Diana Schildhouse, chief data and analytics officer at Colgate-Palmolive, describes herself as a data storyteller – but what does that mean in terms of day-to-day delivery?
“To be successful in roles like this, you must have a deep connection with the business and understand what you’re trying to solve, what their questions are, and then devise solutions,” she says.
“Those solutions could use advanced analytics. Sometimes, it’s about creating simpler solutions. But success is always about solving that business need.”
Schildhouse joined Colgate-Palmolive in April 2021 as chief analytics and insights officer. She was previously at Mattel for eight years, latterly as senior vice-president for global strategy, insights and analytics. Schildhouse has also worked for Westfield, Merrill Lynch and Disney.
“Most of my experience has been in consumer-facing companies,” she says.
“In terms of my career, I’ve always been in functions like advanced analytics, insights and strategy. When I saw the opportunity with Colgate-Palmolive, I thought about the breadth and scale of the company. It was an exciting opportunity for me to come in and build from the ground up, while leveraging the scale of the business.”
Schildhouse joined the company in a newly created role to develop an analytics and insights strategy for an organisation that operates in over 200 countries and territories globally. After proving her success in this role, she assumed her current position in June 2025, where the breadth of responsibilities increased to include oversight for data and artificial intelligence (AI).
“I had already been running some of those areas, but it made sense for us to bring everything together,” she says.
“You can’t build and scale all the exciting, advanced analytics solutions and everything with AI unless you have data foundations. Many companies are on this journey. They recognise that gaining value from all this data-enabled technology depends on key elements like data strategy, data governance, and a series of related topics.”
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 Diana Schildhouse, Colgate-Palmolive
“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.”
Now, there’s another class of high-end gaming laptop that focuses more on performance than being thin or portable. The Lenovo Legion 7i Gen 10 is one of my favorites in this class, featuring a beautiful white chassis and glossy OLED display. Unlike some OLED displays, the Legion 7i’s screen can be cranked up to over 1,000 nits of brightness. The result is some really splendid HDR performance that brings games to life. HDR is a powerful way of improving the visuals of your games without a performance cost. The Legion 7i Gen 10 is one of the very best in this regard.
It’s still fairly thin at 0.7 inches thick too, while a lot of the ports are found on the back. It’s the definition of a “clean” gaming laptop. It’s no slouch when it comes to performance either, offering either the RTX 5070 Ti or RTX 5080 for graphics.
Cheap Gaming Laptops That Are Worth It
No gaming laptops worth buying are actually cheap. High-refresh rate displays and discrete graphics will always make them more expensive than standard laptops. But as you get closer to $1,000, there is one laptop I always come back to: the Lenovo LOQ 15. Pronounced “Lock,” this Lenovo subbrand is known for cutting the fluff and focusing on giving gamers the performance they need at an affordable price. No laptop does that better than the LOQ 15. Many laptop manufacturers sell their RTX 5060 configurations for hundreds of dollars more. In reality, if you’re shopping around $1,000, there’s no reason to not buy the LOQ 15. Just do it.
If you do want to save some extra cash, there is another option that is cheaper than the LOQ 15 with a few compromises in key areas. The Acer Nitro V 16 is that laptop, which comes with an RTX 5050. This was as affordable as $600 at one point last year—before prices on laptops have risen due to the ongoing memory shortage—but it remains the only laptop cheaper than the Lenovo LOQ 15 that’s actually worth it. It’s fairly powerful for the RTX 5050, and while the screen is pretty shoddy, it’s not a bad-looking laptop. The one big caveat is that the 135-watt power supply it comes with doesn’t deliver quite enough power to keep it charged in Performance mode. Read more about this issue in my review, as it’s important to know about if you’re planning to buy it.
There are other cheap gaming laptops out there I’ve tested, such as the MSI Cyborg A15, but either the Acer Nitro V 16 or Lenovo LOQ 15 are better, cheaper options. You will also find lots of gaming laptops under $1,000 that use older graphics cards, such as the RTX 4050 or 3050. In general, I’d recommend staying away from these. They’re only one or two generations back, but remember: Nvidia only releases new laptop graphics cards every couple of years. So, an RTX 4050 laptop may be well over two years old already, and an RTX 3050 is over five years old. Not only do you get worse graphics performance, these laptops are much more likely to need to be replaced sooner.
Experimental Stuff
One of the exciting things about the world of gaming laptops right now is the experimentation. While clamshell gaming laptops with a conventional Nvidia GPU are the most standard way to go, there’s a few different ways to take your PC games on the go that stretch the boundaries. You might consider a gaming handheld, for example, like the Steam Deck or Xbox Ally X. These handhelds have their fans, and while you can’t also do your homework on these devices, they’re great on couches, trains, and planes.
In mid-April, astronomy enthusiasts will be able to enjoy one of the classic celestial spectacles. The meteor shower known as the Lyrids will illuminate the sky, especially in the northern hemisphere, and anyone will be able to see it with the naked eye, weather permitting—if they know where to look.
The Lyrids began to appear as early as April 14, but their activity peaks between the night of April 21 and the early morning of April 22, according to NASA. During those hours, the shower will show 15 to 20 meteors per hour under dark skies.
The shower gets its name because the meteors appear to emerge from the constellation Lyra. Locating the radiant is simple if you use an astronomical mapping app: Just find Vega, the fifth brightest star in the sky, surpassed only by Sirius, Canopus, Alpha Centauri A, and Arcturus. Once you locate it, look around it; the luminous traces of the Lyrids will seem to be projected from that point due to a perspective effect. Keep in mind that it takes 20 to 30 minutes for the human eye to adjust to darkness.
The moon will be in early crescent phase during the peak, so its light will interfere very little. With a dark sky, meteors should stand out easily. The shower is usually visible from 10 pm to dawn, although early morning offers the best conditions. It is best to stay away from light pollution and, if possible, to observe from high ground. An outing to the mountains works well.
Each meteor shower has a different origin. In April, Earth crosses the cloud of fragments left by comet C/1861 G1 (Thatcher) in its orbit around the sun. This comet, discovered in 1861, takes about 415 years to complete its journey. The grains of ice and rock that it released centuries ago enter the atmosphere at high speed and produce the flashes we know as the Lyrids.
After the Lyrids, the calendar still holds several spectacles for those who follow the night sky. The Eta Aquarids will arrive in May with debris from Halley’s Comet. The Perseids will appear in August, the Orionids will return in October, and the year will close with the Leonids in November and the Geminids in December. The latter is considered the most intense and reliable shower on the calendar.
This story originally appeared on WIRED en Español and has been translated from Spanish.
Over the weekend in China, a humanoid robot shattered world half-marathon record—the human record—by seven minutes.
The star performer was a robot developed by the Chinese company Honor (the smartphone maker), which finished the 13.1-mile race in 50 minutes, 26 seconds. The human record, set by Ugandan Olympic medalist Jacob Kiplimo, is 57 minutes, 20 seconds. The result marks an impressive milestone especially considering that, just a year earlier, the fastest robot at this half-marathon event took two and a half hours to complete the same distance.
But Honor’s robot was not the only participant. The event consisted of more than 100 humanoid robots from 76 institutions across China. The robots lined up alongside 12,000 human runners in Beijing’s E-Town, albeit on separate courses to avoid accidents. The contrast in performance between humans and robots was more than evident.
Run, Robot, Run
A humanoid robot is designed to mimic the structure and movement of the human body, with legs, arms, and sensors that allow it to interact with its environment. In this case, the winning robot incorporated features inspired by elite runners: long legs (almost a meter), advanced balance systems, and a liquid cooling mechanism, similar to that of smartphones, to prevent overheating during the race.
In addition, many of the participating robots operated autonomously, meaning without direct human control. Thanks to artificial intelligence algorithms, they could adjust their pace, maintain balance, and adapt to the terrain in real time. Notably, the Honor robot that achieved the 50-minute mark operated autonomously. The Chinese manufacturer presented another robot, operated by remote control, that ran the same stretch in even less time: 48 minutes, 19 seconds.
As expected, there were some accidents in the race. Some robots fell down, others veered off the path, and several needed technical assistance along the way. While the physical performance of humanoid robots has advanced rapidly, their reliability is still developing. Of course, the laughter and jeers are no longer as frequent as they used to be, replaced by applause and exclamations of surprise.
The winning robot, “Blitz,” from smartphone manufacturer Honor was on display at the awards ceremony after the Beijing E-Town Robot Half Marathon.
Photograph: Lintao Zhang/Getty Images
Robot Superiority
Just like the robots that went viral for their impressive martial arts display a few weeks ago, this long-distance race is part of a broader strategy by China to show off its leadership in the development of advanced robots.
You don’t need to be a robotics expert to see that this achievement demonstrates that machines can outperform humans at specific physical tasks under controlled conditions. (It’s hard to imagine that the winning robot could achieve the same result, for example, if it started to rain during the race.) But humans still have a few tricks up their sleeve: Running in a straight line is very different from performing complex real-world activities, such as manipulating delicate objects or interacting socially.
However, it’s understandable that the image of a robot crossing the finish line in record time, ahead of human athletes, raises several questions. Is this the beginning of a new era in which machines redefine physical limits?
One could argue that a car is a machine, and those have always been faster than humans. But a humanoid robot is designed to mimic humans. It’s more alarming to see one beat humanity at its own game—even if so many of them are still tripping over themselves.
This story originally appeared in WIRED en Español and has been translated from Spanish.
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