There’s rarely ever one answer to the question of why a given data analytics project might fail. It might be due to poor data, the wrong strategy, or cultural challenges, to name a few. But more often, an inability to achieve data analytics objectives can be tied to a failure to uncover the actionable insights that directly improve decision-making.

A recent article from the MIT Sloan Management Review looks at ways to overcome these common challenges, highlighting an approach called “decision-driven data analytics” as a way to ensure initiatives stay on track and deliver better business outcomes. Offered as an alternative to data-driven decision-making, the decision-driven data analytics concept is relatively simple: Instead of finding a purpose for data, find data for a purpose. By doing this, data analytics project leaders and data analysts start with the end in mind, making certain that they are asking the right questions from the start.

According to the article, “Data-driven decision-making anchors on available data. This often leads decision-makers to focus on the wrong question. Decision-driven data analytics starts from a proper definition of the decision that needs to be made and the data that is needed to make that decision.”

To help with the process, companies should look first at the relevant business decisions they are trying to make, who is making them, and what data is required. From there, companies should take an ordered and systematic approach to avoid pitfalls on the road to success, including: 

  1. Identify the alternative courses of action. The article encourages companies to think “wide then narrow” to achieve the best outcomes for data analytics initiatives.  By first considering many courses of action instead of one, companies remain more open to new ideas and possibilities. From there, leaders can narrow their focus to the areas that they can actually influence and impact. Taking this approach helps the company take “high quality and feasible courses of action” and execute them successfully. 
  2. Determine what data is needed to rank alternative courses of action. Understanding the exact data you need to make a decision instead of simply sourcing as much data as possible helps more accurately determine the courses of action you should take for an initiative. Before sourcing and collecting data, the article recommends going back to the decision you are trying to make, arguing that “Starting from the decision draws attention to unknowns, and this has a major advantage.”
  3. Select the best course of action. With your courses of action ranked, it’s time to choose your path. According to the article, “If the first two steps were executed well, data analytics will now reveal the best course of action.” By taking this approach, you can effectively consider more courses of action while ensuring you’re answering the right questions, and then making the best possible decisions based on the answers.

If you’d like to learn more, including some real-world examples, check out the entire MIT Sloan Management Review article.