The promise of big data is that when advanced analytics are applied to it we can learn new and different things that will allow business to progress. The problem is that many retail organizations are not prepared for this promise. In fact you can make an argument that for some, more data is clouding issues and fogging decision making but why is this? As more and better information is made available to the execution elements of a business it may fail to change behaviors. There are many practical reasons like data quality, availability, and tools, however this is not the real culprit. What is really at work here is a lack of understanding of the differences between people and machines.
For a smart machine, the more data the better the conclusion. Machines can ingest tremendous amounts of data, sort them for relevance, find patterns and predict outcomes. With each iteration the process is further refined by the incorporation of new data and measuring performance of previous predictive activity. Machines have no bias toward a particular outcome. If a machine makes a bad prediction it is not emotionally attached to it. Instead it learns from it and goes on to modify its algorithms.
People do many things quite well and given the appropriate information at the right time will make good decisions. However confronted with a wide array of perhaps seemingly conflicting information the human response is to either become paralyzed, unable to make a decision, or to resort to past behavior. Sometimes the information will point to a conclusion that renders their past behavior as obsolete. This is particularly difficult for a human to absorb and act on because they may be emotionally tied to the past behavior or action. Statements like “I built this business from the ground up by doing ….” or “I always run a back to school bogo promotion the second week in August” are just examples of the types of feedback that may result from analysis that says to take a new direction.
In many conversations with retailers that have implemented advanced technologies such as price optimization we find that the optimized prices are never really executed. Many retailers are struggling to implement more advanced planning applications. Just the act of implementing more advanced dashboarding capabilities has at times made it harder to make decisions. Its very important for retail business and technology leaders to understand this fundamental difference. The organization must be prepared for the blended human and machine workplace of the future. The best hope to map a successful future is to understand how to ensure the right information is delivered to the right person at the time of execution of any activity. People must be guided toward the acceptance of smart machines, gently at first but firmly and steadfastly mandated later on in the timeline.
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