The Best and Worst in Business Intelligence

    BI software – tools, platforms, and applications alike – holds great potential for helping organizations readily access the enterprise information needed to make informed business decisions and, ultimately, achieve their business objectives. But, as with any technology, the implementation, roll out, and usage practices play a critical role in the success of BI.

    In tracking mediocre results, and even failure, in the implementation of BI software over the years, many common threads, or “worst practices,” can be found. These worst practices set companies on the inauspicious path of BI failure. They have been repeated by some of the best run and smartest companies in the world. Typically, these worst practices are the result of wanting to ride the latest technology wave without balancing the hype with practical knowledge and experience.

    In order to help organizations learn from the mistakes of others, Information Builders provides the following insight into the worst and best practices for BI, so that you will have a solid understanding of how to avoid BI failure and achieve success with your BI initiatives.

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    Click through for four worst and four best practices identified by Information Builders.

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    BI tools – report design, ad hoc query, and OLAP tools – provide a valuable service and play a critical role in a company’s overall BI strategy. They are not, however, what business users need. Business users need readily available, actionable information that supports educated decision making. While BI tools offer the ability to uncover information, they are simply too complex and time consuming for the majority of business users.

    Often, the first (not to mention the most damaging) mistake that organizations make when assessing BI solutions is neglecting to include business users on the selection committee. When business users are excluded from the solution selection process, their need for simplicity is completely overlooked.

    In some instances executives and managers are technical enough to use a BI tool, but they don’t have the time to work with a BI tool and navigate a data warehouse to produce the information they need. Most people need a faster, easier way to get the information they need than that provided by a BI tool.

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    Even though Excel provides much utility for business users, it wreaks havoc on the quality and consistency of information. This is especially damaging in heavily regulated industries that must adhere to strict compliance legislation.

    Excel was never intended to be a BI tool. It is not Excel that is at fault, but rather its use as a BI tool. Much of what is found in Excel spreadsheets is put there through a manual, error-prone, process, which should never be the case with BI. BI applications should only utilize data from reliable, trustworthy sources.

    Another unique phenomenon created by Excel is what is called “spreadmarts.” When individual users accumulate their own store of relied-upon data in their personal spreadsheets to a point where their information becomes a critical data source, it becomes a spreadmart. A spreadmart is an unregulated, non-secure datamart in the hands of a user who rarely backs up his or her data and may leave his or her job on a moment’s notice. This single user isn’t a problem so much as the accumulation of hundreds of users with unregulated datamarts.

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    This particular worst practice is complex. Data warehouses are a very important part of information technology and, in particular, are a critical component of many analytical systems. So it is not the data warehouse that is the problem. Rather the worst practice arises when a data warehouse is viewed as the solution to all information problems or when it is expected that the availability of the data warehouse will drive business users to information.

    The truth is that not all BI applications require a data warehouse. Many BI applications are better served with integration and portal technology that allows data to reside where it currently exists and pulls it on an as-needed basis. Unfortunately, many organizations fail to assess whether or not a data warehouse is the right solution to their challenge before starting down the warehouse path.

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    Without a specific purpose, BI rarely has impact on business. The starting point for creating a BI solution should be when you identify a project that will solve a specific problem through access to information in a timely fashion and in the right context. “Solve a problem,” means that information will accelerate a slow running process, eliminate a bottleneck, reduce the cost of doing business, or even become a new revenue source.

    When information requirements such as these are identified up front and used as the business driver behind the BI implementation, the implementation of the BI system has a much greater likelihood for success.

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    Start your next project by identifying how timely information delivered in the right context can accelerate a process, reduce costs, or improve productivity in a particular area. Do not start the project for a general purpose!

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    Identify which data integration method will allow you to prepare the data for your application in the most timely and least costly manner. You may find out that a data warehouse is appropriate, or perhaps not. (If you have already chosen a tool it may limit your choices in this area.)

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    Build a BI application that leverages Web-based, parameter-driven forms, personal end-user scheduling for regular e-mail delivery, and alternate output options (e.g. HTML, Excel, PDF) to give end users flexibility.

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    Include business users on the selection committee to ensure that you implement a solution that will be embraced by all users. For most users, this means an easy-to-use BI application that doesn’t require too much of their time. But don’t neglect the preferences of your technical users either. BI tools, although complex, are a great way to provide technical business analysts with a way to contribute new insight to an evolving BI application. Evaluate how many technically savvy analysts you really have and build these tools into the final solution. Be sure that their work can easily be shared with other less technical business users so everyone can benefit.

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