Dr. Elliot King

Decision support systems and their progeny business intelligence have long been thought to be the domain of large companies, often very large companies. Even the term “data warehouse” conjures up images of vast amounts of data that has to be pulled and assembled to serve up a report.

Historically, projects geared to building business intelligence infrastructures have cost millions of dollars, taken months if not years to complete and have involved scores of people from both the IT organization and an array of business units. Even though implementing BI has long been high on many CIO agendas for several years, the subtext always was that BI was for the big boys. 

But that might be changing. A series of interesting conversations have taken place over the past year indicating that BI solutions may be within reach of small and mid-sized businesses. With vendors providing some BI functionally within their core product, and the emergence of hosted solutions and SaaS-based BI solutions in the Cloud, the cost of BI may finally be at a point that SMBs can consider investing in  this technology.

Of course, virtually all businesses, even the smallest ones, always engage in some form of business intelligence by asking simple questions like: Did we make more money this year than last? Who are my most profitable customers? What are new areas of opportunities? For very small companies, the answers to those questions are often either obvious or intuitive.

The real value BI delivers is to automate the process of providing and analyzing information that can provide data to support those insights and uncover trends. At its most basic, a BI infrastructure consists of three parts—analysis, reporting and data integration.  In most BI implementations, the data integration is the most complex and costly part of the process.

For small companies, the first step towards implementing a business intelligence infrastructure has nothing to do with data. First, you want to develop key performance indicators (KPIs), that is, what do you want to measure? Then ask yourself how robust and accessible is the data you need to analyze. The answers to those questions will guide and define what you need to do next in a successful BI implementation.

Image contributed by Michael Elliott