The Estée Lauder Companies has created a chief analytics officer position as part of its senior leadership team. Learn about this important role and its impact on digital transformation efforts. From data science to culture, it's more complicated than you might think.
Data science and analytics are foundational to digital transformation. As analytics becomes more central to business decision-making, data governance and stewardship are essential parts of innovation.
The Chief Analytics Officer is a new role that can help companies create better strategies to link data scientists, business users, IT, and other stakeholders into a company-wide data ecosystem. Developing a broad culture of data and analytics in departments across the company is an integral part of the chief analytics officer role.
As I speak about digital transformation with business leaders in many industries, one clear message has emerged: competitive success depends on aggregating and interpreting data in sophisticated ways. Without data, there is no digital transformation. And without digital transformation, most companies are dead.
To dive deeper into the hidden mysteries of digital transformation and analytics, I spoke with the chief analytics officer at Estee Lauder, Sol Rashidi. She is a seasoned exec and has had senior data roles at Merck, Sony Music, and Royal Caribbean.
Our conversation was episode #710 of the CXOTalk series of conversations with people shaping our world. Watch our entire, in-depth conversation in the video above and read the complete transcript.
Check out edited comments from Sol Rashidi, Estee Lauder's chief analytics officer, below:
What is a chief analytics officer?
[The role is] around aggregation; consolidation; first-party, second-party, third-party data ecosystems; connecting information, not just collecting it; data quality; data fidelity; and what I call the defensive playbook. It's all about the backend ecosystem that's going to support insights and analytics.
The offensive playbook is analytics that you're going to derive from the data. Insights that you're going to generate from the analytics.
We're not collecting data just for the sake of collecting data. We collect data to do something with it. We're generating analytics and insights.
How much data do you collect and aggregate?
Let's collect any and every data element, data set that we can get our hands-on, whether it's first-party, second-party, or third-party. Or you can be use-case driven: I have the use case and need data to support it.
I've managed both. I don't think either is ideal. If you take a very use-case-by-use-case approach, you're always going to be limited to the data sets that support that use case. I don't think that will give you the insights, necessarily, because the breadth, depth, and span of data sets you have by default are limited to existing use cases.
[But,] just being a data hoarder doesn't solve the problem. There has to be a rhyme or reason.
I've gone from "Let's go data hoarding" to "Let's support use-case-by-use-case," to now "Let's prioritize the data sets that we think are going to run our business in the future."
[Business leaders] don't care about decisions being made behind the scenes. They better results in terms of timeframe, cohesiveness, comprehensiveness, and understanding their business.
How do you align data science and analytics with business areas like the Finance Department?
One, build relationships and find people who are willing to have just another 30-minute meeting with you, or have an hour meeting with you, and tell you the way things are. Go to them for coaching, counseling, and guidance.
Second, when you have a use case, business problem, project, or program, use that opportunity. You've been invited to the dinner table. You officially have an invitation. The more you express interest in learning their business, the better off you'll be. Build the relationships. Use your councils.
Third, sit with their finance team. The numbers that they eat, live, and breathe. Attend the financial meetings. Sit with them and understand their models. Where are they pulling the data sets from? What are their sources of truth?
How important is organizational maturity around data and analytics?
It helps to know which teams need a one-stop-shop approach towards analytics versus those that can be self-service or autonomous and on their own, tapping into you for best practices or ideas or opportunities to partner. This helps with capacity planning.
It also helps me understand which teams to partner with and which ones not to partner with.
If there's no investment, no talent, and they're not giving time, it's hard to invest in a team like that because you don't know if it's lip service. You don't know if they're too small for the organization and you probably shouldn't prioritize there.
[On the other hand,] if a team is not necessarily producing the most, however, they've made the investments, they have a team in place, they just need to accelerate what they're doing, well, that's a team you can support. You want to help them in their journey to growth.
It helps me with capacity planning because we can't be everywhere at the same time.
Advice for business leaders
Embrace your D&A [data and analytics] team more. Loop them in. It could be weekly status meetings. It could be quarterly reviews. It could be when you're reviewing major strategic initiatives that you have to unlock or activate. Bring a member of your D&A team into the fold because you'll be amazed.
CXOTalk offers in-depth conversations and learning with the world's top leaders in business, technology, government, and education. Thank you to my research assistant, Sumeye Dalkilinc, for assistance with this post.