Closing the 'insight-to-action' gap with analytics and decision management

For James Taylor of Decision Management Solutions, improving operational intelligence starts by contrasting two familiar concepts: efficiency and effectiveness.

"There's a famous definition of effectiveness versus efficiency: efficiency is doing things right and effectiveness is doing the right thing," Taylor says, quoting famed management expert Peter F. Drucker.

Taylor drills down into that definition in the context of business processes: "If we think about efficiency--doing the thing right--we get a lot of things that are typically measures of successful BPM projects: time to complete a process, cost to serve a customer, cost to process an order"--the kinds of things in which a good BPM project can really help eliminate process inefficiencies.

But thinking about effectiveness generates a slightly different set of measures: "We get things like customer profitability or customer retention," among others, he says. Those types of measures are less likely to be obviously linked to business-process initiatives.

He cites an insurance company as an example. "If we have completely innovated and improved our claims-processing process but we're paying the wrong claims, then it doesn't really matter how efficient our processes are--we're going to have a very poor claim ratio. So our effectiveness measures cannot be improved simply by automating and streamlining our business processes. We must do more," he says.

"Doing more," in Taylor's view, boils down to a single word: analytics. "I believe the use of analytics, particularly the use of analytics in the context of an operational business process, is key to driving this kind of effectiveness improvement," he says.

Effectiveness is especially important today, when organizations find themselves capturing and struggling to manage tremendous amounts of operational data, Taylor says. Analytics help businesses simplify data so that they can apply it, learn from it and make better decisions as a result.

Defining analytics

Analytics is a powerful term, one that's currently generating plenty of buzz—and some confusion to boot. "It has a tremendously wide range of meanings. It could mean everything from everything from reporting to data warehousing to BI-like technologies to data mining and even out to optimization and simulation," Taylor says. "All of those different techniques are fundamentally analytic techniques. They are about simplifying your data so that you can get more value out of it."

And simplification can lead to powerful gains in operational effectiveness, as some companies are already learning. "They talk about dramatically improving customer retention or online conversion rates," Taylor says. "They talk about boosting the effectiveness of their marketing campaigns and driving up acquisition and driving up campaign response. You even hear stories of people reducing crime or driving down the overall risk of their customer portfolios."

In such cases, companies are using analytics to answer critical business questions. He cites these examples: "How do I prevent this customer from turning, convert this visitor, acquire this prospect, make a compelling offer to this person, identify this claim as a fraudulent one or correctly estimate the risk of this one?" The answers to such questions can boost both BPM-driven efficiency improvements and operational effectiveness.

Adding decisions to the mix

Decisions have a central role in the application of analytics to business processes, Taylor says. "Decisions matter because, for many organizations, there is an 'insight-to-action' gap," he continues, referring to a phrase he and co-author Neil Raden coined in their book "Smart (Enough) Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions" (Prentice Hall, 2007).

Basically, companies run analytics techniques against data to come up with insights. "But there are two gaps that prevent this insight from actually being applied," Taylor says. The first gap: Managers don't really understand what decisions they are about to make--and make differently--because of that analytic insight." For that reason, he says, they have a hard time applying the analytics insight effectively. Secondly, once a decision is made, they can't change the behavior of their operational systems or processes. "If you're going to apply insight, you have to be able to take an action as a consequence," Taylor says.

He illustrates that imperative with a story involving a former client company whose business management hired an analytics team to build a sophisticated customer segmentation model. "The IT guys said, 'OK, show us the model because we need to implement it in the CRM system and the website,'" Taylor recalls. "So the business people gave them a PowerPoint presentation"--which, the IT team pointed out, wasn't really a model that could be used in the information systems.

"They had great insight into their customer base, but it didn't present them with an opportunity to change the way they ran their business because it didn't help them impact the operational processes that affected their customers," he says. "To do that, they needed to find a way to apply those insights at a particular point in an operational process—a decision point. They had to know and have access to a decision point in an operational process where that insight would result in different behavior, different actions being taken."

Bottom line: "Decisions are more than just about finding out new information," Taylor says. "Decisions are a point in time where you have gathered and considered some data and you have a selection to make; you have a set of options you could consider or a set of choices from which you must select."

Once you've made that decision, you've also made a commitment to action--for instance, a transaction or a customer response. Says Taylor: "It's that commitment to action that is crucial if you're really going to drive analytics and analytic effectiveness into your operational processes."

About the Author

Anne Stuart, ebizQ's editor from mid-2010 to mid-2013, is now senior editor for at ebizQ's parent company, TechTarget. She is a veteran journalist who has written for national magazines, daily newspapers, an international news service and many Web sites. She’s specialized in covering business and technology issues for 20 years. Based in Newton, Mass., she can be reached at Follow Anne on Google+ and at annestuart_TT on Twitter. For general questions about ebizQ, please e-mail

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