YYC DataCon focuses on what happens after the AI plan gets approved

A funny thing has happened to a lot of data teams over the last couple of years. 

They became AI teams.

Mirna Damergi, co-director of YYC DataCon (alongside Graham Doerksen) and chair of the YYC Data Society board, has watched that happen from inside Calgary’s data community. 

And she sees it as an advantage, with practitioners bringing experience with the data foundations underneath AI.

As companies move AI projects from experiment to essential tools, she’s hoping DataCon is where they tell the story of how they’re moving forward, to other organizations that might not have this similar expertise on staff.

The data community-led conference returns to Calgary on Sept. 11 at BMO Centre with more than 30 speakers and four tracks covering technical deep dives, industry innovation, leadership strategy, and governance, privacy, and trust. 

YYC Data Society has run YYC DataCon as its flagship annual conference since 2020. Local data meetup organizers originally created the society after realizing their groups served overlapping audiences and competed for many of the same resources like venue spaces and time.

The event’s 2026 theme, “From Insight to Impact: Doing Data Right,” puts much of the focus on what happens after organizations decide to build. For Damergi, that starts with a fairly simple end goal.

“We really want people to walk away with a solution,” she said. 

That could come from a session, but it could also come from finding someone in the hallway who has already dealt with the same problem.

“We’re talking about 700 people walking the hallways with their own experience and thoughts on where things are going,” said Damergi. “

These hallway conversations are useful. We might not hear about them, but just knowing that there is an impact in the community is what makes it worth it for us to keep on doing this.”

Getting closer to the implementation

As data groups take on more AI work, Damergi explains that practitioners often find themselves trying to understand decisions that arrive from higher up in the organization.

A company can approve an AI strategy, set a budget, and start moving. Someone still has to decide whether the underlying data can support the plan, how to govern it, who owns the system once it goes live, and whether the thing solves the problem everyone thought it would.

DataCon has four tracks to follow that path from different directions.

The technical track gets into data engineering, architecture, analytics, AI, and machine learning. Industry sessions look at how organizations apply those tools across sectors. The leadership track covers data strategy, ROI, decision-making, and change management. The governance track tackles privacy, trust, and the controls organizations need as they scale.

Damergi has seen what happens when companies skip some of that work. Basically, you can build fast and cheap now, but eventually, a bill (either metaphorical or very much monetary) is going to pop up.

She gives the example of a company training a model on data that someone failed to clean properly or examine for bias.

“It has a time cost,” she said. “It has a resource cost, and sometimes it has even a result like an outcome cost.”

Organizers received over 114 applications and chose 19 speaking proposals, alongside panel participants. They scored the talks partly on whether attendees would leave with something useful.

“Not all of our speakers are famous, and that’s on purpose,” said Damergi. “That’s the whole point, showcasing the talent.”

Speakers tapped for this year’s event include Michael Nar, director of data and risk at Helcim; Olaoluwa Balogun, data and AI support engineer at Microsoft; Tessa Peterson, director of advanced analytics at Wawanesa Insurance, and Tarah Lynch, program lead for genomics and bioinformatics at Alberta Precision Labs.

This year, the event also moves from the broader three-day format Digital Journal covered last year, to one concentrated day. 

Organizers have added a workshop stream, including sessions on networking, data fluency, and context engineering (deciding what information an AI system needs to complete a task effectively). In the latter, participants will work through a problem and build an AI agent for analytics.

That makes the 2026 version of DataCon feel closer to practical problem-solving.

It also says something about the role DataCon wants to play outside the conference itself.

Turning local expertise into practical answers

While Damergi tells me that Calgary’s data scene isn’t as large as other tech hubs, it’s still a thriving presence.

“Why we run DataCon is to spotlight the talent, showcase what Calgary is doing in the data ecosystem, in the data scene, and just put Alberta and Calgary on the map of the data world,” she said.

Calgary’s startup ecosystem has grown at an annual rate of nearly 40% since 2021, compared with a Canadian average of 9.6%, according to Startup Genome Global Startup Ecosystem Report data cited by Calgary Economic Development. 

More companies, investment, and technology work create demand for people who know how to build and operate the systems behind that growth.

Damergi sees YYC Data Society and DataCon as places where those people can find each other.

If an organization needs someone who understands a particular data problem, she wants the community to help connect them with a person who has already worked through it. 

That could mean an expert, a future employee, or simply a peer willing to compare notes.

“What we really want to tell the world is you can find talent here,” she said. “But what we also want to tell others is there is a place in Calgary for every organization that is doing data.” 

Growing an ecosystem requires companies, capital, and technology. It also requires spaces and events where the people doing the work can find each other before they repeat the same mistakes.

“At the end of the day, there is nothing that replaces the human interaction,” she said.

Final shots

• As data teams take on more AI work, they also inherit the questions about cost, risk, and whether the data is good enough to support it.
• Building fast can save time upfront and create a much more expensive problem later if the data underneath the system is weak.
• Calgary’s growing tech ecosystem will need more places where people can compare notes before every organization learns the same implementation lesson the hard way.