In 2011, the world fell in love with ‘Big Data’.
Defined as ‘the collection, processing and availability of huge volumes of streaming data’, Big Data has helped to make more accurate decisions, across a multitude of industries, at little cost.
However, while processing massive amounts of data has had a huge impact, businesses of all sizes are now focusing very much on Small Data… stories from their actual customers, individual thoughts on their company etc. It is set to be the driving force of change and the source of a data revolution in years to come.
Yes, the news on the street, or more appropriately the news on the web, is out with Big Data and in with Small Data!
Big Data vs Small Data
Both Big Data and Small Data hold their own pros and cons…
Big Data is often characterised by 3 V’s: the volume of data, the variety of types of data and the velocity at which it is processed, all of which combine to make Big Data very difficult to manage. Small Data, in contrast, consists of usable chunks. Additionally, Big Data is far more generalisable to the masses while Small Data is specific to the individual.
But what exactly is ‘Small Data’?
Small Data can be defined as a small dataset that contains very specific consumer behaviour attributes. Commonly a case study, it includes anything from the most frequently used emoji to the way a person tends to walk in circles whilst on the phone. In other words:
‘key insights derived from small samples’
A keen campaigner of the importance of Small Data is Martin Lindstrom who argues it is Small Data that leads to big ideas. So much so, he wrote a book on the matter last year, titled: ‘Small Data: The Tiny Clues That Uncover Huge Trends’.
Some examples of Small Data successes featured in his book include:
- Shoes found in an 11 year old kids bedroom leads to Lego’s turnaround!
- The bible losing all its bookmarks lead to the invention of the Post-it!
- Toys in the corner of a bachelor’s living room lead to the humanisation of the Roomba!
- While smoking sent a wrong photo and wished it could disappear – Snapchat!
Another example he uses of Small Data triumphing Big Data is Google’s prediction of the U.S. flu epidemic back in 2012.
Google announced its ability to predict a flu outbreak in the U.S. five days before it would happen based on Big Data and search patterns. In 2015, the centre for disease control revealed that Google’s prediction was twice the size of the correct number.
The point being here is Google cared about the correlation (Big Data) rather than the causation (Small Data). If we spent more time “sweating the small stuff” and understanding the reason why such outcomes occur, there is often something far more valuable to learn!
Sweat the small stuff, make change!