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Merge big data governance with data validation to create new revenue

In the final part of information governance expert Jeffrey Ritter's big data mining webcast with SearchCompliance.com, Ritter talks about the importance of data validation and rules -- two important factors Ritter said companies need to make the "business case" for big data governance.

"Information governance makes the business case by front-end loading the rules process and the governance to allow the downstream resources to be more effective at actually doing their job and fitting the data together to create the intelligence," Ritter said.

According to Ritter, when the rules for validation are created upfront, it allows downstream resources to be used more efficiently because they are no longer preoccupied with the data validation process of big data governance.

"If the rules are written properly and have been used correctly in the engineering process to design provenance data that stays with and connects to the primary content, I simply don't have to employ the resources downstream to validate the quality and the integrity of the data," Ritter said.

Once the, "data about the data" is deemed as trustworthy as the data provenance through the use of proper and efficient validation, it can be used to improve compliance and fuel the analytics engines that are used to create business intelligence, according to Ritter.

As an example of how big data governance combines with data validation and business intelligence to generate revenue and improve the consumer experience, Ritter gave another metaphor -- this time describing a mother shopping with her child in the supermarket.

Information governance makes the business case by front-end loading the rules process and the governance to allow the downstream resources to be more effective at actually doing their job.
Jeffrey Ritter

"What information is now being captured and used to identify the priorities and preferences in the product that is on the shelves? We know that the shelf assortment is a function of buying habits, it's a function of physical-placement; people tend to buy from the higher shelves versus the lower," Ritter said.

Ritter referenced research by Doug Laney, a vice president and analyst at the information technology research and advisory firm Gartner, Inc. Laney studies infonomics, the economic and financial significance of information.

Laney and others at Gartner look at how companies use big data governance to create new revenue streams from their data, Ritter said.

Ritter gave an example involving a "big-box" home goods retailer. He said that one of the retailer's hottest products is lawnmowers, which sell quite well in the northern United States. The lawnmower manufacturer is also a manufacturer of snow blowers.

"Well, big data properly governed, allows us to see the patterns in terms of the success or lack of success in the brand acquiring a presence in the garages of the consumers at that big-box retailer both for the lawnmowers and for the snow blowers. Pricing, marketing [and] relationships with consumers can change based on how we use that information," Ritter said.

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What are some other examples of ways that businesses can capitalize on efficient data validation?
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