not be useful to the wealth creation analytics that big data exists to produce.

Can you give us some examples of new data repositories that an information governance team must embrace?

Ritter: The 21st century marked the end of the structured record. Invoices, purchase orders, shipping notices, commercial agreements -- all of these traditional formats for business information assets are being de-constructed into vast data lakes and master data collections that allow data to be assembled and used in multiple constructions.

Strong information governance can become an enormous accelerator to the financial returns on investment that big data intelligence promises to deliver.

This makes information governance very difficult. Data streams, graph data, execution logs of application loads within Linux-based systems, validation logs for federated identity management systems -- none of these are viewed as traditional "records," but they are vital to the potential next chapters in leveraging big data for business gain. The best business data intelligence is produced from analyzing lots of small records -- and that's where the challenges are.

Information governance is still viewed by corporate leaders, and often information governance professionals themselves, as focusing on the primary content records required by compliance. But the more difficult proposition will be handling the very high volumes of these new types of data.

Many company's data management programs are already struggling to get the funding required to meet their primary compliance obligations. How can information governance managers secure the added funding required for these new challenges?

Ritter: There is a reason big data analytics and the business intelligence marketplaces are growing enormously. The resulting output is powerful in helping organizations create new wealth, and allowing greater velocity in their business decisions. When information governance can connect their positive role in engineering IT systems to allow data to be leveraged in big data analytics processes more efficiently, it can legitimately claim it is helping create new wealth and improve business velocity.

In fact, strong information governance can become an enormous accelerator to the financial returns on investment that big data intelligence promises to deliver. When the engineering process fully embraces information governance professionals' contributions to the design of both the content and the provenance data, data management becomes a proactive business function far more important than one that merely preserves the primary content records required to adhere to public laws and regulations.

Next Steps

Read more from Jeffrey Ritter on information governance strategy, including how to maintain information's value in the digital age and why information governance processes are shifting as legal and compliance rules are more heavily reliant on digital evidence as truth.

This was last published in May 2016

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