By Jan Kestle, President, Environics Analytics
Every organization is looking for better outcomes. They want smarter decisions, stronger performance and results they can explain and defend.
Today, many leaders see artificial intelligence (AI) and advanced platforms as the path forward— and they can be. But too often, an essential truth gets lost along the way.
AI is only as good as the data behind it
When data are incomplete, outdated or poorly governed, the insights will reflect that. So will the decisions. And once those decisions are acted on, the cost of getting it wrong rises quickly.
This is not, at its core, a technology problem. It is a data quality problem.
Canadian businesses are at a pivotal moment. The tools, data and technologies are in place. Without orchestration and without the right partners, the opportunity is lost. It is time to move beyond fragmented systems and embrace a Canada-first approach to data collaboration that aligns with our ethical and privacy standards, regulatory expectations and business realities.
Quality Data are the Foundation of Trust
For decades, Canadian organizations have relied on data to guide strategy, manage risk and measure progress. That hasn’t changed.
What has changed is the speed. It’s the scale and how quickly insights now move into real-world decisions and automated actions.
Data no longer sit quietly in reports. They shape pricing, resource allocation, customer engagement and public policy. Increasingly, data drive decisions without a human ever seeing the input.
That reality makes trust essential.
Innovation and trust are not trade-offs. They reinforce each other. High-quality data must first be comprehensive and represent the full picture rather than convenient slices. They also must be reliable and managed consistently so leaders know what they are looking at and why. They reinforce each other.
Just as important, data must be safe and secure. Privacy and security are not checkboxes. They are expectations. Finally, data must be used transparently, in ways that can stand up to scrutiny from regulators, partners, boards and customers.
These ideas are not new. Canada has long been a leader in responsible data use, including Privacy-by-Design, first articulated in Ontario and now recognized around the world as a best practice approach and codified in regulations like the GDPR.
What is new is how quickly poor data quality can turn into real business risk, especially once AI is involved.
AI Raises the Stakes
AI can be powerful. It can also be unforgiving. When organizations feed AI systems with data that are biased, incomplete or poorly understood, the outcomes are predictable. Insights are misleading. Recommendations look precise but rest on shaky foundations. Decisions become harder to explain and even harder to defend.
In a recent Data Privacy Day reflection, I emphasized that, as AI evolves, organizations must be able to prove that privacy, quality and innovation strengthen one another rather than compete with one another.
That proof is no longer theoretical. AI is moving beyond analysis into decision support and automation. At that point, trust stops being a “nice to have.” Boards want answers. Regulators expect clarity. Customers assume there is responsibility and accountability.
Leaders are expected to know where their data came from. They are asked how the data are protected. Whether the data are accurate. And whether they can be relied on to support decisions with real consequences. When that confidence is missing, AI initiatives stall. Or worse, they move ahead anyway and create unintended outcomes that cost far more to fix later.
Adding More Data Is Not the Same as Better Data
One of the most persistent myths is that volume solves quality. That more data automatically means better insight. More data often means more noise, more inconsistencies and more opportunity for error. Without clear standards, strong governance and human oversight, scale simply magnifies weaknesses.
High-quality data are deliberate. They are selected, not accumulated. Data are maintained, not assumed. And are constantly tested against reality, especially when conditions change.
This matters in Canada, where organizations frequently blend public, private and partner data. The responsibility to ensure that this data work together—and work safely—does not disappear just because the sources differ. This is why having a partner who has the experience and expertise to blend the data safely and appropriately is critical.
Platforms Don’t Fix Bad Data
Many organizations invest heavily in platforms, cloud environments and new AI tools. All of them promising speed, scale, revenue and insight. Platforms matter. But they are not magic. Platforms simply amplify what you put into them. Good data create momentum. Weak data accelerate problems. If the data are strong, responsibly sourced and well governed, platforms unlock real value. If they are not, technology helps organizations move faster, often in the wrong direction.
High-quality data require discipline. Standards for accuracy and completeness must be clear. Data need regular validation, not one-time cleanup. Governance must span both first- and third-party sources, especially as data ecosystems grow more complex. Privacy and security cannot be bolted on later. They must be built in from the beginning.
This is why, at Environics Analytics (EA), we have always treated data stewardship as a business responsibility, not just a technical one. Technology supports the work. It never replaces accountability.
Expertise in Data Collaboration
EA offers the expertise, infrastructure and global partnerships to help businesses blend and activate their first-party data securely with other first- and third- party data, ethically and effectively.
Into this rigorous framework, we bring the best and most appropriate technology from global partners including LiveRamp, Esri, Snowflake, AWS and Microsoft. To be part of our Data Collaboration Services offering, this technology not only has to be best-in-breed, but also fully aligned with our corporate ethics, data governance and privacy principles. As such, we only use the products, components and services we know fit our Canada-first approach. For example, we don’t use a central identity graph, which means we can better protect consumers’ privacy by not allowing the connection of individual digital profiles and behaviours without their consent.
Canada Has an Opportunity to Lead
Canada’s data and privacy landscape is often described as complex. At EA, we see it as an advantage.
Our strong privacy culture, clear expectations and growing focus on responsible AI give Canadian organizations a real opportunity to lead with data that is both powerful and trusted.
In work done with business leaders and privacy professionals across the country, there is broad agreement that respectful treatment of data is essential to long-term success. Not a barrier to it.
Organizations that embrace this mindset are better positioned to use AI with confidence. They build stronger, more durable trust with customers and citizens. Their decisions hold up over time, even as conditions change.
Just as importantly, they turn insight into action without introducing unnecessary risk.
The Bottom Line for Data Leaders
For Chief Data Officers and senior data leaders, the message is straightforward.
Start with quality and trust, not tools. Treat privacy, security and governance as drivers of value, not constraints. Be honest about what your data can support and what it cannot.
Above all, remember better outcomes always begin with better inputs. AI and advanced analytics can be extraordinary assets. But they only deliver exceptional outcomes when they are built on data that are comprehensive, safe and worthy of trust. That has always been true. It simply matters more now than ever.
Contact us at environicsanalytics.com for more information on how we can help you with your data strategy as it feeds into technology platforms.
President and Founder of Environics Analytics, Jan Kestle, has been a leader in data and analytics for over five decades. An expert in using statistics and mathematics to solve social and business challenges, she has worked with hundreds of organizations in all sectors to support their data-driven decision-making. Jan is a former member of the Canadian Statistics Advisory Council and is the Dean’s Advisory Council Emeritus for the Ted Rogers School of Management at Toronto Metropolitan University, and on the advisory boards of the DeGroote School of Business at McMaster University, and the Morrissette School of Entrepreneurship at the University of Western Ontario. She holds a Bachelor of Science degree in applied mathematics from Western University and is a recipient of an Honorary Doctorate from Toronto Metropolitan University (TMU).
