I’m very focused on enabling data-driven decision-making for the organization, giving everyone across the business access to the tools and the data they need to make effective decisions. Enabling data is key–it’s about instilling a data culture and ensuring everyone understands what data means and how it applies to their role. A key component of that is building data literacy so that everyone speaks a common language.
I’m also focused on bringing discipline to measuring the value of our analytics work. For example, we quantify the value of the recommendations coming out of our work and align those with the business to determine which are feasible. By tracking that, we can focus on both business adoption as well as driving real, measurable value from analytics.
We started with an outside-in perspective. We took time to understand other proven success stories and familiarize ourselves with the learnings that have happened along the way in this space. We then looked at how this might apply to our journey while recognizing that a cookie-cutter approach cannot achieve successful analytics transformations.
We are operating in a ‘build the plane while you fly it‘ mode, so we aren’t waiting for our data to be in a perfect state before we move forward. This is where some companies stumble–by prioritizing perfection over progress, there’s a high probability that you’ll never get started.
We’re also adopting a use-case-driven approach rather than a tech-first strategy. That means we focus on the different domains across the business where analytics can drive the most value. For example, that might be revenue growth, pricing, trade promotions, or marketing and media effectiveness. We choose a couple of priorities and then focus on building, embedding, and scaling.
The first thing I’d say is that the partnership with the IT organization is critical. For us, that means shaving folks working on data strategy and data operations that act as a day-to-day link with our IT experts. They make sure that we have the data we need to do the type of analytics work that we want to do. Of course, you cannot underestimate the importance of roles like data engineers and data scientists, and they need to work together in an ecosystem. We also need analytics translators for adoption to serve as a link between data scientists and the business teams. To succeed, we need to articulate what data means, how to use it, and how it solves key problems.
We’re also looking to foster important attributes, such as curiosity, across our entire team. Curiosity is important in terms of problem-solving and around best-practice approaches to learning, understanding what’s new and evolving in the analytics space, and how different approaches might set us apart.
Storytelling is also crucial. It’s never been more important for everyone within an organization to connect the dots in their work and weave it together to tell a compelling story that spurs action. The most successful analytics teams can clearly articulate what their work means and how it can impact the business.
There are two areas where we are focused that I believe will set us apart.
The first is that business link. Everything we do within analytics starts with a business question rather than a tech-first approach. We have to deeply understand what decisions our business partners need to make, when they need to make them, and what priorities are most important to their success. Then, we can help to solve some of those through analytics. The greatest successes within analytics come from embedding that work into the rhythm of day-to-day business operations.
Another is data storytelling. Of all the case studies I’ve seen around analytics transformation, as well as the transformations I’ve led myself in prior roles, the ones that are most successful are those that succeed at changing behaviors and getting those behaviors to stick. It’s about fostering a business-first approach that can be enabled through storytelling–especially as we start to link many different types of data. It’s not just about syndicated data or market share data but also first-party data and data partnerships with retailers. Being able to mine that data, draw relevant insights, and spur action is key to real scalable success.
Change management can be a big challenge–it’s the case in any organization where people are comfortable working in a particular way.
Building trust across the organization is incredibly important to solving adoption challenges. That starts with really taking the time to listen and understand your stakeholders’ priorities and their business problems so that we can address the most important areas for them. I invest in building relationships, whether that’s through one-on-one meetings, lunches, or informal chats. By communicating effectively and, most importantly, by listening, you can build trust from the beginning and, therefore, overcome a lot of skepticism from the outset. It’s also effective to find the curious, interested champions with whom you can partner for ‘quick wins’ before scaling up.
You also have to articulate your vision and strategy and where you’re headed very clearly and often, not just to your team and your analytics organization but also to the entire business. This means speaking in plain terms to demystify data and analytics and make it something that everyone understands.
We have our analytics strategy on a single page at Colgate, including all of our strategic pillars. It acts as a kind of north star that can be referred to constantly to communicate why we’re doing what we’re doing and to make sure that we’re focusing and staying on track with what’s most important.
We have our strategy, and we know where we’re headed, but we also recognize that the business and the analytics space will continue to evolve. We have to constantly look at what’s happening externally to make sure that we’re still taking the most relevant approach for our business. We need to stay current with emerging approaches, data sources, and technologies. We are most excited about more predictive and prescriptive analytics for the future and using AI to automate decisions that require human effort. However, it is always with the approach of tying it to a business question, then building, showing value, and scaling.