Breaking down big data.

• Author: Fu, Elizabeth
• Position: Big Data at Work:Dispelling the Myths, Uncovering the Opportunities - Book review

Big Data at Work:Dispelling the Myths, Uncovering the Opportunities

Thomas H. Davenport

Harvard Business Review Press

2014, 240 pages

Our increasing ability to collect more and more information has popularized the term "big data"--to the point that it has become a catchall that simply means "data." But the words still have meaning. According to Thomas Davenport, "Big data refers to data that is too big to fit on a single server, too unstructured to fit into a row-and column database, or too continuously flowing to fit into a static data warehouse." The key attribute, he contends, is the lack of structure, presenting new challenges and opportunities.

Davenport begins Big Data at Work by admitting his initial skepticism about the concept, nearly dismissing it as hype. After extensive research, however, he identified differences between big data and conventional analytics. The book sets out to explain some of these key differences and to guide decision makers in making effective decisions about big data and its use.

OVERACHIEVERS, UNDERACHIEVERS, AND THE DISADVANTAGED

The early chapters of the book provide a general overview of big data and its importance, moving on to a description of ways a typical business trip can be transformed through greater analytical processing of information. In Davenport's view, "Big data is going to reshape a lot of different businesses and industries," but some industries are better suited than others. "Overachievers" like consumer products, insurance, online, and credit card industries are in a better position because they continually collect and actively analyze large amounts of data. Meanwhile, "underachievers" may have had more data, but they did not follow through with analyzing information that would have benefitted them or their customers. Lastly, "data disadvantaged" industries simply did not have the data to analyze, or their data are poorly structured.

DEVELOPING A STRATEGY

After helping readers identify where their organizations may fit on the "big data" continuum, Davenport guides readers into developing a big data strategy. He uses examples from various companies to address some of the common business objectives--reducing costs, saving time, providing new offerings, and supporting internal business decisions--that businesses seek to achieve by using big data. Guiding readers in developing such strategies, he walks them through the requirements of the big data analysis discovery phase, learning what...