Sanjiv Rai was one of the few pollsters who predicted a Trump win in the last US election. His claim is that he used 20-million data-points in his AI forecasting tool to predict that victory. His great achievement was to make sense of 20-million items of data and to do it accurately. His courage was to go public when virtually everybody else was predicting Clinton.
In a simplistic way I’m using this story to draw our attention to the difference between data and information. Data is the building block of information and so both are valuable but in different ways. As decision-makers we need to seek information that gives us the insight to make better decisions. As analysts we need to make sense of the data accurately.
Ten or so years ago I did the opposite to Sanjiv Rai. We were working with one of the large financial service firms. Our job was to build a model to help them make sense of their enormous business. In one sense the question was simple “how do we organise our business to hit our annual earnings target?” However the business consisted of hundreds of products and teams and that made it a complex question. The problem deconstructed was how to best distribute a limited budget across numerous initiatives to secure earnings. Those initiatives included product advertising, customer incentives, staff incentives and recruitment. They were targeted regionally. As you can imagine the problem had hundreds of different factors. From a technical point-of-view we built a cracking business model that sliced and diced the data and results in a million different ways. However, it was immensely time-consuming (perhaps even impossible) to chart a journey through this complex situation and draw a clear conclusion. We created lots of data but we created little information.
I learnt from that mistake that the most valuable work is not building the model, but understanding what the analysis really means and helping others do the same. It was a career defining moment because it shaped everything else that followed. At the time, we came up with the tag-line “realise the value” because it reminded us we had to work hard to convert the output of any business model in to meaningful, relevant and graphical information sets that are quickly assimilated by our partners. It was one of the most important experiences that shaped how we approach our work.