By 2004, Wal-Mart’s data warehouse had reached 500 terabytes of data. (A terabyte is one million million bytes). Since then, it is estimated that the volume of data being stored is increasing by 50% a year – doubling every two years, and data flows on the internet in the United States alone are approaching an annual total of 1,000 exabytes. (Two exabytes equals the total volume of written information generated worldwide annually; five exabytes equals all the words ever spoken by human beings.)
This is what is now being called the age of ‘Big Data’.
It’s not difficult to understand the reason why this is happening. We collect more and more data, particularly about our customers: all their transactions; their ordering patterns and frequency; who they are, where they are and how they behave; and what they say about us on the social networks. And we store it, because the cost of storing or transmitting a kilobyte of data is now too cheap to measure.
What is less understood is that this data is probably one of the company’s most important assets and one that is woefully under-utilised. It is a strategic resource that can be used for making better decisions.
A recent study estimated that if all the data being stored by companies globally applied ‘deep analytical’ expertise to their data, then total savings would be more than $149 billion in operational efficiency improvements alone. No wonder the World Economic Forum, at their last meeting in Davos, declared data to be a new class of economic asset, like currency or gold.
In a research study into 179 large companies published last year, it was found that those companies applying ‘data-driven decision making’ achieved productivity gains 5 to 6% higher than any other factors could explain.
So how do you mine the potential gold from this most valuable asset? How difficult will it be? Well, three simple steps will do it:
1. Identify the data that just sits in storage just because it’s there.
2. Find the analytic support that will help translate the data into useable insights.
3. Put them together and get the insights. But don’t ignore the insights – act on them.
If you take these three steps, you’re on the road to data-driven discovery and decision making.
As a meerkat once said: “Simples!”