Once poor data has entered a system it can replicate and infect good data, much like a contagious disease. Results extrapolated from a bad data set may be unknowingly tainted and you may be making decisions that have far reaching implications, based upon false beliefs. Further still, it may only become apparent that you’ve made wrong decisions due to bad data, at a very late stage… Or worse, perhaps even never.
Separating the good from the bad
If we are to preserve the integrity of our systems, bad data must be refused entry at the very first gate. If we first establish the qualities exhibited by good data, we can then distinguish the good from bad. A good data set should be;
Once good quality data has been obtained and the bad purged, you must ensure to preserve integrity by correct data management. The process of extracting data from one set and merging it into another, poses significant risk of corrupting previously good data.
Data obtained from different sources may achieve more accurate results if processed separately. If however you’ve been able to assess the compatibility of the proposed merger and deem all sources suitable, there is obviously a statistical benefit to analysing a larger pool of data.
Results speak for themselves
Ultimately, it may prove practically impossible to completely test the accuracy of all your data. If this is the case then a final method of testing your data is via the results born of your marketing campaigns. If you are receiving outcomes that are not reflected in the statistics derived from your initial data, then it’s time to look again at that data.
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