1. Cyberspace Law Committee Meeting, August 3, 2012 Big Data Lois Mermelstein The Law Office of Lois D. Mermelstein lois@loismermelstein.com 512-222-8589 Ted Claypoole Womble Carlyle tclaypoole@wcsr.com 704-331-4910

2. What Is Big Data? ✤ Data that exceeds the processing capacity of conventional database systems. ✤ Too much data ✤ It moves too fast ✤ It’s too diverse

3. How’d we get here? ✤ Storage, processing speed, and bandwidth are becoming exponentially faster ✤ Networking is expanding exponentially ✤ And you can buy all the pieces - data, infrastructure, processing source: http://radar.oreilly.com/2011/08/building-data-startups.htmlhttp://radar.oreilly.com/2011/08/building-data-startups.html

4. Crunching Big Data - Volume ✤ Turn 12 terabytes of tweets/day into improved product sentiment analysis ✤ Convert 350 billion annual meter readings to better predict power consumption ✤ Crunching Facebook recommendations based on your friends’ interests

5. Crunching Big Data - Velocity ✤ Time-sensitive analysis and decision-making - to catch important events as they happen ✤ When there’s too much input data (so toss some) or immediate decisions must be made ✤ Examples: ✤ Scrutinize 5 million trade events/day to identify potential fraud ✤ Analyze 500 million daily call detail records in real-time to predict customer churn faster

6. Crunching Big Data - Variety ✤ Not just names/addresses in a customer database ✤ Want to analyze text, sensor data, audio, video, location data, click streams, log files, and anything else that’s available ✤ Principle: when you can, keep everything - there might be something useful in what you throw away

7. Unexpected Consequences ✤ Anonymous AOL searcher isn’t (NYT, 8/9/2006) ✤ Anonymous Netflix users aren’t, when compared with IMDb database (Wired, 12/13/2007) ✤ For many, browsing history is unique and repeatable (8/1/2012) ✤ Target knows when you’re pregnant (NYT, 2/19/2012)

8. Lessons to (Re)learn ✤ Correlation isn't causation ✤ But correlation may be all you need ✤ You can't hide in the crowd

9. Personally Identifiable Information PII as a mathematical function How many points of data do you need? Pineda v Williams Sonoma Stores, Inc. (Cal, Feb 10 2011)

10. HIPAA De-Identified Data Re-Identifying De-Identified Data

11. Escaping Regulatory Requirements Privacy Fair Credit Reporting Redlining Employment Discrimination

12. Single Transaction Owned By: Retailer Wholesale vendor Manufacturer Shipping Company Customer’s Bank Customer’s ISP Retailer’s Bank Merchant Card Processor Phone company/Hardware/Software

13. Government Using Big Data Law Enforcement

14. Copyright Issues Who owns the data? Who owns the derivative works? Combined data?