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Can enterprise business intelligence be agile?

Instead of spending years developing an enterprise business intelligence strategy to consolidate BI tools and come to a corporate-wide agreement on key performance indicators, some enterprises are finding that an iterative approach at the departmental and business unit level is more effective.

For instance, a division of a global equipment provider to the pharmaceutical and bioscience industry turned to a SaaS BI provider to gather revenue and supplier metrics. This division wants a specific set of data and a relatively quick turnaround.

The company has many BI tools in place, but the division went with SaaS BI provider Oco Inc., because “Enterprise BI is too tough to tackle” said Mike Beckerle, Oco’s CTO.

Coming to a corporate-wide decision on the definition for one metric alone can be daunting.

Take a common enterprise business intelligence metric, customer profitability. On an enterprise scale, the definition of a customer will vary depending on which business unit you’re talking to, as will the metrics for deciding what should be measured to determine a customer’s profitability: How much does it cost to satisfy and support this customer, versus what they pay us in return, for example. That is one of many questions that can determine if a customer relationship is paying off.

Some argue that enterprise-wide definitions and metrics are unattainable. Not only because everyone has to come to agreement on what data should be measured, how it should be measured and who it should be measured by, but also because business goals and markets shift constantly, making a defined metric obsolete pretty quickly.

This is not to say that data should not be centralized or common BI goals are not being created across an enterprise, but I’m wondering if business units and departments are finding it easier and more productive to go it on their own. And if so, what ramifications will this have on an enterprise’s ability to discover cross-departmental patterns and achieve the ultimate goal of predictive analytics?

Let us know what BI path you’re on. Email me at

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Agility in BI does not have to be limited to local solutions. A common data architecture can evolve while delivering value quickly with package solutions, leveraging EII or Dashboards to similulate integration on the front end, or allowing functional areas to innovate directly into a common data platform. This is harder than traditional centralized development and requires some creativity in architecture and governance, but drive an enterprise solution without throwing agility out the window.
Intelligence is an attribute of persons. The assignment of it is called the person's IQ. A person may also be agile, but how to assign it has not been agreed.. 
Businesses do NOT have intelligence, albeit in a business/company employing people, it could be construed that the the average IQ of the people employed is the IQ of that business. 
A business/company employing people may likewise be agile.