By Melissa Strong

Hello again, everyone. Melissa Strong here. I know I promised you five more articles in my Open Source series (they’re still coming), but I’m taking a short detour because I just finished Dashboards that Deliver by Andy Cotgreave, Amanda Makulec, Jeffrey Shaffer, and Steve Wexler.

To explain why this book jumped to the front of the queue, let me rewind to June. I had the chance to meet Andy, Steve, and Amanda at the Data Visualization Society’s Information is Beautiful (IIB) Awards in Miami, which capped off several days of the Outlier conference. It was a great time.

Come August, when I began working on my dashboard capstone project, it was natural to reach for The Big Book of Dashboards (2017) by Steve Wexler, Jeffrey Shaffer, and Andy Cotgreave. Then I learned a follow-up was coming out in late September. Perfect timing.

I read the two books back-to-back, and they complement each other far more than the typical “good original, weaker sequel” pattern. This is not that story at all. 

The Big Book of Dashboards is about solutions. It shows how others solved specific problems under real constraints. I use it when I want to test whether a layout or communication approach makes sense for my own users.

Dashboards That Deliver is about process. It focuses on the conversations and decisions that make a good solution possible. I keep coming back to it as I work on my capstone, especially since the project is still shifting and not fully settled.

Together, the books keep the project moving in the right direction. One helps me figure out what I’m trying to achieve; the other helps me design a dashboard that will inform real decisions once it’s built.

One of the biggest points the authors make is that dashboards rarely fail because of the charts. They fail because teams skip the early steps that truly matter. Dashboards That Deliver spends most of its time on those early conversations, even though they can feel slow or a little uncomfortable.

The book breaks the work into a simple flow that actually helps you get somewhere. Before you sketch anything, you need to understand what decisions the dashboard is supposed to support. It sounds obvious, but it’s the part that’s often missing. Stakeholders walk in with ideas they saw online, and if you take that at face value, the project can drift fast.

Dashboards That Deliver encourages you to slow down and start asking real questions first. Sometimes that means asking “why” over and over until you get past the surface request and into what they truly need. It shifts you from taking orders to helping people figure out the real problem they are trying to solve.

The same idea applies to prototypes. Instead of building something polished right away, the authors suggest simple drafts and early feedback. The rough look is the point. It keeps everyone focused on clarity instead of decoration.

This approach has already helped me with my capstone. The project is still changing, and when I catch myself wanting to make something look perfect too early, I am reminded: understand the problem first, check that it works for people second, and save the polish for the end. It keeps the whole thing moving in the right direction.

Part I explains a practical, user centered way to build dashboards that people will actually use. If you have ever worked with Agile or any kind of iterative approach, the overall idea will feel familiar, but the focus here is clearer. A dashboard should help someone make a real decision, not just display a pile of data.

The book also makes a simple point that stuck with me. There is a difference between a live dashboard and a dead one. A live dashboard is something people open, understand, and act on. A dead dashboard is a nice looking report that gets ignored. The framework is designed to help teams build more of the first kind and none of the second.

Part II moves from theory into practice. It shows how organizations apply the framework from first conversations all the way to implementation. The scenarios highlight different industries, different constraints, and different definitions of success.

Even if a scenario doesn’t match your domain, it’s worth reading. Cross-application learning is real. You’ll find patterns and strategies that translate even if the data or goals look nothing like your own. This is where the book’s versatility starts to click.

Part III is a set of short essays that focus on practical techniques and the kinds of real-world challenges teams run into all the time. A few chapters jumped out at me because they offer ideas you can use right away.

One essay explains Big Assertive Numbers and how a single well-supported number can sometimes communicate a message more clearly than a complex chart. Another chapter covers chart duos, which show how pairing two visuals can tell a stronger story than either one by itself.

There is also a timely chapter on Generative AI. It looks at how AI can help with quick drafts, early exploration, and basic analysis, but it is very clear that AI is not a shortcut. The authors discuss ethics, accuracy, and responsible use. They point out where human judgment is paramount and where AI can simply give you a helpful boost. It is a practical, grounded look at how to use these tools without sacrificing quality or trust.

And yes, they brought back the screaming cat illustration from Eric Kim. Some traditions deserve to stay.

Dashboards That Deliver doesn’t replace The Big Book of Dashboards. It completes it. Both books have already helped guide my capstone dashboard project, and I’ll continue referencing them as the work moves toward completion. They’re practical, honest, and rooted in experience, exactly the kind of resources that help turn dashboard ideas into tools people will actually use.