Copyright © 2009 James Taylor. Visit the original article at Analytics simplify data to amplify its value.
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With IBM’s announcement this week that it was acquiring SPSS I have been talking to a lot of folks about analytics. Analytics is one of those topics that is often on the edge of what IT people know so I thought a couple of posts on analytics might be useful.
Now analytics can mean a lot of things and different people interpret it differently but I always like to go back to one I first heard at FICO:
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Analytics simplify data to amplify its value
This always struck me as going to the core of analytics – the power of analytics to turn huge volumes of data into a much smaller amount of information and insight. People use analytics as a phrase very casually, describing everything from reports to embeddable analytic models built using sophisticated statistical techniques. A report, of course, largely fails this test as it does not simplify data nor amplify its value, it simply packages up the data so it can be consumed. Or, in the case of most reports, so the data can be ignored. Reports are not analytics, but dark alleyways into which data is lured and quietly strangled. But many things can be described as analytics:
- Visualizations
- Statistical analyses
- Data mining results
- Predictive models
In every case the analytics are simplifying the data (a picture, a graph, an equation not thousands of rows of data) and yet amplifying its value by showing a data consumer what the data means. That consumer could be a person or a system, and different kinds of analytics work better in different circumstances.
IT people need to educate themselves on the role of these different kinds of analytics and their potential. Anytime a system has data that users or other systems want, the designers of that system should be asking themselves if there are analytic techniques that could be applied to amplify the value of that data while simplifying its consumption. And you don’t need to understand the math behind these techniques to tell what might be useful. Understanding the power and limitations of these techniques is enough to spot the opportunities. Your systems store and manage data so something that makes that data more valuable makes your systems more valuable.
