Last in my last post on Return on Information I dealt with Information Sophistication. This time I focus in on how this sophistication could enable us to harvest information from historical facts. Enabling us to create KPI’s, understanding the factors leading to success or failure.
What Descriptive analytics is
In a nutshell Descriptive analytics is effectively a set post-mortem analysis. Almost all management reports falls within this category, they portraits performance factors based on historical data. Their aim is that recipient of the analysis is able to gain some sort of understanding on the reasons behind successes or failures.
Descriptive analytics tend to group up information on relevant dimensions, unlike predictive models that focuses on the individual element. For instance is Descriptive models is great for comparing and creating KPI’s over SBU’s performance.
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