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.

Machine learning saves us from the salt mines

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 in taxonomy for GDPR. The ML technology will automatically 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.”

Kochar echos this positive, proactive tone. In fact, companies are suppressing their entire digital transformation mission by ignoring the data classification mess in the corner. “Clients are not able to move on their transformation journey because they don’t have data classification done right. And you can’t put humans to it. You’re going to need the technology, you’re going to need the machine learning algorithms and the AI built into your software to get that,” Kochar said. 

Data democracy gives power to the people

One thing you can put humans to is the task of creating business value from properly governed, democratized data. The policies and cultures within organizations are shifting so that business people no longer want to have to beg IT for fresh data insights, Kochar pointed out.

“Recently, I was talking with these banks, where they’re like, ‘Can you come and talk to our CFOs?'” Kochar stated. The new outlook emerging is that “maybe the data needs to be owned by businesses, no longer an IT thing,” she added.

“Governance and integration, I feel like, is a glue which is helping us drive that cultural change in the organizations, bringing IT and the business groups together to further drive the insights,” Kochar explained.

Do business people have the chops to really understand data and extract insights from it? Aside from automation, natural language processing is another trick that can bring the IQ requirement to mere-mortal level. “If you’re looking for something, you don’t have to always know what your SQL is going to be for a query to do it; you just type in, ‘Hey, I’m looking for some customer retention data,'” Kochar said. This Google-for-data model enables easy self-service, she added. 

So what’s coming in governance 3.0, so to speak? Cutting out even more human-intensive work and getting smarter about metadata are on IBM’s agenda. “Think about having a universe where you don’t have to sit there and connect the dots, and say, ‘I want to move from here to there.’ The system already knows it. It understands your behavior. It knows what your application is going to do and kind of automatically does it for you,” Kochar said. “No more science fic — I think we can make it happen.”

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the IBM Signature Moment — Machine Learning Everywhere event. (* Disclosure: TheCUBE is a paid media partner for the IBM Signature Moment Machine Learning Everywhere event. Neither IBM, the event sponsor, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE