WHAT YOU'LL LEARN
- How to spot the difference between data products and data-powered products.
- Why a killer UI matters as much as the data itself.
- The biggest hurdles in scaling data products (and how to leap over them).
- Whether specializing in data products is really worth it for PMs.
In a world where everything seems to be a “data product,” Earl Lee, Product Lead at Hightouch, helps clear things up. He classifies data products into two main buckets. The first involves companies that source net-new data, like scraping government sites for fresh info. The second—where Hightouch shines—helps businesses unlock the full value from the data they already own. Earl believes it’s not just about having data; it’s about putting that data to work in ways that matter.
THE EXPERT
Earl Lee is a product leader at Hightouch, where he’s spearheading AI Decisioning—a tool that streamlines marketing personalization. Before Hightouch, he co-founded HeadsUp, a company that used machine learning to help B2B sales teams target their most valuable leads. Earl’s background in handling data-driven products gives him an edge in turning complex datasets into practical, action-driving tools.
THE INSIGHTS
1 - Know your data product type
According to Earl, data products come in two flavors. The first, like FiscalNote, focuses on gathering and processing net-new data from various sources, while the second, like Hightouch, enables companies to maximize the value of the data they already have. It’s not just about data collection; it’s about turning raw data into actionable insights that power workflows.
2 - Don’t sleep on the UI
Data products aren’t just about what’s happening behind the scenes. A solid UI can make or break the user experience. Earl emphasizes that even in data-heavy products, the interface matters. Companies like Hightouch focus on providing intuitive interfaces that make it easy for users to get value from their data. The lesson here? A product is only as good as its users’ ability to navigate it.
"If your data is provided in a way that doesn’t allow the customer or end user to easily act on it or easily get what they’re trying to get out of it, then it sort of defeats the purpose of having that data in the first place."
3 - Tame the wild world of messy data
One of the biggest challenges in building data products is handling the variety and inconsistency of customer data. Every company’s dataset is structured differently, and the quality varies. Earl points out that building a flexible product capable of dealing with messy data is key. The more adaptable your product, the better it can serve a broad range of users.
“Every company has their own data set, structuring it in different ways. The level of completeness and quality of that data is going to vary a ton… You have to count for all of that.”
4 - You don’t need to be a data guru
While experience with data products can be helpful, Earl believes that PMs don’t need to specialize to succeed. Core product management skills—like problem-solving and understanding user needs—are just as important. With the right approach, PMs can pick up the necessary knowledge about data products as they go.
"I think I would view competency and experience working with data products as you might with any other vertical or subject matter area. It’s impossible for someone to understand what’s happening under the hood.”
5 - Stay sharp with newsletters and AI
To stay ahead of industry trends, Earl turns to newsletters like What’s Hot in Enterprise SaaS and Stratechery for broad insights. When he needs to dive deeper into a specific topic, he uses ChatGPT to help him ramp up quickly. It’s all about using the right resources to keep learning and evolving in the fast-paced world of data products.
