At dunnhumby, while building Walmart Luminate, I had an “aha” moment that changed how I think about data products: we were spending too much time discussing features, and not enough talking about insights.
This is a common trap, I’ve seen countless organizations treat data products like traditional ones. It simply doesn’t work.
Here’s why: traditional products are about features enabling outcomes. A CRM helps manage customers, an accounting package manages finances, a word processor helps create documents. The value chain is clear and linear.
Data products flip this model on its head. Their features and outcomes are essentially the insights they generate and the actions they drive. When we built Walmart Luminate, success wasn’t about adding more features – it was about generating insights that drove better decisions and measurable business outcomes.
A Different Kind of Product
Let me give you a real example. At Yahoo!, our advertising platform started as a simple marketplace for ad space. But the real value emerged when we started layering in data capabilities – audience insights, performance analytics, optimization algorithms. The core product remained the same, but its value multiplied exponentially.
This highlights a fundamental truth about data products: their value isn’t linear. At dunnhumby, combining different data sets often created insights worth far more than the sum of their parts. A customer segment analysis combined with promotional data might reveal opportunities nobody had spotted before.
But here’s the catch – data products are also more fragile. One unreliable data point, one privacy breach, one quality issue, and you can lose customer trust forever: trust isn’t a feature, it’s the foundation everything else builds on.
Different Skills, Different Mindset
Think about what makes a great traditional product manager: they obsess over user experience, feature prioritization, market fit. All crucial skills. But for data products? That’s just the starting point.
I learned this building teams over time. The best data product managers weren’t necessarily the ones with the strongest traditional product background. They were the ones who could bridge worlds – understanding both the retail business and the possibilities of advanced analytics. They could translate between data science solutions and real business problems.
You also need to think differently about infrastructure. In traditional products, infrastructure supports your features. In data products, your infrastructure choices fundamentally shape what’s possible. Get your data architecture wrong early on, and you’ll pay for it forever. Trust me on this one – I’ve seen it happen more times than I care to remember.
This is why I was agonizing so much about data structures at dunnhumby: where do the data sit, can they travel easily, how many times do they have to travel, how are the data schema, how easily can you access the data, how do they integrate upstream, what’s the refresh rate? These aren’t just technical decisions – they fundamentally shape what products you can build and how much value you can deliver. Once we had to completely change the product because the refresh rate wasn’t what it was supposed to be! We were seconds away from throwing the whole thing out of the window when we had a breakthrough and pivoted toward a different data product. Not different features, a whole different product!
The Evolution: From Internal Tool to Product
Let me walk you through how this typically plays out. I’ve seen this evolution multiple times, and it usually follows three stages.
Stage 1: Internal Focus
Here’s a story from my early dunnhumby days. A retailer came to us wanting to optimize their private label portfolio. Simple request, right? But it perfectly illustrates the first stage of data product evolution.
You’re not building for external customers yet. You’re using data to enhance internal operations. But don’t underestimate this phase – it’s where you learn what makes data valuable in your specific context.
The product manager’s role here looks very different. You’re not shipping features; you’re:
- Finding high-value use cases within your organization
- Translating stakeholder needs into actionable insights
- Making data quality a priority (trust me, this becomes crucial later)
- Proving value creation
The key? Success isn’t just about generating insights – it’s about building the organizational muscle to act on them. I’ve seen plenty of great insights die in PowerPoint decks because organizations weren’t ready to use them.
Stage 2: Adding Intelligence
This is where it gets interesting. You’re taking existing products and enhancing them with data capabilities. Think of it as adding a brain to your existing offerings.
At Yahoo!, we transformed our basic ad platform into a sophisticated performance marketing solution by progressively adding data capabilities. Each new data layer – audience insights, performance analytics, optimization algorithms – multiplied the value of the core product.
But here’s the trap I see most teams fall into: adding data features just because they can. Every enhancement needs to solve a real problem or create meaningful value. Products will fail if they become over-engineered data platforms rather than solutions to customer problems. If something doesn’t generate value, then it is not needed, and you won’t put it in. Simple as that.
Stage 3: Data as the Product
This is where data product management truly comes into its own. You’re not enhancing existing products anymore – you’re creating standalone data products. Exciting? Yes. But this is also where I’ve seen many organizations stumble.
Building Walmart Luminate was a masterclass in this stage. We weren’t just packaging data – we were creating a suite of products that fundamentally changed how retailers and CPG manufacturers worked together – at the biggest retailer in the world! Every insight was worth tenths of million of dollars.
The challenges here are unique:
- You need to understand not just what data you have, but what it’s worth to customers
- Scalability becomes crucial (custom solutions kill data product economics – let me say it again: custom solutions kill data product economics)
- Partnerships become central to your strategy
- You have to fit into existing customer workflows, or be able to change them (the best insights are worthless if they don’t change how people work)
The Hard Truth
Want to know the biggest mistake I see? Organizations jumping straight to Stage 3 before mastering Stages 1 and 2. It’s tempting – the allure of data monetization is strong. But it’s like trying to run before you can walk.
I’ve learned that successful data products aren’t built – they evolve. You start by proving value internally, then enhance existing products, and finally create standalone offerings. Skip these steps at your peril.
Looking Ahead
Here’s what I know for sure: the future of product management is increasingly data-driven. Whether you’re improving internal operations, enhancing existing products, or creating new data products, success requires fundamentally rethinking how we approach product management.
What stage is your organization at? How are you adapting your product approach? I’d love to hear your experiences.
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