Building a Data-Driven Culture: Three Mistakes to Avoid

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Building a Data-Driven Culture: Three Mistakes to Avoid

By Ganes Kesari | MIT Sloan Management Review | May 14, 2024 

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A company’s investment in multiple data and analytics projects won’t, on its own, result in employees using data insights to make their own decisions. This end goal requires leaders to take different types of interventions. And it’s hard work: In a recent survey of Fortune 1000 CIOs and data executives by NewVantage Partners, about 6 in 10 leaders acknowledged that they haven’t been able to establish a data- and analytics-driven culture.

Why is it so hard? How can leaders foster an environment where decision-making with data insights becomes a habit?

Despite a heavy focus on analytics tools and algorithm accuracy, most initiatives struggle to deliver actionable insights. Even when the insights shared are actionable, people are often not excited about using them. Finally, projects that do see meaningful adoption often lose steam after road shows end — and then die slowly. These are just symptoms of the problem, not the root causes.  Three core factors cause those symptoms: most initiatives are run as technology projects, users resist changes to ways of working, and it’s difficult to demonstrate analytics ROI.

Business leaders can address the factors outlined above by taking the following actions.

1. Fix burning challenges and ensure business ownership.  How do your data initiatives start? Identify key business function stakeholders, uncover their biggest priorities and pain points, and brainstorm ideas. Get clarity on the target users, and quantify outcomes: Will the initiative drive share of customer wallet or save inventory costs? Prioritize high-impact ideas that are reasonably feasible in terms of data availability, technology capability, and change management. Identify a business owner who will lead the initiative, be accountable, and influence user involvement — from project design through execution and ongoing usage.
2. Drive adoption with executive storytelling and gamification.  Successful organizational change begins with a clear, inspiring vision. The most effective transformations occur in companies where executives actively walk the data-driven talk. Leaders must communicate the vision and explain what it means for users — encouraging them to both adopt new habits and discard old ones.
3. Identify success metrics early and rally teams around them.  The best time to define success criteria is while you are evaluating a project idea. When prioritizing projects, leaders need to define expected business outcomes, how success will be measured, and the data needed to compute ROI. Often, this data isn’t readily available; you might need to collect new customer feedback or develop alternate employee productivity metrics. 

In reality, building a data culture is a marathon. Leaders who prioritize executive support backed with meticulous execution will succeed with this transformation. 

3 key takeaways from the article

1. A company’s investment in multiple data and analytics projects won’t, on its own, result in employees using data insights to make their own decisions. Why is it so hard? How can leaders foster an environment where decision-making with data insights becomes a habit?
2. Despite a heavy focus on analytics tools and algorithm accuracy, most initiatives struggle to deliver actionable insights. Even when the insights shared are actionable, people are often not excited about using them. Finally, projects that do see meaningful adoption often lose steam after road shows end — and then die slowly. These are just symptoms of the problem, not the root causes.  Three core factors cause those symptoms: most initiatives are run as technology projects, users resist changes to ways of working, and it’s difficult to demonstrate analytics ROI.
3. Business leaders can address these road blocks by:  fix burning challenges and ensure business ownership, drive adoption with executive storytelling and gamification, and identify success metrics early and rally teams around them.

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Topics:  Data, Transformation, Culture, Teams, Organizational Behavior, Leadership, Technology