Articles or market studies which are backed by data are normally seen as more relevant by readers than those which do not feature data. In some cases, the authors use tons of data to prove their thesis. But is more data always better?
From my point of view, it is not the amount of data displayed that counts. It is the quality of the data used to gather information about trends or market segments that really matters. Therefore, the first step to turn data into insights is a qualitative check of the data—the so-called data cleansing—as especially as data derived from a large dataset may contain errors which can lead to wrong assumptions when interpreting the outcome from an analysis. In addition, it is important that the data is derived from a meaningful data universe which contains all relevant data and is in the best-case survivorship bias free.
The second step is to calculate meaningful statistics since it doesn’t help to calculate statistics or ratios which are not sufficient for the use case. Sometimes one does see statistics that prove the thesis of the author, but are not fit for purpose, as they might be too general to drive a real assumption from the results or are simply not created for the respective kind of analysis. Therefore, the universe from which the data are derived needs to be clarified and statistics used for the analysis must be fit for purpose to turn data into information.
After this it is time to translate the information gathered from the statistics into knowledge by connecting the dots. To do this, one needs to add some qualitative elements to the analysis because this helps one to understand what happens in the analyzed market or segment.
To turn the knowledge of what happens within the analyzed market or segment into insights, one needs to dig even deeper, as this means one adds the “why” to the “what.” To achieve this, one may need additional statistics from related markets or segments, as well as some grassroots research to understand what really drives the trends in the analyzed markets. Any author and reader should ask himself or herself whether the assumptions and conclusions made in an article or study are really backed by the data provided.
The views expressed are the views of the author, not necessarily those of Refinitiv Lipper or LSEG.
