Essay on how big data is different

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Thomas H. Davenport, Paul Barth and Randy Bean

How ‘Big Data’ Is Different

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How ‘Big Data’ Is Different
These days, lots of people in business are talking about “big data.” But how do the potential insights from big data differ from what managers generate from traditional analytics?
BY THOMAS H. DAVENPORT, PAUL BARTH AND RANDY BEAN

THESE DAYS, MANY PEOPLE in the information technology world and in corporate boardrooms are talking about “big data.” Many believe that, for companies that get it right, big data will be able to unleash new organizational capabilities and value. But what does the term “big data” actually entail, and how will the insights it yields differ from what managers might generate from traditional analytics?
There is no question that organizations are swimming in an expanding sea of data that is either too voluminous or too unstructured to be managed and analyzed through traditional means.
Among its burgeoning sources are the clickstream data from the Web, social media content (tweets, blogs, Facebook wall postings, etc.) and video data from retail and other settings and from video entertainment. But big data also encompasses everything from call center voice data to genomic and proteomic data from biological research and medicine. Every day, Google alone processes about 24 petabytes (or 24,000 terabytes) of data. Yet very little of the information is formatted in the traditional rows and columns of conventional databases.
Many IT vendors and solutions providers use the term “big data” as a buzzword for smarter,

THE LEADING
QUESTION

How can companies capitalize on insights from
“big data”?
FINDINGS
Monitor the flow, rather than a fixed supply, of data.
Work with data scientists rather than data analysts.
Integrate analytics into core business and operational functions. Big data encompasses everything from clickstream data from the Web to genomic and proteomic data from biological research and medicine.

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more insightful data analysis. But big data is really much more than that. Indeed, companies that learn to take advantage of big data will use realtime information from sensors, radio frequency identification and other identifying devices to understand their business environments at a more granular level, to create new products and services, and to respond to changes in usage patterns as they occur. In the life sciences, such capabilities may pave the way to treatments and cures for threatening diseases.
Organizations that capitalize on big data stand apart from traditional data analysis environments in three key ways:
•They pay attention to data flows as opposed to stocks. •They rely on data scientists and product and process developers rather than data analysts.
•They are moving analytics away from the IT function and into core business, operational and production functions.
1. Paying attention to flows as opposed to stocks There are several types of big data applica-

tions. The first type supports customer-facing processes to do things like identify fraud in real time or score medical patients for health risk. A second type involves continuous process monitoring to detect such things as changes in consumer sentiment or the need for service on a jet engine. Yet another type uses big data to explore network relationships like suggested friends on LinkedIn and
Facebook. In all these applications, the data is not the “stock” in a data warehouse but a continuous flow. This represents a substantial change from the past, when data analysts performed multiple analyses to find meaning in a fixed supply of data.
Today, rather than looking at data to assess…