How business leaders can make a data-literate culture stick

How the C-suite can scale a data-literate culture

Some organizations arrive at data literacy early. Cellular Intelligence, formerly known as Somite AI, a TechBio company, builds foundation models to help scientists understand, predict and ultimately control cell behavior. Its founding team includes PhDs with backgrounds in AI and developmental biology.

"It was not hard to instill a data-driven culture," said Micha Breakstone, co-founder and CEO of Cellular Intelligence. "That's how we are wired."

But for many businesses, shifting to a data culture requires change management. Employees must understand the benefit to their work, and leadership must offer support when it's needed. An in-house data advocate can smooth the transition.

Andrea Malick, a principal advisory director at Info-Tech Research Group's data and analytics practice, said an organization should have a passionate and visible "face of data" to lead the transition. That point person should have the resources and the mandate to plan a consistent, strategic data literacy program, she added.

With that leadership in place, practitioners point to a handful of fundamentals a data literacy program should have to ensure it takes hold across the business.

1. Use plain language, not jargon

A data literacy program won't work if it is too technical for employees. Consistent, clear language helps teams align on what metrics mean and how to use them.

"Leaders should not only champion, but model, a culture where everyone speaks plainly, without buzzwords, and where they feel comfortable asking others to clarify unfamiliar terms," Malick said.

Breakstone noted this as a challenge for Cellular Intelligence. Machine learning specialists and biologists at the company must speak the same language regarding data. Even basic concepts, such as "context", can mean different things to people with an engineering background versus those steeped in pure science, he said.

In her work building data literacy programs, Malick advised leaders to commit to a common, enterprise-wide language, especially as AI tools and terminology change quickly. Even an enterprise glossary or short video tutorials can help employees view data as a shared asset, not something owned only by IT, she said.

2. Match training to the audience

Data literacy training programs can offer too much too quickly, overwhelming participants.

"People can absorb only so much at a time," Malick said. "Find the core narrative with two or three main takeaway messages for each session and build the content around that."

Another pitfall is that program designers assume the audience knows more about the subject matter than they actually do.

Training programs aren't always built in-house; some come from external providers. Malick noted that some companies she worked with attended weeks-long data and AI trainings, but the provider assumed participants has a baseline knowledge level.

"They felt left behind immediately," Malick said.

Considering this, she recommends that literacy programs "Start at the absolute beginning."

3. Keep training practical

Data literacy is as much about practice as it is about attending tutorials or completing certificates, Malick said. She added that lessons must be relatable so employees can start using their new skills in their day-to-day work. Adding learning groups or training communities provides more opportunities for people to practice and discuss what they've learned, she said.

"If you're not actually applying what you learn, then you're still not a data-driven culture," Malick said.

4. Make it interactive

Building data and AI fluency in an organization depends on behavioral change, Cornelissen said. Specifically, employees must change the way they think about their work and how they accomplish tasks. But organizations will find it difficult to make that happen when training fails to engage participants.

"If you just have video content, the number one challenge is that a lot of people will just not do it," Cornelissen said. "People don't want to sit through hours and hours of video content."

Interactive components let people work with the tools as they are learning. Actively solving challenges with AI assistants, such as Microsoft Copilot or Anthropic's Claude, also makes a difference, Cornelissen said.

"You come out of the program with the confidence and the actual skills you want to build," he said.

5. Shift the mindset, not the tools

Enterprise-wide change management is a key factor. Getting executives on board with data literacy is a crucial step.

John Spens, director of data and AI for North America at Thoughtworks, a consulting firm, said he convened a meeting of CDOs a few years ago to compare what was holding back analytics adoption. The key takeaway: The problems weren't platforms or catalogs -- they were organizational behaviors and decision habits.

"There was not one technical conversation in that session," he said. "Nobody talked about Amazon or Google or Databricks or catalogs or any of that."

Instead, the CDOs discussed how stakeholders failed to embrace data experimentation, Spens said. That approach involves creating a hypothesis and testing it with data. If data disproves the hypothesis, then the organization adjusts assumptions and changes its actions.

"They were all just focused on the fact that their business executives weren't using the numbers, weren't thinking about test-and-learn, weren't thinking about all the benefits of being an insights-driven organization," he said.

A philosophical shift is especially important for organizations investing in data modernization. Without broad adoption, even the best data platform will fail to deliver its full value.

"If that mindset doesn't happen, then you create this amazing buffet that nobody shows up to," he said.

The long-term benefits of data literacy

Businesses that push through the obstacles and build a data-literate culture will gain several advantages over their competitors.

A growing appetite for data creates request backlogs that data and analytics teams can't keep up with. A data-literate workforce can take some pressure off these teams by handling basic interpretation and analysis. Data literacy also helps leaders explain what they need from the data team, reducing confusion about priorities and whether the team is answering the right questions, Malick noted.

In addition, AI deployments tend to perform better with a data-literate workforce.

"In many organizations, there's an awakening around the need for data literacy almost as a result of the need for AI fluency," Cornelissen said.

Reliable AI deployments depend on more than tools. They require strong data quality, governance and workforce literacy. DataCamp's report found that 21% of surveyed leaders reported significant positive ROI from AI investments, rising to 42% among leaders who said they have a mature, organization-wide data literacy upskilling program for all employees.

"Our research is very clear," Malick said. "Organizations that invest in data literacy significantly outperform those that don't."

John Moore is a writer for Informa TechTarget covering the CIO role, economic trends and the IT services industry.