BI Mistakes

Avoid overdependence on vendor’s claims about their product and its performance. Everyone wants to be the best when they are making a presentation about their products. The value and performance of the products always look promising; they just might be. But one must be careful to do one’s own homework about the product rather than just blindly accepting vendor presentations and claims. The key here is that tools are not the only critical success factors for a successful data warehouse. There may be instances where the capabilities being evaluated from the tool are not available in the current release; however, the vendor might showcase that they’re been planned in the next release. Making decisions based on these kinds of assumptions is very risky for the simple reason that you are building a thorough dependency on the delivery of a separate entity over which you have no control. In making a decision about a tool on which you are spending a fortune, collect references from the vendor and network with them to see how the product has been doing in a similar environment. You should leverage references where work has already been completed. Apart from the references, it might also be worthwhile to consider the analysis of those products done by research firms like Gartner and Forrester.

Mistake #8: Assuming data quality can be managed “somehow.”

As we speak about the maturity of the data warehouse today, there is still a lot to be explored and learned about the severity of the data quality problem. Assuming data quality can somehow be taken care of might lead to lot of inconsistencies in the downstream systems. The quality of the data has to be checked and cleansed at the source or at least before it enters the data warehouse. It is inappropriate to do any quality checks in the data warehouse itself.

Companies depend heavily on information to make decisions regarding profits, effective operation and customer satisfaction. Inaccuracy and inconsistency in the data will hinder the company’s ability to perform competitively. An effective data quality program is almost a must in these maturing systems. It would allow companies to analyze better and make more meaningful decisions. The data quality initiative need not start as a big bang or with the purchase of an expensive tool. It could be an initiative a step-by-step approach that could be automated with a tool once it’s matured in organization.

Mistake #9: Overdependency on contractors and ignoring the need to build BI capabilities in house.

Hiring contractors for specialty skills has benefited data warehouse delivery within organizations. However, contractors may benefit specific projects, but not on an ongoing basis. If there is overdependency on the contractors who come in, do good work and leave upon delivery, they not only take their deep knowledge with them but also are not available for any clarifications and fixes if a need arises. Don’t treat contractors as employees. You should draw a very clear line for what contractors can help with and what stays internal.

Contractors can very easily be caught up in office politics. This is even truer if your contractors are coming from a specific software vendor. It is practically not possible for them to be unbiased about their products. This is where a knowledgeable in-house person with BI skills is able to evaluate and advise the clients if anything is derailing or if the technology is just not fit for their environment.

Mistake #10: Assuming you are done once the data warehouse project is in production.

As the nature of data warehouse is change and becoming more and more productive with iterations, so is the delivery. Once the project is in production, you have just completed a phase - you are still not done. You have a new world to explore and make continuously

...