Organizations around the world and across various industries are employing the philosophies of big data in order to gain more insight into their business and improve processes. However, it is not rare for many executives to wonder if big data is just analytics.
The two may be related but there are critical differences between them, according to Andrew McAfee, principal research scientist at the Massachusetts Institute of Technology (MIT) and Erik Brynjolfsson, Schussel Family professor at MIT’s Sloan School of Management.
In a recent article in the Harvard Business Review, the two outlined three key points where big data is different from analytics:
Volume – Recent research by analyst firm International Data Corp. (IDC) indicates that the global amount of digital data will grow from 130 exabytes to 40,000 exabytes by 2020.
For example, Walmart collects more than 2.5 petabytes of data every hour from customer transactions alone. A petabyte is one quadrillion byte or about the equivalent of 20 million cabinets of text. Consider that 90 per cent of the data today was created only in the last two years.
Velocity – The speed of data is even more important that the volume. Real-time or near real-time information access enables organizations to be quicker in making decisions and executing moves than their competitors.
For example a group of researchers from the MIT Media Lab used location data from mobile phones to determine how many people were in the Macy’s parking lot on Black Friday – the start of Christmas shopping in the United States. This enabled them to estimate the retail company’s sales on that day even before Macy’s was able to record its sales.
Variety – Big data comes in many forms. It can come in the form of images posted on Facebook, email, text messages, GPS signals from mobile phones, tweets and other social media updates.
These forms of data are collectively known as unstructured data. Each person today is potentially a walking data generator. That’s, however, data that is not organized in database.
The structured databases that stores most corporate information are not well suited for storing and processing big data.
At the same time, elements of computing such as storage, memory, processing and bandwidth are becoming cheaper making it more economical for companies to conduct expensive data-intensive approaches to analyzing information.
Big data may be unstructured and unwieldy, but there’s an enormous amount of vital signals among the “noise” that accompanies it. The value is just waiting to be mined.