5 pitfalls of data analytics approaches

As more credit unions design and test their approaches to data analytics, a few common traps that slow success are emerging. During our presentation at the NAFCU Annual Conference, AdvantEdge Analytics’ Tim Petersen and I talked through five of these pitfalls and offered advice for side-stepping them.

  1. Innovating for Innovation’s Sake

    With so much hype surrounding analytics, especially when it comes to artificial intelligence, machine learning and deep learning, too many organizations are jumping in head first without fully understanding how the technology can make a difference for their end-users.

    To avoid this pitfall, sit down with your teams for a strategic level set. Identify one or two key opportunities for improvement, both internally and externally.

    That is your critical first step, and it comes before anything else – before hiring data experts, before finding a data partner, and definitely before deploying data technology.


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