When it comes to managing product investments, I’ve shifted my perspective significantly over the years. The phrase “stay within budget” doesn’t resonate with me anymore. Instead, I view it as having a pool of money that I can allocate strategically to maximise value. This approach requires a solid understanding of the data at hand and a clear vision of what we aim to achieve.
Embracing Hypothesis-Driven Engineering
One of the key concepts I advocate for is hypothesis-driven engineering practices. Whether we’re developing new products or enhancing existing ones, starting with a hypothesis is crucial. Here’s how I approach it:
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Identify the Idea: What do we want to add to our product? This could be a feature on your backlog or a broader initiative across your product portfolio.
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Define the Outcome: What are we trying to achieve? This clarity helps in aligning our efforts with the desired results.
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Run Small Experiments: What’s the smallest experiment we can conduct to test our hypothesis? This allows us to validate our ideas without committing extensive resources upfront.
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Measure Progress: How will we assess whether we’re moving towards our goal? Regular evaluation is essential to determine if we should continue investing in a particular initiative.
This methodology can be applied at any scale, from individual features to entire product lines.
A Real-World Example: Azure DevOps
Let me share an insightful example from the Azure DevOps team at Microsoft. They faced a challenge: how to help customers manage their technical debt effectively. The product unit manager, who oversees budgetary control, recognised that people are the most significant expense in product development. With a team of exactly 600 people, the focus was on allocating resources to ideas that would yield the highest return on investment.
The team proposed a grand idea: to create tools that would help customers identify and manage their technical debt. This initiative required collaboration across various teams, each contributing their insights and expertise. They dedicated a significant amount of time, around four to six months, to explore this concept.
However, after extensive experimentation and customer feedback, they discovered that their solutions didn’t resonate with users. Despite the investment, potentially around £10 million, they learned a valuable lesson: not every idea will succeed, and sometimes, it’s better to pivot and redirect resources elsewhere.
Learning from Failure
In traditional project management, this could easily be seen as wasted money. Yet, I argue that the learning gained from this experience is invaluable. They avoided the pitfalls of long-term investments in a failing idea, which could have resulted in a situation akin to the Windows 8 debacle. Imagine the cost of nearly 20,000 people working for six years on a product that ultimately disappointed customers and damaged brand reputation.
This experience catalysed a shift in Microsoft’s approach. They recognised the need for rapid testing and validation of ideas, ensuring that they could quickly adapt based on customer feedback. This shift is not just necessary for product teams; it should permeate every level of the organisation, from the ground up to the boardroom.
Conclusion: The Path Forward
In today’s fast-paced environment, adopting hypothesis-driven engineering practices is essential for success. It allows us to make informed decisions, minimise wasted resources, and ultimately deliver products that meet customer needs.
As we move forward, let’s commit to this approach at every level of our businesses. By doing so, we can ensure that our investments yield the maximum value and that we remain agile in the face of change. Remember, it’s not just about the money spent; it’s about the insights gained and the ability to pivot when necessary.
Smart Classifications
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