Another risk that comes with overfunding a new idea at a large company is that it becomes a must-win idea. It can’t fail.

Failure is an important part of innovation. Not all ideas are good ones. Ideas that aren’t winning need to die. The company needs to prune them so they can put energy into something else.

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When a large company that doesn’t have a culture of innovation decides to start something new, it often fails because it throws too much money and too many people at the initiative.

Lots of funding changes what success means. The product can’t start small and grow. It needs to win big, fast. If it’s not an immediate breakthrough, it’s a failure.

But innovative products aren’t built that way. Great products are discovered over time, not invented in a flash of brilliance.

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To build successful products using data and experiments, you must start with a hypothesis. Data can inform your decisions, but they cannot decide for you. Simply following the data wherever it leads will create an incoherent product. It will be lifeless and uninspiring.

Successful products begin with an insight. Data isn’t insight. Data by itself can lead you astray. It’s easy to drive a single metric up in a way that is harmful in the long term. Without an insight into how that number should move, chasing the data can lead to a quantitative success that’s a qualitative failure.

Start with the question you want to answer. Start with a belief about the world. Those beliefs can be formed by past research and experiments. Past data can reveal context or trends that become a hypothesis. The beliefs can also come from experience, domain expertise, observation, or even creative sparks.

Experiments and data can validate your hypothesis. You have an idea that if you do something you’ll change the way people behave. They’ll buy more products. They’ll use this feature more. They’ll stick around longer. They’ll invite their friends. The data tells you if you are right. It helps you add detail to your hypothesis. It tells you the idea was correct for some people but wrong for others.

Without a hypothesis to start from, data can’t provide insights. You don’t know what the data is telling you. Be data-informed, not data-driven.

There was a ceramics class where half the class was told they had to create one dish for the semester and the quality of that dish would be the basis of the grade. The other half the class was told their grade would be based on how many dishes they made, but quality didn’t matter.

The first group spent the semester designing the perfect dish. They spent weeks picking out just the right material. They agonized over sketches of their designs. They argued over colors and styles.

The second group just made dishes. They made 3 the first week. The second week, they were able to move faster and made 8. Those 8 weren’t any better than the first 3. But a few weeks in, they knew how to make dishes and their designs were improving.

At the end of the semester,the first group had one pretty good dish. But the second group had dozens of amazing dishes.

Because they got more reps in actually creating dishes, they started making really nice ones. The others had a good theory of what would make a good dish, but only had one shot at turning that theory into reality.

The first group’s dish was a cup. It was pretty good, but the teacher had hoped for a bowl. The second group had cups, plates, bowls, and statues.

In product management, you’ll never plan your way to a perfect product. But by running lots of reps, you’ll make lots of fantastic ones.

Input metrics can be helpful to track progress toward long-term goals, but they can cause issues if not used carefully. They might make people act in ways you didn’t expect, limit creativity, and lead to micromanaging.

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Changing from "Customers" to "Partners" will give you a better perspective on internal product development.

Companies are using internal product platforms to speed up development, make user experiences consistent, reduce operational costs, and support various environments, including mobile. One of the key differences in product management of an internal platform is who your end users are. They adopt and use your product for different reasons than an external customer would.

It’s tempting to view the product managers and engineers using the platform as your customers. As product managers, we spend most of the time thinking about customers, so it’s natural for an internal product to think of fellow employees as their customers.

But the concept of a customer comes with assumptions and context that can slow your success as a platform team There’s a huge difference between an actual customer and these internal “customers.”

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