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?
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, 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 real-time 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.