How would you describe your data? That is a phrase you’ll never hear outside of a Data Scientist cocktail party. But it’s an important question.
Descriptive Data, or “Why did that happen?” is where the past 2 levels really starts to sing. Now that your reporting supports business intelligence you can start to dig even deeper
Digging deeper with data sounds intimidating but it doesn’t have to be. Your BI level information works well for short term understanding and is almost a real time indicator of your business. Learning WHY it happened factors in variables that would never cross your mind.
So we used the teddy bear example of business intelligence. You can correlate data points to see beyond the obvious. Descriptive Data takes that even further by looking at the data correlations you might miss and brings them to your attention
For example: Using our Teddy Bear company, your reporting and BI indicate there is a spike in sales around February and then again around December. It shouldn’t come as a shock that these are connected to the holidays. If that is a shock, you might want to get out of the teddy bear business.
Descriptive Data looks deeper into the data and notices things that seem obvious but aren’t always clear. In this case it tells you that dads and husbands buy the bears in February and that mothers buy bears in December.
Not the most groundbreaking discovery either, that is pretty common sense for those holidays. However, it also tells you that its dads under 40 but over 27 who are buying the bears. Its moms 20-35 who are buying the bears later in the year. This insight translates to better accuracy with sales and marketing efforts.
Machines don’t find this kind of work tedious or boring, like most of us would qualify it. Because its a machine you can run these kinds of reports all day every day. To do this by hand would take weeks or months. A machine can do it in minutes.
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