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In today's fast-paced world, staying up to date with the latest trends and insights is crucial for product, design, and analytics people. Ten years ago, when I would speak to audiences and ask how many listened to podcasts there would be only a handful that would raise their hand. Now podcasts have emerged in popularity as a valuable resource, offering in-depth discussions, expert interviews, and industry insights on various topics. In this blog post, I am providing recommendations of twelve exceptional non-’product’ and non-’data’ podcasts that I recommend for product, design, and data people.

In the ever-evolving landscape of technology, Generative AI has emerged as a groundbreaking innovation, reshaping the way we approach problem-solving, creativity, and design. As product managers and designers, it's crucial to understand the concept of prompt engineering – the art of effectively communicating with AI models to elicit desired outcomes. This article delves into the essence of prompt engineering and provides best practices for harnessing the potential of Generative AI.

Generative Artificial intelligence (Generative AI), like ChatGPT, is the hammer that nearly every product person is being asked to swing looking for nails as of the writing of this article. There are good reasons around Generative AI interest, but this post seeks to break things down from the prospective of a product leader, manager, or owner of one or more existing products on how to approach and evaluate Generative AI for those existing products. In a future post there will be a discussion around leveraging Generative AI from the new product development perspective.

Learn about the transformative power of Artificial Intelligence (AI) in product management and design with our insightful blog post. Delve into the myriad ways AI can revolutionize product development, from analyzing analytics data and classifying customer feedback to drafting content and creating visuals. Learn how AI empowers product managers to make smarter decisions, conduct efficient market research, and improve product quality.

This post delves into the significance of a product roadmap as a dynamic document pivotal in defining a product's vision, goals, and strategy. It emphasizes the roadmap's role in harmonizing stakeholder communication and enhancing focus and execution. The article outlines key practices for creating an impactful product roadmap.

Data Storytelling and Translation bridges the chasm between numbers and narratives. Learn the intricacies of translating raw data into compelling stories that captivate, inform, and inspire action. The book covers proven frameworks for converting data into compelling narratives, strategies to tailor data stories to different audiences, techniques to avoid common pitfalls and biases in data representation, the balance between aesthetics and accuracy in data visualization, and uses real-world case studies illustrating the power of effective data storytelling.

Understanding your customer, your audience, and even people close to you is not easy. It takes effort. It takes intentional listening. It takes intentional action. One technique to help you understand others is through empathy mapping. It is something that I use a lot and it works.

This is a short-form video on the Cobra Effect. It is a topic that I think is valuable to understand for those seeking to make better human-centered and data-driven decisions. There are a couple of reasons why this is top of mind right now. First, the Cobra Effect is an important concept that everyone should be aware of especially if in a position where you are creating or influencing metrics and their target. Second, I see more and more gaps where discussion around incentives and unintended consequences is not deliberate prior to actions being taken.

Two hot or dare I even say sexy roles are product manager and data scientist. Some make the magician or unicorn metaphor for great product managers and data scientists even. No, I don’t believe in real magic and unicorns and certainly not product manager magicians and data scientist unicorns. Let’s discuss what these sought after product and data roles from perspective of someone aspiring to be but also from someone hiring someone that is.

Behavioral science is an area where organizations are just starting to realize its importance. As you are making your digital transformation, the human element is even more important and behavioral science should play a role. Think of it as the study of why people do what they do and why they make the decisions they make. No matter your role it is time to start harnessing the power of behavioral science to make better products, provide better experiences, and generate better insights.

What is the difference between data visualization and data storytelling? This is a valid question. Industry-wide there has been a significant focus on perfecting and simplifying data visualization, yet much time hasn’t been spent on developing the storytelling process. It is presumptive to think that the presenter of the data visualization is wholly responsible for the story; in fact, the story starts building many steps before presentation.

The world is extremely complicated and most things are made up of many things that together work as a system whether it is the human body, your car, or even your city. Most things around us are really systems and have a lot of moving parts and processes that are interconnected. This also includes most things that you are going to encounter if whether you are a data scientist, product manager, customer experience architect, product designer, or leader to name a few. To help us understand, design and innovate these complex systems there is even a whole field of study called systems thinking.

The takeaway of this post is that good data visualization or data viz for short helps bridge the contradiction of fast impulsive thinking and slow deliberate rational thinking. It allows us to harness the power of data and rational thought in a fast-thinking manner. Up and to the right has a meaning. Red has a meaning. Line charts have a meaning. If using these and other data viz artifacts well then we possibly solve or at least reduce the thinking fast and slow contradiction as respects incorporating data into decisions.

Data Governance and Data Literacy are two sides of the same coin. They both are about helping an organization maximize value from data. They both are about helping an organization broaden access to data. They both require a lot of change management and adoption at a top-down and bottoms-up level. Aligning your Data Literacy efforts with your Data Governance and ensuring both of these align with your Data Principles and Data Culture are critical.

Good metrics start with understanding defining the purpose. Understanding the purpose requires you to ask what you are trying to accomplish by creating, managing, analyzing, and communicating your specific metric. It is also important to realize that often times there are multiple purposes served with metrics.

Data visualization has become common place and more and more products have incorporated it and analytics into their apps. This article explores how you can better do this as a product manager and product designer. We do this by going through some of the good, bad, and ugly of data viz in product and some steps that you can use to ensure yours is good. Well implemented data visualization in products can lead to better product adoption and use but the reverse is true for bad data visualization.

Think of Data Principles as your manifesto or by-laws in how your organization uses data. Data Principles could be about how you go about collecting data. They can be an important right of setting the tone of your organization in how data is used from the C-level throughout. How you go about sharing data within the organization. What your view of data in the decision making process. What are your views around Data Governance. What is transparent and what is hidden to your consumers, partners, employees, etc. Certainly Data Principles can include a lot. In this article we will go through how you can establish good Data Principles.

Metrics are important but not everything needs to be measured in a data-driven world. It is easy to add a metric without really understanding the upstream and downstream consequences. That is why it is important that your metrics have a positive return on investment (ROI).

There are different types of games in the world and at a high-level they can be divided into finite and infinite games. Understanding which game you and your colleagues are playing in an organization and driving culture and processes towards alignment will lead to greater success.

The post discusses the complex relationship between legality and ethics in data usage. It highlights various factors that influence this dynamic, such as changing global attitudes towards data privacy, security concerns, and evolving legal standards.