Big Idea: Competing With Data & Analytics

The Four Traps of Predictive Analytics

Management consultant James Taylor explains how to avoid common mistakes of predictive analytics.

Michael Fitzgerald Reading Time: 4 min 

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Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
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Computer Mouse Trap

If the name James Taylor makes you think of “Fire and Rain,” Carly Simon and adult contemporary radio, you’re probably not into business analytics. On the other hand, if you are into business analytics, or more specifically predictive analytics, the name means something very, very different. The other James Taylor is British and the CEO of Decision Management Solutions, an analytics and management consultancy in Palo Alto, California — and, to the right audience, he’s a rock star.

Taylor was in Boston recently performing his “greatest flops” — a countdown of the things companies fail at when starting out to do predictive analytics (drumroll, please):

The First Trap: Magical Thinking

Taylor said companies see analytics as a kind of magic — plug in some data and reap profit windfalls. The truth is, companies must understand what they want before they go analyzing things helter skelter, especially when it comes to making predictions. He points out that there are really only four things businesses can use analytics to predict: risk, opportunity, fraud and demand.

Companies also can’t just build a model once and apply it everywhere. Each of the four areas will almost certainly need different models, and companies may find they need a different model for every question they ask, Taylor said.

The Second Trap: Starting at the Top

Organizations often try to start using predictive analytics at the top of the organization to gain buy-in, Taylor said. But top executives make the kind of decisions that don’t lend themselves to analytics, he argues. Predictive analytics works best on decisions that get made repeatedly, but top executives most often make strategic decisions, which, Taylor said, tend to be one-time situations. Other top-level decisions are often tactical, which are also relatively complex and hard to formalize.

But operational decisions, such as those in which companies choose a supplier or determine whether to extend credit, lend themselves well to predictive analytics. So companies need to recognize that predictive analytics works best for prompting decisions about operations, rather than initiating their use at the executive level.

Companies also need to frame their predictive analytics around actions. “Don’t look at how good a customer is. Look at, what action should I offer to a customer? Should I change suppliers?” Taylor said.

Topics

Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
More in this series

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Comments (3)
HORACIO CARVAJAL
This article explains the main issues for a project in Analytics. But I would stress on the importance of Data. In my experience, data usually brings many unexpected findings which may result in a transformation of the project's goal/answer to respond, or it would simply require additional time for creating the right data set.
Henry Aguillon
From a basic logical thinking perspective First and Second traps as presented above have been way too common in my experiences. As a fallacy of equivocation a layman understanding is often presented to multiple problems to "what" should be doctrined as the predictive focus, and not only miscommunicated to higher levels but mis-scoped on capabilities and capacity, because of improper research of the objectives. What you get is just repetitive pitches on what stakeholders want to hear.

This in turn results in overreaching expectations and lack of results, and of course a waste of time and resources. 

I appreciate this article and great job exploring the tunnel vision in predictive analysis.
Praveen Kambhampati
Great article with simplified solution. Reflects the maturity and depth and expertise gained from Consulting assignments. Thanks for sharing.

The traps appear some similarities to ERP implementation assignments in manufacturing companies. Interestingly the IT enabled companies today train and assign their finance guys to predictive analysis and develop one silver bullet to undo tomorrow's wrong doings of investment. A desperate deployment for future proofing with a one time modelling.

As well clarified Predictive Analysis, is not a one time, task but an ongoing evolution of a decision support system. more realistic inputs would make the mirror more clean to a high definition reflection of an organizational status as it stands. The refinement and data inputs need more areas and importantly departments other than financial accounting and P&L, to have predictive enablement to support better decision making. 

Also organization paranoid about data security may find it difficult to get benefited from Predictive analysis. They may have to use the methods and modelling separately and multiple times to gain a consolidated view of the analysis performed.