As an example of analytics in action, McNeice cites the vision of the smart grid -- the "future" of electricity supply in which digital technology will allow richer connections between consumer and producer, improving cost allocation and driving efficiency. The smart grid is the kind of open-ended, multivariable situation -- with millions of consumers and thousands of power sources -- that is tailor made for analytics.Analytics is the feedback loop that makes possible the completion of the think-plan-do-check business cycle. You need that feedback loop to improve your processes
Susan McNeice, Research analyst at Forrester,
"BI and analytics are a beautiful combination. If it were up to me I wouldn't deploy one without the other," she says.
Crudely speaking, analytics is the brain to BI's brawn. BI does a great job of managing large quantities of data and organizing it so that it can be useful. It can take that data from many parts of the enterprise -- manufacturing, marketing, customer service -- and pull it all together.
Analytics is the final step that adds an additional layer of insight, which can then provide insights about specific phenomenon. Thus, it plays an ever more important role in business process management. "I would venture to say that in BPM, analytics should be able to give you tremendous insights about the effectiveness of those processes. It may show which processes are most efficient and where changes need to be made in the process by actively correlating activities with outputs," she says.
"Analytics is the feedback loop that makes possible the completion of the think-plan-do-check business cycle. You need that feedback loop to improve your processes," she adds. True, real time analytics -- like the smart grid example -- takes things a step further and can be a vehicle for in-process change and for real time or near-real-time improvements in a process.
McNeice says analytics usually isn't a simple plug-and-play addition to BPM. Although you can buy software packages from vendors, "Analytics takes time. You need to develop an analytic competency in your organization or you need to depend on a managed service provider, which can be a good option if you are a smaller company," says McNeice.
Additionally, says McNeice, corporations must be patient. Analytics must be done by people who know what they are doing -- not necessarily PhDs in statistics but people who know how to harness the value of the product. "The hallmark of a good analytics environment is the willingness to experiment. Start small, get some pilot projects out there, and scale fast," she says.
This was first published in September 2010