Monitoring Unstructured Data: A Case Study for Big Data and Finance

At the start of 2012, Big Data was identified as one of the 10 critical issues that would shape Wall Street by Wall Street & Technology. The financial industry has been trying to figure out how to efficiently and effectively leverage data for decades but regulation changes are forcing financial entities to focus on it now more than ever.

Unstructured data is critical as institutions make day-to-day business decisions. In the financial world, the difference between getting news as-it-happens as opposed to hours later can be millions of dollars. By harnessing the power of near real-time data, financial institutions can automatically monitor the Deep Web for news, press releases, SEC filings, or other online updates about companies of interest.

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FINDING COUNTERFEITERS: A CASE STUDY FOR BIG DATA AND BUSINESS

It is estimated that counterfeiting is a $600 billion a year problem that has grown over 10,000 percent in the past two decades. In the U.S. alone, counterfeiting costs businesses $200 billion to $250 billion annually. Counterfeiters are skilled at profiting off of another companies marketing efforts; and the openness of the Internet makes selling fraudulent products online easy. Finding them can be difficult and time consuming. According to U.S. Customs, the top 10 counterfeited products are:

  1. Electronics
  2. Shoes
  3. Pharmaceuticals
  4. CDs and DVDs
  5. Clothing
  6. Perfume
  7. Watches
  8. Cigarettes
  9. Computer hardware
  10. Toys and games

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Structured vs. Unstructured data

With respect to the mass categorization that is central to most computer operations, there are two types of relevant data which affect speed of assimilation as well as information recall: structured data and unstructured data.

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