Business intelligence (BI) practitioners have always thought of the world as data-centric. Data integration, data warehouses, data marts, reports, and query builders were always about data. BI has traditionally excelled at answering questions like "what happened" or even "why did it happen" but always fell short on "what do I do about it" and fell short of the next logical steps which traditionally have been the realm of business process management (BPM) and business rules engines (BRE). This data-centric view of the world turns out to be plain wrong. The world is much more process and rules-centric. We run many processes every time we come to the office, these processes generate data, which in turn trigger rules, and in turn generate more data output that is being consumed by processes in an endless loop.
Why is BI TEI (Total Economic Impact) so elusive? Recently I reached out to all major BI software vendors and asked them to provide a customer reference who's willing to stand up and confirm a hard $ return on investment from BI implementation. Guess how many takers I got? None. Yes many are willing to point to expected savings and benefits, but no one's gone back and calculated the actual results. Why? It is definitely very complex. For example:
Make sure you account for both direct and indirect costs.
Direct costs are the obvious expenses and capital expenditures associated with BI software, hardware and consulting services. A good rule of thumb is to expect to pay $5-$7 dollars for system integration and management consulting for every $1 you pay for software. And don't forget to include the costs of training and on-going support.
Indirect costs are for software/hardware/services for non-BI specific components which are nevertheless necessary to achieve a successful BI implementation: data quality, master data management, metadata implementation, portals, collaboration, knowledge management and many others. The indirect costs are not as easy to quantify. For example, do you attribute the cost of implementing a data quality solution to the BI initiative? Most likely your data quality problems exist in your sources, so one might think it should be a separate effort. However, very often you identify data quality problems when you build your first BI solution, so there may be a tendency to bundle in these costs into the BI project. As a result, these indirect costs are notoriously difficult to identify and negotiate (with other stakeholders), but nevertheless they are a major component of the total cost.