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Ten Mistakes to Avoid When Modernizing Your Business Intelligence Programs.

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TDWI Ten Mistakes

Over the last several decades, BI has evolved from large, monolithic implementations controlled by IT (think Business Objects or Cognos) to orchestrated sets of smaller, more agile capabilities that include visual-based data discovery and governance (such as Tableau, Qlik, and others). These new capabilities provide more democratic analytics accessibility that is increasingly being controlled by business users.

In this evolution, the modern analytics subsegment of the BI market has seen the most dramatic growth—substantially faster than the overall BI market. For this reason, the first wave of BI modernization focused on analytics agility and the ability to add smaller vendors and point solutions to traditional BI platforms. Largely, traditional BI platform vendors have expanded to support these trends.

As you embark on your BI modernization effort, you would do well to learn from companies that have successfully completed their own BI modernization projects—and even from companies that have failed in that regard. In the not so distant future, your own BI modernization will incorporate the advantages of AI, augmenting much of what are now manual processes to streamline data management and evolve the analytics process for faster, more accurate insights. To move to this exciting new future, we must first understand the common mistakes that have negatively impacted BI modernization projects. If you can avoid these pitfalls, you can ensure a successful BI modernization initiative.

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