The 2026 business landscape is no longer focused solely on digital transformation, as a persistent data paradox begins haunting the boardroom. While enterprise leaders view basic data literacy as essential for day-to-day operations, 60 per cent admit to a profound skills gap within their teams.

This gap isn't just a technical oversight; it’s a structural operations risk. Organizations are rushing to adopt the latest artificial intelligence (AI) tools, only to find that their workforce lacks the foundational fluency to navigate them. At MacEwan University’s School of Continuing Education, instructor Shawn Ang teaches the Foundations of Data, a course designed to close this skills gap. This 14-hour course isn't about turning managers into data scientists; it’s about reinforcing the bridge between knowing and doing by equipping professionals with the confidence to turn raw data into strategic assets that close the gap on implementing business decisions.

The paradox: When AI fails confidently

The pressure to integrate AI has reached an all-time high. But as Ang warns, technology is only as good as the foundation it sits upon. "The biggest risk isn't that AI fails – it's that AI fails confidently," Ang explains. "When you have a workforce that knows how to use AI tools but doesn't understand the data feeding them, you get polished outputs built on a broken foundation. People stop questioning what the tool tells them because it sounds authoritative, and decisions get made on flawed data at machine speed."

The core importance of AI literacy and data upskilling lies in recognizing that human judgment and data stewardship are the most critical pillars in using the technology. AI does not solve data problems – it amplifies them. For an organization to truly thrive, it needs a workforce that understands the why behind the numbers before they ever hit a dashboard.

The red flag on your desktop

For many non-technical professionals, the barrier to building a data-driven culture is a lack of understanding of their own digital infrastructure, and a common fear of math – sentiments that are amplified by those who don’t work within math-informed fields like statistics or code. They may feel unqualified to assess if their systems are ready for the AI era. Ang points to a common red flag that exists in almost every office: The shadow dataset.

"A team has five versions of the same spreadsheet floating around, each slightly different, and no one can tell you which one is the right one," Ang notes. "What most managers don't realize is that those spreadsheets are the organization's database. When your business runs on scattered spreadsheets, you've essentially built a database with no structure, no governance and no single source of truth."

MacEwan University School of Continuing Education’s approach counters this by focusing on human-centred technology. "The first thing I tell learners is, ‘You already work with data every day. You just don't call it that,’" says Ang. "We use tools learners already know, like Excel, to show that the spreadsheet on their desktop follows the same fundamental principles as large-scale databases. Once that realization lands, the intimidation fades."

“The goal is to move from math fear to data curiosity. A key part of this is building a data-driven dictionary." Innovation often stalls because tech teams and business leaders don't speak the same language. By establishing a common vocabulary, managers can act as data translators, bridging the gap between technical requirements and business goals.

Solving the last mile breakdown

One of the more significant data literacy skills gaps is the last mile – the critical transition from viewing a report to taking business action. While many programs focus on technical data-collection knowledge, 72 per cent of organizational capability breakdowns occur in this final stage.

"Most managers look at a dashboard and take the numbers at face value," says Ang. "They never think about the journey that data took to get there, from the moment someone entered it into a system, through every transformation and handoff. This course pulls back that curtain."

By teaching the steps of data-driven decision making, from identifying objectives to sharing insights, learners can begin to recognize how foundational data shapes the analytics they rely on every day. This is where MacEwan University’s commitment to applied learning shines, providing a space where students don't just theorize about data, they learn to critically think about it.

The “aha” moment is built through ownership

By building a sense of ownership among learners, working professionals can view data as a shared responsibility that empowers their decision-making and leadership. "The 'aha’ moment I'm looking for isn't a technical breakthrough," says Ang. "It's the moment a learner stops thinking of data as something the data team handles and realizes that data is everyone's responsibility. It touches every part of the organization."

By demystifying the core concepts of data literacy, Ang and the School of Continuing Education are helping leaders view their business areas with fresh eyes, moving them from data avoiders to data champions. The 14-hour course aims to give both professionals and managers practical skills to stay competitive in a flexible format that offers an accessible first step in lifelong learning, helping them develop both technical and durable skills at any stage.