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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."


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