Crucial to the success in today’s digital world is being able to convert data to insights. Doing this effectively will give us an advantage over our competitors and can even become new services.
For example, the French firm Babolat originally developed tennis rackets with sensors for quality control to check that the new rackets were strung correctly. In testing these they found that the long-held beliefs about where the ‘sweet spot’ was for hitting the ball were incorrect and were able to improve their product. Then they realized that the data and analysis from these sensors could be used to help improve people’s game – and developed an app and online coaching tool which provides analysis and tips based on your personal data of where and how hard you hit the ball on the racket. In a further spin off, many professional players started to use these rackets and so the firm were able to sell the data generated in professional tennis games to TV programmes who could show these statistics as part of their commentary.
However Forrester reports that whilst:
Some people are using the vast swathes of data now available to simply look for patterns –helpfully, we now know that people are more likely to stock up on strawberry pop tarts before a storm in the US. However, in most cases, we will garner greater insights when we take the time to formulate the right questions and are able to understand enough about the black box of AI that spits out the answer to know how to interpret the results, and at what confidence level. A good rule of thumb is to continually separate “what data can I actually do anything about” from “what is interesting”.
For example, built on top of its SAP infrastructure, one large Asian telecoms company has implemented real-time performance dashboards for each of its 1,000 small business teams that display customer acquisition, customer and employee satisfaction and financial profitability. This additional data has had a direct impact on the business, leading to greater transparency, accountability and increased agility.
So next time someone else has done the analysis and is presenting the results to you – make sure you know what questions to ask:
- What business goal do these insights help us solve?
- What’s the wider context in which this data and insights fit (e.g. benchmarks, year on year trends)?
- Are these results statistically significant?
- What is the confidence level of this data?
- What outliers were there and how were these dealt with?
- For how long will these predictions stay valid?
And most importantly, what insights should we take from this data?
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