Business Intelligence Solution: Success or Failure?
How many times have you heard a statistic such as "42 percent of respondents rate a business intelligence solution as moderately successful" or "more than 50 percent of all business intelligence solutions fail"? When I read these types of statistics I often wonder what's behind these numbers. I'd love to be able to drill down directly to the respondents and ask them how they defined success or failure when answering the question. I recently met with a company that asked if I could come in and help make their business intelligence solution more successful. Naturally, my first question was to ask them how they define success. Each person in the room defined success a bit differently and meeting turned into a healthy discussion on what constitutes a successful business intelligence solution.
As an industry, I believe that one of our big challenges is that we have left the definition of success relatively ambiguous and in some cases, in the hands of the tool vendors. We have done a good job at identifying the general contributors of success, such as ROI, better access to data, number of users, increased quality of data, cost savings, etc. The problem is that these contributors in themselves don't necessarily determine the success or failure of a business intelligence solution. For example, how many business intelligence solutions are considered a failure because they were three months late delivering the initial data warehouse or have less than five percent active users in their company.
As a starting point for discussion, I am going to make a definitive, and likely controversial statement, of what defines success and throw some concrete numbers into the mix. In my experience, there are two components that determines the success of a business intelligence solution; reality and perception. Reality can drive perception but perception cannot drive reality. From a reality perspective, the success or failure of a business intelligence solution can only be measured by its ROI. Why? Because companies are in the business to make money and if they don't, they will ultimately fail. A business intelligence solution is an investment that a company makes in hopes of achieving a positive return. With that in mind, I contend that a business intelligence solution that achieves a negative ROI over time is considered a failure. One that achieves a zero to 24 percent ROI is considered a moderate success and one that achieves a 25 percent or greater ongoing ROI is considered highly successful. Ongoing ROI is calculated each year that takes into account all increased revenues, decreased costs as well as capital and operational investments.
A quick story from the trenches. A few years back I was the director of data warehousing for a large manufacturing company and each month the CIO used to ask me how many active users do we have of the data warehouse. He believed that the program was failing because less than 40 percent or our targeted constituents were using the data warehouse. Then our team embarked on a project sponsored by the VP of international sales to identify high growth customers and create a set of targeted analyses and reports for his field reps. Ultimately, he attributed three percent of the growth that year in international sales to the work of the DW team. Our CIO stopped asking how many active users we had and starting asking how much ROI we were generating from our business intelligence solution.
Some readers whom have attended my Lean BI course might be scratching their heads thinking this conflicts with the first principle, "Focus on Customer Value". In my next blog, I'll discuss how customer value drives ROI and why both reality and perception are important to the success of a business intelligence solution and how reality can drive perception. In the meantime, I welcome your feedback.
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