Big Data, Big Solutions How to Embrace Big Data and Manage Risk Kendall Burman Counsel Mayer Brown Elise A. Houlik Associate General Counsel Fannie Mae Beth Hill General Counsel and Chief Compliance Officer FordDirect
What Makes Data Big? (AND WHY IS IT SPECIAL?)
What Makes Data Big? What is big data and do I have it? The Three V s: Volume, Velocity, and Variety of Data Examples of big data use in industry and public sector Why is big data special? Maybe it s not? Still fits into regulatory/best practices framework Widely embraced by different sectors Distinct issues arise because of data s bigness, not its uniqueness 3
Big Data Big Solutions Government review and guidance on big data: White House issued reports on Big Data in 2014 and 2016 FTC Report on Big Data: A tool for inclusion or exclusion? EU s guidance on Big Data and addressed by General Data Protection Regulation 4
The Big Data Lifecycle FOCUS ON ISSUES FROM BEGINNING TO THE END
Big Data Big Solutions Big Data Lifecycle Collection Use Compilation and Consolidation Data Mining and Analytics 6
The Big Data Lifecycle Collection: Growing variety of sources from which to collect data (IoT, mobile apps, traditional sources) How do you provide adequate notice or obtain appropriate consent given these different sources of collection? Are you collecting data directly from individuals or from third-party sources? Do third parties have permission to share data? In context of big data, even more important that disclosures are clear, concise, and accurate. 7
The Big Data Lifecycle Compilation and Consolidation Big data is created through compiling and consolidating different data sets in order to obtain maximum value for different uses. Data is defined as either personally identifiable or anonymous/aggregate. But what if data is fluid between these two categories? Identifiable data can become anonymous, and anonymous data, when combined with other data, can be linked back to an individual. 8
The Big Data Lifecycle Data Mining and Analytics Data doesn t have all the answers, and sometimes those answers are wrong. Problems are a result of incomplete or inaccurate data; faulty or biased algorithms. Data tools can be outsourced to other entities, and security concerns are raised when permitting a third party to access or host data. 9
The Big Data Lifecycle Use The main focus of US government reviews on big data have been on the uses of big data and whether these uses are unfair or biased and to the disadvantage of the data subject. Use concerns work both ways are you using the data in ways that are unexpected or are you providing data you collected to a third party who may be using it in unexpected ways? 10
Big Data Solutions (COME WITH BIG DATA CHALLENGES)
Big Data Solutions Most common big data challenges and how to solve them: 1. But the data is anonymized 2. But more data is better 3. But I trust my vendors with my data 12
Big Data Solutions But the data is anonymized? Problem: Companies may rely on the fact that data sets are anonymous in order to avoid personal data restrictions. Or they may define anonymous or aggregate data as personal/confidential when it s not in order to further protect it. Solution: Accuracy and anticipation are critical when labeling data. 13
Big Data Solutions But more data is better? Problem: You can always make a case to acquire and hang onto data, but maximizing data invites privacy and security problems. General concern from regulators with companies that tend to collect lots of information and retain the information for long periods of time, often without any reason to believe it will be needed or used. Research has shown that adding more data does not solve the issues of inaccuracies and bias. Solution: Minimize data and have a data governance policy. 14
Big Data Solutions But I trust my vendors with my data? Problem: Many companies acquire data from outside sources, contract out their analytics, store data with a cloud computing company. Vendors, security and privacy practices may come back to haunt you. Solution: Diligence, contracts, audit rights, and effective follow-up. 15
Takeaways
Takeaways Embrace big data solutions, but have a (data governance) strategy Ensure disclosures are clear, concise, and accurate Data has limits: recognize the risk of error in using novel tools used to process highly variable data Think about the human effect and unintentional impacts of new big data tools Understand the risks of holding onto data and of using third parties 17
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