The term business intelligence (BI) has become a catchall for a range of capabilities, from data warehouses to dashboards, from predictive analytics to performance management. During a recent engagement with a financial services company, we drew a sharper line between the terms reporting and analytics.

Reporting is the means of delivering and presenting data. Most companies have a multitude of reports that provide information about different business metrics. In days of yore, the infamous green-bar report proffered costs and revenues. As cumbersome as it was to produce, the green-bar report was little more than a readout of data from a single system.

Traditionally, people who developed operational reports were applications experts familiar with the data layouts and processing functions of their application systems. The applications themselves are process-centric, thus, reporting is status- and indicator-based, reflecting a moment in time. To answer a predictable set of business questions, reports on the day's sales numbers or inventory levels are distributed at the close of business each day. Typical questions include: are we running low on inventory? (Action: Let's place an order with the supplier.) What's the average customer wait time in queue? (Action: We'll need to add more customer care reps.) How many beds are occupied in ICU? (Action: Let's realign nursing staff.) Operational reporting provides status indicators, giving businesspeople what they need to take action.

But static, batch reports don't explain why. The business corollaries for this abound: Why is inventory so low - because sales are rising or because the order process is inefficient? What can we do to prevent this from happening in the future? Change prices? Increase supply?

A regional bank we worked with recently had operational systems staff that regularly built reports on loan performance. The reporting data came from the bank's loan processing system, whose programming staff understood the metrics and data attributes associated with the loan process, from origination to funding to servicing.

Unfortunately, IT staff couldn't address information that fell outside of the loan system. Many banks repackage and sell their loans to other parties in a securitization process. When bankers needed to understand risks associated with the loan portfolio, the operational systems staff couldn't produce the necessary information. These requirements meant access to data sources outside of the operational application.

The chief financial officer (CFO) realized that the bank required more than just loan statistics. Bankers needed to know the potential of loan defaults and the likelihood of prepayments and late payments, revealing risk. They wanted to analyze the attributes of an individual customer that might affect his ability to pay. The CFO wanted to forecast the risk and profitability of the bank's loan portfolio and knew that this information exceeded IT's existing capabilities. It was time for additional data and analysis tools.

And IT needed to evolve its business knowledge. Where traditional operational reporting measured existing processes, the new business requirements not only involved more data but a newfound level of business rigor. The business hadn't yet identified the rules necessary to calculate profitability and risk. IT had to formalize business requirements gathering in order to drive clarity around profitability and forecasting. They had to work with the business to determine what data needed to be analyzed and how.

The bank never had a data warehouse, having always relied on operational systems for its reports. To address the range of new business analysis needs, IT needed to integrate new subject area content with existing data. To delineate responsibilities, ensure reasonable workload and minimize political backlash, we recommended creating a separate team whose responsibilities would transcend single-system functional reporting.

The new BI development team would include broad business and data analysis skills that would initially help define risk analysis and forecasting. Whereas operational reports within the application areas would continue to leverage narrow application data, BI development would model and integrate data from multiple sources and collaborate with business users to establish definitions, calculations and derivations. The team would also acquire tools that could support more complex data inquiry and provide visualization capabilities that went beyond simple two-dimensional charts and reports available with the standard set of financial applications. The new organization provided a larger, more cross-functional data delivery capability.

It's too early for the bank to hang the BI center of excellence shingle. Most of the members of the new team come from other areas of the business and continue to be mentored by outside experts. But as more integrated data becomes provisioned, functions such as metadata management, data security and data quality will enrich the team's repertoire and make information the corporate asset the bank knew it was all along. Problem solved! 

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