4 reasons why most data science projects fail

Experts have called 2017 the year of data literacy and digital transformation. While data is a key component that drives true digital transformation, too often companies approach data and analytics projects the wrong way. In fact, a mere 13% of data and analytics projects reach completion, and of those that do, only 8% of company leadership report being completely satisfied with the outcome.

Why are data science project results so dismal? 

Most failures can be traced back to four major pitfalls: starting with the wrong questions; using faulty data; weak stakeholder buy-in; and lack of diverse expertise.

Recognizing these common hazards upfront puts CIOs and IT Directors in a much better position to lead data science projects that drive valuable insights, and contribute to an organization’s overall successful digital transformation.

All too often data science projects begin by analyzing data with the expectation that an interesting insight will reveal itself and become the basis for the business case that justifies the transformation.

This “exploratory analysis” approach often generates dozens of potential data projects that could yield compelling results. But do any of them produce a strong business case that can, for example, reduce costs, encourage repeat customers or retain employees? The scope of an “exploratory” project is simply too broad to drive useful analysis and is a waste of IT resources.

The better approach is to initiate the project with an established goal that maps directly to creating business value. Projects that follow the “hypothesis-testing” approach begin with a specific set of clearly-defined questions that indicate which data should be analyzed.

This targeted approach streamlines the data mining and analysis process by pairing business justification with business action, thereby directing IT resources to the information most likely to produce credible and meaningful findings. Starting with the right question sets the stage for a successful data science project through increased accuracy and efficiency, resulting in purposeful insight.

Using accurate data is fundamental to a project’s success, but bad data is the most underestimated cause of failure. Oftentimes companies simply do not spend enough time cleansing data.