The high failure rate of BI projects and programs has been well documented throughout the years. The reasons why they fail has also been well documented, which leads to the obvious question of "why do they continue to fail at such a high rate?" Our company is often brought in to assess existing BI programs and provide recommendations to organizations to help turn around their BI programs.  There are many reasons and combinations of issues that can play a part in the failure of BI projects and programs. 

One important reason is what I like to call the allure of the silver bullet. In most organizations, BI projects emerge out of an immediate need to report and analyze data that is not easily accessed. It could be in a source system that has a complex schema, in multiple systems and require integration, be too voluminous to easily analyze, or one of many other data access related issues. There also may, or may not, be tools available to facilitate the reporting and analytic process beyond MS Excel. Whatever the reason, experienced BI practitioners will usually estimate the solution at 3 to 6 months on average if no BI infrastructure exists, or 30 to 90 days if one does.In the face of this daunting estimate, the first instinct of many decision makers is to search for a less costly solution, in terms of both time and money.

Fortunately, for these decision makers, the market has recognized this opportunity and sprouted vendors that have developed pre-packaged reporting and analytic solutions to meet this need. They are often created for a specific source system, subject area and/or industry vertical. In some cases, they utilize custom interfaces, but more often they leverage existing BI front-end software. If they provide ETL capabilities, then it's usually delivered as pre-developed mappings through an off-the-shelf tool. The sales team provides a well-polished demo and promises a solution in less than a week. It sounds pretty convincing. So, how can these pre-packaged analytic solutions lead to so many failed BI projects and programs? The concept in itself isn't the issue, in fact in many cases it can help reduce the implementation time and cost, as well as provide value. The issue is that these solutions aren't a silver bullet and are usually evaluated tactically rather than as part of the framework of the overall BI strategy.

BI project success and failure is measured by most companies as whether the projects are delivered on-time and on-budget. In fewer cases, they are measured on the return on investment (ROI). The challenge with pre-packaged analytic solutions is that they are sold as a quick hit and low cost solution to the traditional BI project. What they usually fail to mention is that this is only possible in the case where an organization has not customized their ERP system, has a high level of data quality, only moderate data volumes, only requires sourcing data from one system and has 100% overlap in reporting requirements. Unfortunately, this doesn't describe the state of most data environments. What is more common is that these projects require more time to implement at a cost that often approaches, if not exceeds, a traditional solution. If these factors aren't accounted for in the project timeline, budget and ROI calculation, they are seen as having failed.

The challenge that pre-packaged analytic solutions present to the long term success of BI programs is that their data model is often difficult to scale and once customized, difficult to upgrade, especially when an ETL and semantic models are included. BI architectures that are difficult to change, given the constant change of business and requirements on BI, is a recipe for failure. They also often don't track changes to the data and provide a flexible architecture, such as staging layers that facilitate the addition of new source systems.In some instances, it ties organizations to a data integration (DI) tool that doesn't scale or adds another tool to manage in their environment. At one customer we assessed, the DI tool that was used by the pre-packaged analytic solution was the same one they were using, but a different version and so they had to maintain two versions of the same software.

As I mentioned, the issue isn't with pre-packaged analytic solutions per se, it's that they are often purchased for the wrong reasons and not factored in to their long term BI strategy.Organizations can derive a great deal of value from these solutions, but don't get lulled into believing that they are a silver bullet.

A version of this article was first published on February 16, 2011, on www.b-eye-network.com.