How AI can make GDPR compliance sexy | 7wData
The terms data governance and compliance usually excite businesses about as much as tax season excites wage earners. But artificial intelligence software could change that by juicing governance with analytics and insight capabilities. Best of all, properly governed data is democratized data, which puts the power of analytics in regular business people’s hands.
“I call it governance 2.0 or governance for insights, because that’s what it needs to be all about,” said Madhu Kochar (pictured), vice president of analytics product development and client success at IBM Corp.
The whole thrust of enterprises today is toward digital transformation with data analytics at the core. Creeping regulatory headaches like General Data Protection Regulation, which will go into effect for any business with European operations in May 2018, threaten to stamp the speed break, however. Can new intelligent governance technology help users sail through regulatory checkpoints and amp up their analytics game in one throw?
Kochar spoke with Dave Vellante (@dvellante) and John Walls (@JohnWalls21), co-hosts of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the IBM Signature Moment — Machine Learning Everywhere event in New York. They discussed how AI and Machine Learning can remake data governance software to enable information technology and business people. (* Disclosure below.)
This week theCUBE spotlights Madhu Kochar in our Women in Tech feature.
Manual drudgery has slowed down data analytics and compliance for years, according to Kochar. The biggest hope for bringing them up to the real-time pace of digital business is automating away as many steps as possible with machine learning.
“In our governance portfolio in IBM Information Server, we have infused machine learning in there,” she said. Instead of ordering an army of workers to sit around and assign business terms to the data, machine learning algorithms and models can understand and classify it. For one thing, this can guard against potentially bone-breaking GDPR slips, Kochar pointed out. Users can feed intaxonomy for GDPR. The ML technology willautomatically tag data in the catalog as personal data, sensitive data or data needed for certain types of compliance.
“Embedding ML into … a packaged software, which gets delivered to our client, people don’t understand actually how powerful that is, because your data, your catalog is continuously learning from the system itself, from the data itself,” Kochar said, adding that this can cut costs significantly.
GDPR will descend on enterprises officially May 25. The word on the street is that preparedness among businesses isn’t what it should be. One study put the percentage of GDPR-ready companies at an abysmal six percent.
Perhaps a gander at the legislation’s silver lining would thaw their fears and get them moving to compliance.
“A panicked response to GDPR, which focuses almost exclusively on data protection and security requirements, distorts an organization’s data and analytics program and strategy,” said Lydia Clougherty Jones, research director at Gartner Inc. “Don’t lose sight of the fact that implementing GDPR consent requirements is an opportunity for an organization to acquire flexible rights to use and share data while maximizing business value.”