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analytics program enablement for growth-mindset organizations

3 Mistakes Leadership and Practitioners Make In Predictive Analytics

3-mistakes-leadership-and-practitioners-make-in-predictive-analytics

The buzz about Predictive Analytics has not died down, and the organizations who have experienced its potential all agree: the discipline is here to stay. Its mainstream adoption is due to the success experienced by organizations that have properly implemented projects, but many more have held back due to a preconceived complexity. Both leadership and practitioners alike have been guilty of making crucial mistakes, preventing their businesses from reaping the rewards from predictive analytics. Here are a few of them:

  1. Beginning Predictive Analytics The Wrong Way

The most common barriers to making the leap and starting predictive analytics include lack of support from leadership, fear of substantial expenses, being overwhelmed by the flood of data and an unwillingness to apply a new initiative.

Predictive analytics isn’t like a typical IT project – this technology requires a formal and organized approach with complete support from leadership. Many companies make the mistake of starting predictive analytics with the wrong focus, by prioritizing resources into collecting, storing and transforming data instead of into unearthing hidden insights that are actionable and produce measurable results.

  1. Implementing A Project Instead Of Emphasizing Training

Unlike typical IT projects that focus on meeting goals at the operational level, predictive analytics requirees an emphasis on strategic planning and implementation. But the majority of the needed training to succeed in predictive analytics isn’t technical at all, because organizations now have access to software that automates the mathematics involved. With these tools, many practitioners are able to build successful predictive models with little technical know-how.

While some background in statistics is definitely helpful, a person’s expertise in the development of the model itself doesn’t have too much of an impact on how a project will succeed. It is required, however, to have at least one leader or manager to be proficient  in a formal approach to predictive analytics.

  1. Building Models Without A Plan

Diving into predictive analytics without a solid foundation or understanding happens more often than you think. Many businesses make the mistake of trying to acquire insights out of data without knowing the models that they’re developing. While it’s possible to end up creating a workable model even if the process wasn’t planned well enough, these models more often than not fail to answer the right questions, and thus can’t be implemented correctly.

While investing in training and following formal processes can be difficult to sell to leadership – after all, this would need a solid investment of resources – this should be thought of as an investment in the project’s blueprints, making sure that it doesn’t fail even before you begin.

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