Aspiring to Be a More Data-Driven Company? Start by Changing the Mindset

Scaling AI Claire Gubian

The Business Transformation team at Dataiku operates as an internal consulting team, working to do a myriad of critical tasks, with the most notable one being advising customers (including our biggest ones!) on their AI transformation strategy, from theory to actual implementation. We meet hundreds of customers from different industries, geographies, and levels of AI maturity, which gives us a unique perspective on what truly makes a difference when it comes to transforming every employee, leader, and executive into an AI believer, which is what we’ll unpack in this blog post.

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What Do We Mean by "Changing the Mindset"?

These days, the power of data can pretty much be applied to enhance any business decision or process. But does everyone truly believe that or understand what it really means? When we talk about transforming everyone in the enterprise into AI believers (and, eventually, builders and consumers), it comes down to changing the mindset — the system of belief tied to data and, ultimately, the ways of working and making decisions. 

We know that the majority of today’s organizations are on a transformation journey to become more data driven. According to Accenture research, “AI Achievers” — the companies that are the most AI mature — enjoy 50% greater revenue growth, clearly outpacing their competitors. In the next sections, we outline two key strategies for organizations to keep front of mind on this journey that share the same common thread: driving the mindset change.

Change Needs to Be Led From a Top-Down and Bottom-Up Perspective

For starters, executive sponsorship and data fluency are paramount. Data needs to be part of the company strategy or, at the very least, woven into strategic initiatives. This buy-in not only helps project alignment but also helps boost program visibility, exposure, and word-of-mouth advocacy. Next, those involved should acknowledge the current culture of the company and how data is perceived. Is it a natural fit or is it a stretch? This can vary depending on the industry (how digital is it to start with?), the company values (is continuous learning a core pillar?), the teams, executive understanding of data-driven business outcomes, and more. 

It’s also important to note that a sustainable data culture cannot be achieved if data teams are siloed and not closely working with the business. Some tips to help change that include:

  • Making business teams data fluent through use case hands-on training and dataset comprehension.
  • Developing self-service analytics so business teams can visualize data, generate insights, and simple models on their own.
  • Encouraging collaboration between data practitioners and business people.
  • Ensuring projects owned by data scientists answer identified business problems and have a solid business case attached to them.
  • Aiming for a hub and spoke organizational model, where data teams are embedded directly in the business, with a central team driving best practices, standardization, and license management.

Further, there will always be employees who are more keen to embrace change than others — these will be your early adopters. They will be the ones who like trying new things, new tools, and have a growth mindset. Ideally, you want a blend of newer and longer tenured employees. In order to make a strong case internally, secure initial wins and success stories and then communicate, communicate, and communicate to inspire others. Set up a data community to foster best practices, knowledge, and success sharing and, importantly, invest in internal learning programs. Changing the mindset will undoubtedly require multiple touch points and repeat content. It should be a balance of theoretical content and hands-on training. 

Address the Rational + Emotional Components to Driving Change

So, for starters, how should you appeal to the rational side of things? Clearly articulate why embracing data at all levels will help drive more business value and relevance. Start measuring progress against becoming a data-driven organization by making the concept less fuzzy. You can do so by identifying KPIs to measure a baseline and progress made against it. A few examples of KPIs include:

  • Percent of adoption and frequency of use of self-service data tools (i.e., one Dataiku Latin American customer has a goal of 90% adoption of their addressable market by 2024)
  • Total business value generated by projects leveraging data and analytics 
  • Percent of business processes that incorporate data and analytics (i.e., one Dataiku global pharmaceutical company has defined a goal of 80% by 2025)

Then, when it comes to the emotional side of things, explain why embracing data will help augment people and not necessarily reduce headcount. Explain why everyone should be excited about the opportunity to spend less time on manual and repeat tasks and, finally, start renaming data literacy programs to data fluency programs — it’s much more appealing that way! 

At the end of the day, for any organization to make their AI program a success — so much so that it is less of a formal program and actually fades in the background and becomes entrenched with the organization’s business model — they need to start by driving the mindset change. Whether an organization is just starting out on the mission to improve and accelerate its AI maturity or is determining how to scale a specific element such as governance or talent, we hope you find these steps helpful to keep in mind when driving short-term wins and long-term transformation.

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