1) A production manager shows a graph with the average number of widgets produced in 2 hour time period. This allows the sales director to calculate the number of widgets he/she can expect to deliver in the next week.
2) The same production manager shows the same graph but states the accuracy of the data has an error margin of 3-5%. This allows the sales director to calculate a much more realistic number of widgets to be delivered over the next week and thus satisfy the customers with more certainty.
OK; this is a simple scenario but you get the picture, as more and more complex data sets are created the conclusions made from them become ever more prone to error or higher risk consequences. So, by being open within the business culture to accept data error bands then a business can become more prepared to pivot and gain competitive advantage as the reality of performance is realized.
The quality of your data and data capture systems will define the difference for your business between operating business intelligence system (BI) as opposed to a business stupid system (BS). We all know that BS does not stand for very long!
For this purpose I suggest that leaders question their questions, question the quality of data being provided and allow the foundations of your business intelligence systems to become robust. Once this sound strong foundation is in place the delights and fun and advantages of data mining, accurate OLAP (online analytical processing) provide the back bone to great commercial decisions and when you are ready to engage in the world of big data, your own quality contributes to a more trustworthy and beneficial system.
Did you copy,paste and action the above steps? No? Well, try it, you got this far and I would not enjoy sharing this information if you did not use it! Indeed I prefer that you stop reading right now and action the above steps before you continue.
So, did you? OK I’m kidding, now that you have the above process underway you can march forward with increased confidence in your business intelligence quality.