Five Questions You Should Ask Before Investing in Predictive Analytics

Loraine Lawson
Slide Show

Five Ways Big Data Can Turn Health Data into Actionable Insights

Predictive analytics is causing quite a buzz right now, but organizations tend to forget one crucial question before jumping in: Does the data and data architecture actually support useful predictive analytics?

It seems so obvious, and yet, a shortage of good data is the most common barrier to successful predictive analytics, according to Tom Davenport, a data digital business expert with a long list of credentials such as President’s Distinguished Professor of IT, research fellow at the MIT Center for Digital Business and Management at Babson College.

For instance, if you want to make predictions about your customers’ future buying patterns, you need data about where they are buying, what they have bought, and information about those products and demographics, Davenport explained in a Harvard Business Review blog post.


Predictive analytics deals with identifying trends and relationships that are less linear. Often, it leverages less traditional data sources than BI, such as social media data.

But Davenport’s piece only touched on the issue of data. A recent Health IT Analytics more thoroughly discusses the problems of source data. Even though the focus is on health care data, it’s easy enough to extrapolate to other industries and enterprise data.

In addition to determining whether you’re collecting the right data, the article recommends you ask:

  • Is the data delivered in real time?
  • If not, how recent is the data source?
  • What’s the time frame I need to target? (Year to year? Month to month? Week to week?)
  • How does this compare to my competitors? (That’s not always possible to know, but if you’re not investing in real-time data and your competitor is, you may find that your efforts are too little too late, according to Health IT Analytics.)

Data Analytics

Health IT Analytics makes great points about investing in predictive analytics, but it’s especially important for health care IT divisions. Author Jennifer Bresnick also looks at how underlying data structures and the resulting interoperability issues can cause problems for predictive analytics projects.

Loraine Lawson is a veteran technology reporter and blogger. She currently writes the Integration blog for IT Business Edge, which covers all aspects of integration technology, including data governance and best practices. She has also covered IT/Business Alignment and IT Security for IT Business Edge. Before becoming a freelance writer, Lawson worked at TechRepublic as a site editor and writer, covering mobile, IT management, IT security and other technology trends. Previously, she was a webmaster at the Kentucky Transportation Cabinet and a newspaper journalist. Follow Lawson at Google+ and on Twitter.



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