Gaps from Sophistication
Just because we are using more sophisticated types of analyses does not mean that we are doing a better job listening to all of our data appropriately. As an example, let's consider a clustering analysis of the cars that were sold in the first quarter. We decide to use every attribute we have for these vehicles (price, colour, type of interior...) and let our very sophisticated algorithms automatically decide how many significant clusters exist.
Voila! Four clusters are produced and we are instantly presented with these "meaningful" groups of vehicles sold. When we truly listen to more of the detail data elements, however, we find that the clustering is really only highlighting that there were four major price points. The mid-size and the luxury cars were spread across the top two depending on the options installed and the compact and sub-compact cars spread across the bottom two depending on options. Just because the analysis was more sophisticated it does not mean it listened to the data better. Unacceptable silences can still exist, and in fact are often harder to find.
Gaps in the Field of View
At first glance, knowing everything on the window sticker of every car sold in the first quarter seems to provide a great set of data to understand what customers wanted and therefore were buying. At least it did until we got a sinking feeling in our stomachs because we realised that this data only considers what the auto manufacturer actually built. That field of view is too limited to answer the important customer desire and motivation questions being asked. We need to break the silence around all the things customers wanted that were not built.
In summary, we need to be careful to listen to all the relevant data, especially the data that is silent within our current analyses. Applying that discipline will help avoid many costly mistakes that companies make by taking the wrong actions from data even with the best of techniques and intentions.
Dr. Bradley Fordham, PhD, is CTO, Wireless Generation, (ART+DATA) Institute Thought Leader
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