The current wave of big data isn’t the first one. The previous ones just went under a different name, something like BI; Business Intelligence, or AI; Artificial intelligence. They stood then for a new level, in the triangle between storage technology, computer technology and depth of understanding, i.e. the results of the subsequent learning process.
So as not to throw the baby out with the bath water, in the current discussion about digital transformation, insight from the previous wave is needed. This was about ‘learning maths’, developing rule generation. An algorithm can learn from thousands of cases and derive rules, from which customer behaviour can be predicted.
In relation to the depth of understanding, you can predict the behaviour of most customers through rules with two, three or a maximum of four points of behaviour. Secondly, for higher resolution (more points) the total picture becomes worse. The maximum achievable resolution was (in correctly predicted individual cases) around 85%.
Today is about the use of big data in digital translation. The first message is that it does not make economic sense to set rules for customer relationships. The second message: we are all 85% collective knowledge and 15% individual. In a business management interaction that means: I as a provider can regulate 85% of processes in a customer relationship, the remaining 15% I have to leave to my customers. Of the 85% of the processes, I can rely on 3 or 4 points. The world is actually simpler than we are inclined to think.
The real challenge of big data in digital transformation is not about understanding customers. It is about activating customers, Because the 15% only comes from them.