Big data is like a fashion trend: You can't turn around without seeing yet another magazine article, research report or talking head pontificating on why you can't live without it. In this case, however, the trend-pushers have a point. Case studies at the Gartner Business Intelligence Summit added to a growing body of evidence that a company's ability to collect, process and analyze large data sets could mean the difference between success and failure. Leadership -- on the IT and business side -- is key.
First, let’s define the value of big data for your company. Back in 2008 and 2009, simultaneous improvements to storage, memory, processing and network technologies triggered big data's rise in popularity. These improvements are often characterized as the four V's of big data -- volume, variety, velocity and value. It's a moment in technological history that happens once every 15 years or so, and when it does, companies stumble to figure out how to take advantage of this rare occasion, often seeing it as an opportunity for more data analysis. This is where businesses are now with big data and why the fourth V – “value” has emerged.
Big data analytics can not only help businesses create new efficiencies, make better decisions and edge into real-time processing, it can also inspire business transformation. Some of the most successful manipulators of big data have figured out how to turn the data they've collected all along into a new source of revenue – we can just look at Netflix and Amazon as our case studies.
To get started take inventory of data sources you currently use within your business today, both internal and external. This could include operational data, such as point-of-sale or machine-generated; syndicated (commercial) data; dark data, which refers to data collected, used once and then archived, such as email; and public data, such as weather reports or census information.