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SmartAdvice: How To Leverage Business-Intelligence Tools

Companies want to leverage their investments as much as possible, The Advisory Council says, so consider, what are your pain points? Also, how to structure an outsourcing deal so you can cut your losses early if it's not working out.

Editor's Note: Welcome to SmartAdvice, a weekly column by The Advisory Council (TAC), an advisory service firm. The feature answers two questions of core interest to you, ranging from career advice to enterprise strategies to how to deal with vendors. Submit questions directly to smartadvice@tacadvisory.com


Question A: What are important considerations when selecting business intelligence and OLAP tools?

Our advice: Now that most businesses have invested in transaction-processing software and databases, many are investigating how to leverage these investments to gain more detailed and relevant insight into their operations. Technology vendors have responded with a plethora of business-intelligence tools ranging from executive dashboards to in-depth transaction analysis systems. Before investing on one of these systems, it's important to understand how these tools can help extract the information to gain competitive advantage, while avoiding some of the pitfalls.


Related Links

BI Scorecard: Evaluating The Suites, One Functional Area At A Time

OLAP Analysis

Survey Of Business Intelligence Systems


There has been much vendor hype about business intelligence and online analytical processing tools. If you believe the vendors, installation is a snap and your return on investment will be immediate. Don't get me wrong, these tools can be powerful additions to your toolkit, but they're expensive and can be difficult to implement.

  • Identify the problem
    Before investing, understand your motivation for considering such a system. What are your pain points? In other words, what problems need to be solved or what goals are you hoping to achieve? Since data-analysis tools take a fair amount of configuration and development effort, don't waste your time solving problems that don't need to be solved. Match the tools with your overall business strategy. Micro-customization, commodity, or multichannel strategies each have different requirements.
  • Technology considerations
    The key to a successful implementation of any of these systems is to make sure that the data is in a usable form. If you're lucky enough to be creating the data architecture from scratch, then it's easy to incorporate any needed modifications to current data at the outset. If you have the more common situation -- legacy systems and obsolete data structures -- consider a middleware solution with custom connectors. This solution can be particularly effective with deeply entrenched, incompatible legacy systems. While using OLAP tools can generate unprecedented and detailed views into business operations, implementation often will take more time and resources than originally anticipated.
  • Custom development vs. commercial off-the-shelf tools
    Once project goals are well defined, the next decision is how much custom software to develop (whether in-house or outsourced). Generally, unless there's a very specialized need, you should start from the assumption that someone else has probably built a tool that will do at least 80% of what you need.
  • Integrated systems vs. bolt-on tools There's no question that an integrated system will deliver better performance, but they can be quite expensive -- millions of dollars for a full implementation. If you have a fairly modern data architecture built on a commonly supported database platform (SQL Server, Oracle, DB2, etc.), bolt-on tools might serve you quite well without a large development effort.
  • Real-time vs. lag-time metrics OLAP tools are processor-intensive; adding levels and dimensions to the OLAP cube causes processing costs to increase exponentially. To mitigate this problem, limit the number of real-time reports and do as much preprocessing as possible. Commonly, report-generation capability is divided into several tiers based on roles within the organization.
  • Be prepared to spend serious money on processing power and data-modeling software. If you have legacy systems (and who doesn't), then be careful to determine if you need to re-architect your system first. These tools have much promise, but to take full advantage of their power, you need to have the right data architecture and be willing to devote resources in the planning stage.

    -- Beth Cohen

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