Canadian culture revolves around a healthy lifestyle and few national brands deliver on those values the way Loblaw does. Food, health and wellness, fashion and beauty, and financial security are all at the heart of Loblaw’s brand and values. Lurking just beneath the surface is a highly sophisticated data and analytics capability and community that is making Loblaw into a world-class analytical competitor. Industry veteran Paul Ballew, who leads the effort, will share how he is helping Loblaw apply science to a venerable and dominant brand.

Key Takeaways

Describe your title and role.

I'm the Chief Data and Analytics officer for Loblaw. I'm responsible for all of our data activities, infrastructure, and analytics functions, both the day-to-day support that we provide for the business and business insights—descriptive analytics and alike—and the data science teams. We also run the loyalty program for the company.

Where does the analytics function sit in the organization and who does it report into?

We report directly to the President and Chief Operating Officer, Sarah Davis. She's responsible for all of the businesses under the holding company.

Is that the right place for the analytics function to report?

When you think about the scope of what we've signed up to do—support the business comprehensively on cost, effectiveness, and efficiency; redesign our loyalty program; customer-facing activities; marketing effectiveness; the full remit on top of the data organization as well—it makes sense that we report to an officer, either the President/Chief Operating Officer, the CFO, or some capacity along those lines. The maturity of data analytics teams has gotten to the point where their impact is broad. They're not just focused on a narrow set of use cases like we used to see when we reported to the Chief Marketing Officer or a similar function.

Describe the organizing model you have adopted for analytics.

I believe that the true model going forward to achieve scale and the full impact and objectivity that you want a data and analytics organization to have, while still being connected to the business, is a hybrid model. We're a centralized organization with our own profit-and-loss statement because we're responsible for external data-and-analytics monetization. But we also have resources dedicated to the individual lines of business, which means we're not losing the connection point and the business context. You want the scale and sophistication of the science, but if you're decoupled from the business, you desensitize yourself and won't get the full impact. So, the hybrid model is where I see the world going. We have about 600 team members. We brought most of the analytics community together in my team, so there may be another 50 or 75 team members outside of our organization who work closely with us.

What are four or five important qualities and behaviors of analytics leaders?

1. Synthesize and generate insights. The technical side of our jobs has become ever more complex because of what's going on in data management and, of course, analytics and data science. You need to understand the technical elements so you can synthesize and generate insights.
2. Business engagement. Relationships do matter to drive partnerships and adoption, and to connect with the business on a personal level, as well as a business level.
3. Connect and influence. Our work increasingly includes transformational elements in business, which require process redesign, change management, and changes in decision rights.
4. Evangelism. You have to have a sense of mission, purpose, and energy. Laying out the strategy, helping people see themselves in it, helping them see the importance of the mission, and then bringing it all together.
5. Good business person. Because we're generating revenue for the business with massive budgets and large capital investments in infrastructure.

How do you measure performance and success when it comes to analytics?

We have hard targets with our businesses that we co-own, whether they're financial targets, or KPIs, or other outcomes or objectives. On top of that, engagement measures matter a lot. We're in the knowledge business, therefore, our assets go home every night, and we have to be able to recruit and retain and develop and nurture. We also have some other things we're trying to track against. Sometimes they're external benchmarks. Are we truly on the cutting edge and keeping up with our peers in the field, across all the dimensions that matter? And then, if you have a budget, you have to come back to that as well. Are we achieving our budgetary objectives on the cost side? On the revenue side? In the case of Loblaw, we have to perform against how a legacy company thinks and operates, which is heavily around hitting those targets with the business and then hitting our own budget and talent-development targets.

Helping these venerable companies go through transformation is more exciting to me than being with the digital natives. The digital natives start with the science, and they put the business on top of it. We have an established business, and we're trying to put science in a meaningful way into it to help it have another 100-year success story. The key is to adapt to the best of our ability to the culture and become that agent of change, which is really what the companies are asking us to do.

For more insights from Paul, listen to the full podcast.