The presidential election offers practical lessons to companies planning to profit from Big Data. The data-driven get-out-the-vote operations illustrate the four steps in Big Data: (i) data collection; (ii) analytics based on the data collected; (iii) business decisions based on the analytics; and (iv) converting the decisions into business outcomes. Analyzing data and making decisions without translating them into specific outcomes — such as getting the right voters (customers) to vote (purchase products) — short-circuits the business use of Big Data.
“Wrongful use of restricted data can be like a virus that infects the entire database and exposes the company to liability.”
Conducting meaningful predictive analytics is difficult. A distinction can be drawn between simple data mining, such as adding up regional sales from records organized by state, and business intelligence, which involves discerning patterns to learn things hiding in the data but not identified earlier. The legal issues in this phase involve protecting the data sets, algorithms, and the information resulting from or used to generate the analytics.
Executives making data-based business decisions require staffs or third-party advisors who can translate data into meaningful company-specific analyses. From an executive's perspective, the ability to analyze the analytics is as important as the underlying analysis of the data. Given the advantages of drilling down into data and viewing it from different perspectives in real-time, visual displays of Big Data analytics will evolve from static pie charts and PowerPoint presentations to dynamic, interactive data displays. The legal issues that arise at this stage are trade secret and other intellectual property protection for business plans based on analytics, and confidential agreements with outside data scientists and consultants. Further IP issues arise when academic institutions are involved.
“A distinction can be drawn between simple data mining, such as adding up regional sales from records organized by state, and business intelligence, which involves discerning patterns to learn things hiding in the data but not identified earlier.”
Big Data is where analytics, cloud computing, and mobile computing converge. Mobile computing provides a way for employees — and customers — to access and act on data from remote locations. It enhances the goal of providing data anytime, anywhere regardless of the device and employee location. Building the IT infrastructure to conduct Big Data is expensive, and requires continuous updating to take advantage of improved hardware and increasingly powerful software tools that enable better analytics. While a select number of companies can afford this infrastructure on their own, for many companies it makes more economic sense to use industrial-strength cloud computing service providers. These providers operate in a competitive environment where constant improvement is a business requirement. This benefits customers because they are buying ever-improving services rather than equipment. Critical legal issues in both mobile and cloud computing are privacy, data protection, and cybersecurity.
The business value of Big Data is converting data collection, predictive analysis, and business decisions into business outcomes such as cost reductions, efficiencies, new products, better customer and business partner relationships, increased profitability, and achieving a competitive advantage in the market.
This article originally appeared in Law Technology News on November 12, 2012.