Late adopters to big data, analytics, cloud and artificial intelligence may be losing out in becoming a successful 21st century organization. The push for these newer digital technologies, however, has led to major concerns over data compliance, privacy and governance.

“Each and every one of us has become a living data set — our age, our race, our salary, what are likes and dislikes. Every business is collecting every second,” said Dinesh Nirmal (pictured), vice president of development for next-gen analytics at IBM Corp. “… How do you create insights to the data? How do you create consent on the data? How do you be compliant on that data?”

Nirmal 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 big data and its impact on data standards and regulations. (* Disclosure below.)

There are three angles that will disrupt the big data market, according to Nirmal: AI, blockchain and quantum computing. These disruptions can come at a hefty price tag. McKinsey reports that tech giants, including Baidu Inc. and Google, spent between $20 to $30 billion on AI in 2016, with 90 percent of this spent on R&D and deployment and 10 percent on AI acquisitions. McKinsey also found that 20 percent of AI-aware firms are early adopters, concentrated in the high-tech/telecom, automotive/assembly and financial services industries.

Data privacy concerns

An ever-present challenge with big data is meeting privacy regulations, including the fast-approaching General Data Protection Regulation. Effective in May, the European Union will begin implementing GDPR for any business with European operations. The regulation is about protecting consumer data, data consent, the export of data outside the EU, and companies’ responsibilities to erase personal data under strict conditions. If companies don’t follow the regulation, they will face steep fines and other penalties.

“This is really scary for companies, because they’ve been trying to catch up to the big data world,” Nirmal said. “How do you make sure that that data gets completely deleted [in time]? … How do you get that consent from the customer …? So there’s a whole lot of challenges,” he said.

With the machine learning market predicted to be worth $8.81 billion by 2022, according to Markets and Markets, companies are working on new risk management and data protection strategies in AI, blockchain and quantum computing. For instance, IBM has built tools like its InfoSphere Information Governance Catalog, an interactive, web-based program that allows users to  analyze data and sources and establish a governance framework. IBM is also building a data virtualization layer — a federation layer — that will allow data scientists who want a very small piece of enterprise data to pick just that data and build a model, for example, based on that data, without moving the data.

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