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'Small data' is just as important as its 'big' counterpart

Paul Newman Archive

The latest way of thinking about analytics in the corporate environment is that "more is better." Breaking down data is the key to gaining a competitive advantage in business, whether it's retail or health care or anywhere in between, so IT offices are clamoring to acquire more information. The more they can get, the better results they can achieve.

But here's the counterpoint. It's not just about quantity of data collected - it's also about quality. When companies mine for information in countless different places, collecting knowledge from a variety of web forms and phone interactions, it can be difficult to keep it all straight. With increased data comes decreased data quality.

That's why it might behoove companies to focus less on "big data" and more on its smaller counterpart. What does small data mean? It's open to interpretation, but one answer is this: It means zooming in on exactly the right subset of data you're looking at. If you want to know how your product will sell among women aged 18 to 24, then look at that precise group and get the most accurate reading possible. Block out the extraneous noise.

What small data entails
According to Information Age, "small data" is much more than just a buzzword being tossed around in IT offices. Editor Ben Rossi sees it as a new way of thinking about analytics.

"On one hand it's a design philosophy, inspired by consumer apps and services that deliver useful data, content and insights to users on the go," Rossi explained. "On the other, it's the technology, processes and use cases for turning big data into alerts, apps, and dashboards - the 'last mile' for business users within corporate environments."

For both of the above reasons, focusing on smaller subsets of data really does matter.

Why it works in business today
So how can small data help? What can it do for a business that big data doesn't already?

There are a couple good answers to this question. One has to do with ROI. Simply put, collecting massive amounts of information costs a lot of money, and there's no guarantee that investment will pay off. Would you rather make a million cold calls, or instead call 1,000 people who are really relevant leads? Think about which one is a better bet.

Secondly, there's the matter of data quality. The more focused that businesses become when collecting and analyzing people's information, the easier it will be to fight off inaccuracies.