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James Taylor's Decision Management

James Taylor

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:

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.

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I, too, find that people use the term Analytics, but really can't describe what it is. I am in the business and I can describe it multiple ways, depending on my audience. The way I generally explain it is to start with something people understand, the dreaded report.

A report is a visualization of data. A report could be something as simple as a list which dumps a bunch of raw data out in a list. That is what people generally think of as a report.

But now, let's take that report and determine what are the users of this report going to do when they look at the data? What actions are they going to take? Are they going to group things and look at sumamrizations? Are they going to need further detail about some of the records? What are the interactions they need?

Once you have some of these answers, you can put those capabilities right in the report. You can guide the users to their answer. You may want to guide them into a slice-and-dice UI which allows them to explore their data, then allow them to drill to a detail report from there to get subsequent information. When you have a system that allows you to do that, and there is some significant intellectual capital invested in the content and presentation of the data, you have analytics.

Then, as you mentioned, there are other branches of this into Predictive and Text analytics to name a couple.

I deal with Independent Software Vendors looking to embed BI into their applications. Most of them think primarily about embedding reports. They have a tremendous amount of Intellectual Property that can be embedded to build a robust Analytical Application, we just need to get them to use it and deliver what their customers really need.

James Taylor blogs about decision-management technologies such as predictive analytics and business rules, discussing how they deliver agility, improve business processes and bring intelligent automation to SOA.

James Taylor

James Taylor blogs on decision management for ebizQ, and is an independent consultant on decision management, predictive analytics, business rules, and related topics.

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