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Mind the Product

Guest Post

Building for change: When transformation and product management meet

Product Development

What are product features? Definition, examples, and 5 types

B2B product management

Artificial Intelligence (AI) & Machine Learning (ML)

Building Product Teams

Business Strategy

Data Driven Product Management

Digital Transformation

Diversity and Inclusion

Minimum Viable Product

OKRs

Prioritisation

Product Design

Product Research

Product Development Process

Product Ethics

Product Discovery

Product Leadership

Product Management Career

Product Strategy

Stakeholder Management

🌎 <a href="https://www.mindtheproduct.com/world-product-day/" style="color:#FFFFFF;text-decoration:underline;">Happy World Product Day!</a> Be sure to celebrate product, yourself, and your peers today! 🎉

Why teams often struggle with OKRs: A conversation with Jeff Gothelf

Lessons from my first year as a Prod Ops Manager

How Google makes AI work at Enterprise scale – Miku Jha (Director, AI/ML and Generative AI, Google Cloud)

5 ways to invent and simplify as an AI/ML product manager

US Business Lead

Wes Galliher

The rollercoaster ride that is product development is inherently chaotic and ever-changing. So, as process-oriented professionals, we product managers look to the structure and predictability of a scientific approach for certainty.
Our inputs into such a scientific approach, however, are based on assumptions &#8211; either individual or corporate &#8211; that contain significant amounts of uncertainty. Sure, there are semi-quantitative ways of estimating uncertainty for each assumption (for example, the <a href="https://www.intercom.com/blog/rice-simple-prioritization-for-product-managers/" target="_blank" rel="noopener noreferrer">RICE framework</a> for prioritization), but as with many things in product development, these are guess-based models that help us to compare options.
Effective product managers can artfully apply past experiences and learnings (either their own or those of others) to manage the level of uncertainty in their decisions and approaches. This is where the support of an agile, learning organization is an incredible benefit. It means a product manager can hone their ability to leverage past experiences through continuous experimentation and applied insights.
<h2>Learn From da Vinci: an Unusual Role Model</h2>
Not many product people would consider Leonardo da Vinci a role model, but his approach to his craft shows a wonderful blend of art and science. As a “master of art and science”, da Vinci would meticulously investigate everything &#8211; from engineering to human anatomy to birds flying &#8211; in order to gain inspiration and an in-depth understanding of his art subjects.
Like da Vinci’s paint, brushes, notebooks and canvases, there is no shortage of methods, frameworks, and tools at the product manager’s disposal. They include:
<ul>
<li>Prioritization methods, including 2&#215;2 matrices, stack ranking, RICE, or “buy-a-feature” (the worlds of art and science even collide in this <a href="https://foldingburritos.com/product-prioritization-techniques/" target="_blank" rel="noopener noreferrer">Periodic Table of Product Prioritization Techniques</a>)</li>
<li>Problem discovery frameworks like Jobs-To-Be-Done, service blueprints, journey maps</li>
<li>Release planning using Jeff Patton’s <a href="https://www.jpattonassociates.com/the-new-backlog/" target="_blank" rel="noopener noreferrer">story mapping</a> or Roman Pichler’s <a href="https://www.romanpichler.com/blog/make-the-product-backlog-deep/" target="_blank" rel="noopener noreferrer">DEEP framework</a> for your backlog</li>
<li>Project management tools for backlog management, roadmap creation, user feedback collection, and many more</li>
</ul>
As a consultant, my clients often ask me for advice on what tools and methods they can buy or use to be successful. They are looking for a cookie-cutter approach to learn, apply, and then rinse and repeat. Unfortunately, effective product management, especially in the complex world of technical product development, depends on how well we wield our toolbox of tools. Because product management is hardly ever black and white, selecting the right tool for a given situation requires the artful assessment of a number of factors:
<ul>
<li>The situational context</li>
<li>A deep understanding of why a given tool or framework is useful</li>
<li>The desired outcomes and how to guide the team towards them</li>
<li>Understanding that the first answer to any question in product is almost always <a href="https://www.mindtheproduct.com/the-1-answer-in-product-it-depends/" target="_blank" rel="noopener noreferrer">“it depends”</a></li>
</ul>
<h2>Channel Your Inner Han Solo… and C-3PO</h2>
As Star Wars’ Han Solo famously said: “I’ve got a bad feeling about this”. Force-related discussions aside, Han was assessing his current situation through intuition. Psychologists will tell you intuition comes from patterns we’ve identified in our past experiences (as a smuggler, for Han Solo). This is especially useful when quick decisions are needed based on limited data, but can also be valuable as a supplement to collected data. C-3PO, on the other hand, can collect, process, and regurgitate zettabytes of data better than any human could even dream. But short of telling you the odds of some catastrophe occurring, he can’t apply the intangibles of experience and “gut feeling”. Both are needed.
“Data-driven” is a commonly observed adjective on resumes these days, but the subjective assessment of that data is what differentiates the great product managers from the good. Take, for example, assessing different opportunities for your product. You should be prioritizing these opportunities through a scientific, data-driven approach, but as Teresa Torres will tell you, <a href="https://www.mindtheproduct.com/critical-thinking-product-teams-teresa-torres/" target="_blank" rel="noopener noreferrer">these are ultimately subjective decisions</a>. Recognizing and embracing the messy combination of objectivity and subjectivity allows you to adjust when needed or when you find out you were wrong (which is one of the most valuable learning experiences you can have). While quantitative methods are extremely valuable for guiding strategy and decision-making, the challenge with these tools and techniques is that they start to masquerade as rigorous science, when in fact <a href="https://www.mindtheproduct.com/take-control-of-prioritisation-holly-donohue-on-the-product-experience/" target="_blank" rel="noopener noreferrer">they need to be a bit of an art</a> combined with the human element of intuition and discussion.
<h2>Learn the Tools, Apply Them Wisely, and Trust Your gut</h2>
Invest the time to learn frameworks and tools that can be applied in various situations. Apply quantitative methodologies to assess and make quality decisions. But ensure you leverage past experiences and lessons learned to select the right tool, and ask yourself: “Does this make sense?”. If not, ask more questions, get input from others (including your users), and then make any necessary adjustments. Do this often enough, and you can become a master of the art and science of product management.

core/freeform

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How to be a better data-driven product manager

User insights for product decisions by Cris Valerio

Data scaling for startups by Crystal Widjaja

Make Better Product Decisions by Using Quantitative and Qualitative Data, by Brian Tran

Embrace Uncertainty: the art of Applying a Scientific Approach

Wes Galliher

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