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March 19, 2025
How Are Data-Driven Cultures Built?
Human factors — culture, people, process change, and organizational alignment — remain a barrier to establishing data-driven organizations for 78% of respondents, according to Wavestone’s 12th annual data and analytics survey.
In comparison, the survey of over 100 Fortune 1000 executives in data, analytics, and AI leadership positions showed only 23% citing technology limitations as a principal challenge to becoming data-driven. The survey found that 57% of organizations still struggle to build a data-driven culture.
In a blog entry, Krish Das, VP, enterprise data officer at Ulta Beauty, offered seven tips to help retailers and brands build a data-driven culture:
- The data journey should be a business-driven journey: Das advised, “You should never do something that is not in alignment with the overarching business strategy.”
- Have the right set of executive sponsorship: He wrote, “This is critical in determining how far your data program will go.”
- Don’t boil the ocean. Start showing value as early as possible: Das wrote, “This will help with demonstrating value quickly, building trust and sparking excitement with key stakeholders across the business.”
- Be honest about the foundation that you have, and work to get it right: Das stressed, “If you have a weak foundation — i.e., low quality data, lack of clarity around what data you have, lack of trust on data and difficulties with accessing the data in a timely manner — it is going to be difficult to talk ‘big game’ initiatives.”
- Never underestimate the need for change management: Das wrote, “Proper communications, training and upskilling, peer coaching, and collaboration opportunities are important for raising awareness and excitement, improving data literacy and driving adoption.”
- Be a king-maker, not a king: Das stated, “Find a business champion and put them on a pedestal. You must have the mindset that data leaders are enablers, and leave your ego at the door.”
- Have a “Teflon spirit”: Das wrote, “This is a difficult journey, and you should anticipate more downs than ups.”
Jared Coyle, head of AI at SAP North America, foremost emphasized the importance of gaining buy-in at the top.
“Make sure your leaders are open to the change, and that they are encouraging everyone to make decisions based on data, which means they have to show how they are making decisions,” he told Information Week.
Next, Coyle urged organizations to encourage and reward data-driven decisions across the organization. He added, “Make sure the data is available to all, and that you’re referring to it regularly. Lastly, you want to encourage everyone who has a role in creating great data, to be part of the movement.”
In a column for Harvard Business Review, Mai AlOwaish, chief data & innovation officer at Gulf Bank of Kuwait, and Thomas Redman, president of Data Quality Solutions, cited the importance of “building the new culture from day one,” even though it runs against the conventional wisdom of seeking quick wins to help build support from the team.
“Quick win efforts often take shortcuts, running roughshod over people and culture and increasing the likelihood that these projects fail,” the two wrote.
Other advice included getting “everyone involved,” prioritizing data quality, and having “persistence and courage” in building a data-driven culture. AlOwaish and Redman stated, “Expect to have some bad days, but keep the larger prize fully in mind.”
Discussion Questions
What are the core pillars of building a data-driven culture?
What are the root causes of failure in nurturing a data-driven culture?
Poll
Loading …BrainTrust
Lucille DeHart
Principal, MKT Marketing Services/Columbus Consulting
Neil Saunders
Managing Director, GlobalData
John Lietsch
CEO/Founder, Align Business Consulting
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Data is valuable. Being able to interpret the data and use it creatively is equally important. Good retailers do both.
As is often the case with these trendy topics, I think we may have a semantics problem: when I read that “human factors” are a barrier to “establishing data-driven organizations”, my first reaction is “And thank God they are!” (why would we want something where humaness is viewed, not as a positive, but some kind of obstacle to be overcome?) Which brings us to the question of what we mean by “data-driven”: I’m assuming – hoping – it means a system where objective measurements are used to supplement experience and intuition; but notice the order…the thinking must lead to the measuring, not the other way around. If “data-driven” is synoymous with mindlessly pursuing some metric(s), perhaps it’s best an organization not be so described.
Totally agree. Perhaps “data supported” is a better description. Start with a premise and look for the data that supports or doesn’t support it.Without the real world application of the data, it can be misleading. Much of the dialogue in the world lacks credibility because there are no facts to support it. Data can provide that back up, reinvigorating critical thinking skills which are essential in all aspects of business and life.
There are as many dangers in blindly implementing data driven decision making as there are in relying solely on intuition. The key to building a data driven culture is to stop thinking of it as a zero sum game. It’s not a black and white issue and there will be times when decisions will be made in the absence of perfect data requiring experience and intuition to properly interpret contextual factors to reach a very educated guess but a guess nonetheless.
The challenge is therefore to teach, train, support and empower our managers to make decisions in what is often an imperfect world. In other words, we must first create an environment that fosters decision making because no amount of data will ever overcome a blame culture.
I had to check the date on this article. I thought I stumbled onto a late 1990s copy of Computerworld. Good to know the basics haven’t changed.
Creating a data-driven culture can be challenging because of employee resistance and lack of data literacy. This has been the case in many companies we have worked with.
Furthermore, integrating data systems across different departments and ensuring data quality and accessibility can be significant challenges. The ability to overcome these challenges requires strong leadership, ongoing training, and a commitment to fostering an environment where data is valued and used to its full potential.
Integrating a centralized data management platform that facilitates seamless data sharing and collaboration across departments is one effective strategy.
Cross-departmental teams can also benefit from working together on data integration projects.
Furthermore, investing in automated data integration tools can help streamline processes and ensure consistent data quality.
“The human factor” vs. “a data-driven culture” presents a false choice. Successful companies gather and process plenty of data, but they still depend on human judgment to interpret that data and to make actionable decisions — at least, let’s hope so. Otherwise we are already living in the world of HAL 9000.
Learn to speak data. The primary foundation for building a data-driven culture is to integrate the necessity of data into running the everyday business and creating clear measurable goals. Saying that you want to be the biggest athletic wear brand is very different than saying you want to grow from 10% to 15% market share while increasing profits and then tracking those metrics across disciplines. Are stores driving more foot traffic/conversions, is marketing growing recall, awareness, repeat customers, is finance managing to a lower operating cost, etc? If an organization speaks data, then the employees will learn to as well or they will be left out of the conversation.
I agree with several of the other BrainTrust Members that data driven does not mean that only data is used for decision making. The best organization’s use a combination of the best data and human intelligence and logic to run the business. Companies need to continuously evaluate the impact of decisions to better prepare for the future.
Some of the key pillars of success include 1) Have timely and high-quality master data to start 2) Identify the best set of data, KPIS, and human intelligence needed for particular situations 3) Commit to being a learning organization to better serve the customers though test and learn and other technological approaches.
Consumers are frequently changing in the market at a rapid pace, without a commitment to continuous improvement and learning, retailers will be left behind.
Krish Das did a wonderful job summarizing the critical points. Another consideration is creating easily accessible self-service data platforms to drive utility, value and organizational cohesion. Sounds easy but delivery of this access / content is challenging given organizational culture, risk tolerances, identity access management considerations and varying levels of data literacy across teams. In my 12 years in AI, I’ve found that implementing tiered access models with progressive learning journeys dramatically increases adoption while maintaining governance standards.
The most crucial pillar when building a data-driven culture is data governance. Without clear guidelines on factors like data entry and key data capture fields, retailers are left with a lot of broken, unusable data that establishes a bad foundation for all strategies and tactics built on top of it. For instance, if a retailer is not consistently capturing product measurement data, when a customer searches for a 5×7 picture frame, they may get null search results because the 5×7 picture frame in your catalog doesn’t have the correct product attribution label.
To overcome this, retailers must shore up their existing data, removing duplicates, repairing broken data and updating missing information. Moving forward, they can maintain that new, solid foundation through rigorously maintained data governance standards and by using AI-first catalog-as-a-service solutions.