Supporting growth

Schildhouse reports to Colgate-Palmolive’s chief growth officer. Her peers include executives responsible for digital transformation, supply chain, innovation, research and development, global marketing, strategy and sustainability. She says the organisational structure makes it easier to tie data to long-term aims.

“The farther away you are from the business, the harder it is to make connections and drive impact,” she says.

“What appealed to me here was the fact that analytics and insights were part of the business and growth area of the company. I thought that would position me well to drive value through the work that I’m doing.”

“You can’t build and scale all the exciting, advanced analytics solutions and everything with AI unless you have data foundations. Gaining value from all this data-enabled technology depends on key elements like data strategy, data governance, and a series of related topics”

Diana Schildhouse, Colgate-Palmolive

Almost five years into her work with the company, Schildhouse says it’s been an exciting and enjoyable ride.

“We’ve had lots of success in what we’ve done with our analytics and data transformations here,” she says. “It’s fulfilling to see that my amazing team is driving a lot of that success.”

Joining the company in a new role meant she had a blank page for analytics and insight strategy. She began by asking the business about its major challenges and exploring the potential of technology to help solve those concerns. As part of her efforts, she tracked and traced performance to ensure success.

“That’s something I’m obsessed with, because if we can’t show the value and impact we’re getting, then we could be building the most brilliant solutions and passing them over the fence to the business, but if they don’t actually use them, then we didn’t achieve what we were trying to do,” she says.

Whether it’s for pricing analytics, revenue generation, cost optimisation, or intellectual property creation, Schildhouse has developed frameworks that ensure the solutions her team creates can be scaled globally to deliver value. She says the general direction of travel for data-led transformation at Colgate-Palmolive is about giving the people in the business tools to make better decisions quickly.

“We want them to have information at their disposal,” she says. “Some of the things we’ve built internally can compute billions of scenarios. So, it’s not just a matter of changing where teams spend their time. Some of the things we can do now, you couldn’t have completed a few years ago. Our work is about helping business teams use data and analytics in predictive, diagnostic and then prescriptive ways to make faster, more informed decisions.”

Embracing AI

Schildhouse’s team conceptualises, builds, deploys and embeds AI-enabled solutions, including machine learning models and predictive and prescriptive analytics, across Colgate-Palmolive globally. One example includes revenue growth management (RGM) analytics, which covers key concerns such as pricing and trade promotions.

She says RGM was identified as one of the areas where her team could have the biggest impact when she joined the company. They developed an in-house diagnostic and predictive tool that helped staff on the ground understand scenarios and make faster pricing decisions. That tool was scaled globally. The team also tracked usage to ensure the technology was effective.

The team used successes in RGM as a platform for developments in other areas. Schildhouse refers to promotion and calendar optimisation technology, which business users suggested was an area that could benefit from better analytics. They piloted, tested and refined this tool and are now pushing it out globally to boost pricing and promotions analysis.

Schildhouse’s team is also exploring generative AI (GenAI) in product innovation. Guided by a business-first approach, her team assessed potential technological solutions. They mapped out how the company’s marketers create and test new product concepts, and considered how AI could be deployed to make that process faster, easier and more effective.

Along with technology partner Market Logic, the data team created an insights hub. Marketers can use natural language to query data and receive instant insights from the hub.

“That was the first step that helped us understand unmet consumer needs,” she says.

As a second stage, they developed a tool to help support the creation of product concepts that fit these consumer needs. As part of an innovation funnel, Schildhouse says marketers can test their ideas rapidly in a digital twin. Developed in-house, the twin allows professionals to test their concepts cost-effectively for specific demographic groups.

“This multi-stage approach has been one of our most successful applications of GenAI to an important business area,” she says. “It’s about ensuring there’s a human in the loop and helping our innovation teams get to faster insights and to develop many more concept ideas.”

Establishing priorities

Schildhouse says one of her team’s main priorities is to innovate in an area known as omni-demand generation, which she explains is an approach that helps the company meet its consumers with the products they need. This work will incorporate progress in key areas, such as RGM, plus media, marketing and e-commerce analytics.

“We have some exciting things planned there,” she says, referring to her team’s aims. “Then I would just definitely say AI – so, continued experimentation, and moving to scale on many of the things that we’ve already piloted within that area.”

Schildhouse says her team considers AI initiatives via a framework that explores both horizontal and vertical elements. The horizontal elements are the underpinning tools and foundations that allow the company to scale its successful AI initiatives globally and effectively. The vertical elements, meanwhile, are the company’s priority areas for GenAI.

“Our plans for the next couple of years are very closely tied to that framework, but innovation always starts with our strategy – that’s key in helping us know where to focus,” she says.

Over the next 24 months, her internal team will continue to focus on the data strategy and governance foundations that she says are crucial to scaling analytics and AI initiatives.

“We’ll be putting a lot more focus on data transformation, and we’ve already made really great strides in that area, as well as building and launching data products that are reusable and governed. We’ll also be looking at AI and what’s the next generation for us,” she says, including developing data-enabled services for the company’s customers.

“There’s always a portion of what we’re doing that’s focused on exploring those edge cases of what could be the next most impactful area for the company. We want to create products that delight customers, meet their needs and provide the benefits that they’re looking for.”

Learning lessons

Schildhouse feels positive when she considers the future of the data leader role. There’s no doubting, she says, that information and insight continue to be increasingly important for modern companies. However, she reasserts that data leaders must ensure their AI and analytics initiatives are built on strong foundations.

In a world where companies are looking to get actual, tangible value from all their analytics and AI solutions, if your data is not in the right place and it’s not organised, and you don’t have the right datasets, that slows down the whole process

“In a world where companies are looking to get actual, tangible value from all their analytics and AI solutions, if your data is not in the right place and it’s not organised, and you don’t have the right datasets, that slows down the whole process,” she says.

“One of the reasons we were able to scale the RGM analytics tool that we built in-house is because, at the same time as we were creating that, we also started working on the data foundations.”

Schildhouse says her team consolidated and harmonised data from 500 sources in a global view for the RGM project. Lessons learned in this initiative have helped inform others. Yet, regardless of the project, she says one thing remains constant – data leaders must be guided by enterprise demands rather than technological features.

“You must have a business lens,” she says. “Data leaders need to bring that focus and understand how the work of their team translates to something meaningful for their organisation and their industry. That awareness is so key in the role, and that’s where you see the more successful data leaders when I look at some of my peers.”