In this episode I sit down with Erika McBride who looks after all things analytics at the giant material science company, Dow, Inc. Like many of my prior guests, Erika manages the analytics and data science function, but doesn't actually have a data science background. Instead, her main focus is on how analytics translates to business outcomes. You'll hear some great slogans in this episode like, "translating data into profit," and "ringing the cash register at Dow." Erika shares how leadership viewed analytics when she first joined the company, much like a science experiment, and how she has been able to turn the culture around to a highly successful venture inside Dow today.

Key Takeaways

Describe your title and role.

My title is Global Director of Analytics at Dow, Inc. The scope includes data platform capabilities, BI visualization, advanced analytics (AI and machine learning), information quality, data governance, and business consultancy. I'm also the business IT director liaison to our coatings and monomers business. I sit on that leadership team, and prescribe and advocate for the best technologies to help the business achieve their outcomes.

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

We're organized within the Information Systems (IS) function, and I report in through the CIO.

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

It's an interesting question, and it speaks to the relative infancy of analytics as a discipline. You can make arguments about why it should be organized differently, but for us, it works to have the function within IS.

Describe the organizing model you have adopted for analytics.

The centralized function acts as a feeder service to various analytics teams across the company. So, for example, we have data scientists in manufacturing, data scientists in supply chain, and data scientists in R&D. This is a good mix in that we can stay close to our businesses and functions through the federated model but then get some of the scalability through the centralized piece.

My team is around 80 people globally, which includes data engineers, data platforms, BI experts, and data scientists. In addition, we established a community of practitioners that is close to 400 people. We're proud of what that community has done to bring folks together, share best practices, and share technologies and solutions.

In addition, one of the things that I'm most proud of was launching the Analytics at Dow Storefront. We now have a single storefront with many views where all analytics is housed. So, if a sales leader visits the store, it will provide the top five analytic solutions that can help the sales leader in that role.

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

1. Passion for the people. My number one role is to support and develop the people who will then execute on business priorities. We can't make progress without focusing on developing and supporting the team members.
2. Strategic vision. This includes the ability to paint a picture of where we need to go analytically, align resources, and work to execute that vision. I recently introduced a vision called "analytics everywhere," which is to make our analytics so intuitive and so ingrained in work processes that folks don't realize they're using analytics, but their decision-making is better and faster as a result of the analytics.
3. Business acumen. Understanding business challenges and acting as a translator. Translating what the business needs are to analytics and vice versa. Certainly, some technical acumen is important because we need to understand how prescribing the right analytics solutions help solve business challenges.
4. Lifelong learner. This field is moving so quickly, it's important for leaders in this space to be curious and always learning about what's happening through industry consortia and research, etc.

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

When I came to Dow seven years ago, I asked stakeholders what their impressions were of the advanced analytics team that I was leading at the time. Their impressions were, "Oh, they're really smart and they work on a lot of academically interesting projects, which is cool." I thought to myself: If we don't show value, we're not going to be around a whole lot longer. So, we pivoted the team toward a new mission called "translating data into profit." We started tracking business outcomes in dollar terms. The downside of that was that folks got too hung up on the number. Having a specific number invites challenges to the number, especially because there are so many other things that go into decisions besides the analytics, like business knowledge.

Now, we still focus on translating data into profit but no longer with a bottoms-up calculation of the exact dollars that our analytics are contributing. We look at it more holistically. We want to be an essential partner to the business. Now our performance measures include senior-level testimonials.

We also use Tom Davenport's book, Competing on Analytics, to help us establish benchmarks. It has an analytics maturity model that we use to answer where we are in terms of leadership engagement and analytic talent, etc.—to see how we're growing in maturity. It's helpful to periodically step out of the weeds and look at ourselves holistically.

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