Thanks to GDPR and other data regulations, companies are required to have a certain degree of insight and control of the data they collect and store about individual people, or risk paying hefty fines. But getting the necessary level of insight and control is a big challenge, thanks in part to the way companies store data and the large number of data repositories they use. Now a startup called BigID is using machine learning to help companies identify everywhere individuals’ data resides and how it’s being used.
As a product category, data discovery is a well-trod area of the market. There are many vendors pitching solutions that can find sensitive data, such as Social Security numbers, for example, or credit card numbers hidden in relational databases, file systems, and sundry other locations companies end up stashing data.
But according to Dimitri Sirota, CEO of BigID, standard data discovery tools don’t go far enough in determining the context around the information that companies have stored about individuals. That extra granularity is necessary thanks to the General Data Protection Regulation (GDPR) that give individuals new privacy controls, including the power to demand companies tell them exactly how their data is collected and used.
“The problem with privacy is that you have to find not just PII [personally identifiable information] but PI, all personal information,” Sirota tells Datanami. “And what that means is you have to be able to figure out if it’s personal based on the context to a person.”
Standard data discovery tools can ferret out pieces of PII like names, addresses, phone numbers, and email addresses fairly easily. But under the new data privacy paradigm, individuals are empowered to know about other pieces of data, such as IP addresses, GPS coordinates, and cookies that companies often collect and increasingly use as part of their analytic workflow.
BigID came to market about 18 months ago with a solution designed help companies get a handle on these types of data. Tackling this problem in a head-on, relational or SQL-esque manner would require creating and maintaining intricate tables that track each data field for each individual. Master data management (MDM) software vendors found that nearly an impossible task before the big data boom, and today, it’s simply out of the question.
