We’re back for part two on this hotly contested topic. We say dashboards have their place in business, and in data storytelling. You just have to know what you’re looking for. It’s the return of the dashboard! In part one of Don’t Diss the Dashboard, we discussed why dashboards get a bad rap. If you haven’t read the first part, go back and give it a look to get even more out of this article. In part two, we’re picking up where we left off, exploring more about how the story of the dashboard is led by exception. What’s an exception? If we take a step back and consider what we’re really looking ..read more

The models were on point and the analysis was solid, yet the presentation fell flat. Leaders were underwhelmed and no action was taken. Oof. In the debrief after the presentation, you hear, “I think your analysis was pretty good. The delivery just didn’t land with the leadership team. Perhaps we could look at further developing your presentation skills.” [Cue the needle scratching on the record player.] Okay, but hang on. Before you go there, take another look at your deck. And I don’t mean the analysis. I mean the over-riding message you needed your audience to understand. It’s the heart of t ..read more

If your social feeds look anything like mine, you’ve been flooded with content about ChatGPT. Commentary ranges from the exultant disruptors gleefully shouting, “AI will replace humans on so many tasks,” to troubled moderates calling for everyone to simmer down and focus on the risks. As we all know, artificial intelligence (AI) is not a new concept. The building blocks of artificial intelligence first emerged during World War II, and AI has been the subject of countless movies and books since the 60s—up to and including the fabulous “The Mitchells Versus the Machines” released in 2021. The ma ..read more

In the first few weeks after Netflix released the movie Don’t Look Up, my LinkedIn feed lit up with references to the film. Or more to the point, an early scene where the characters meet the United States president for the first time. If you haven’t yet seen the movie, the segment that had everyone in my network talking started at 17:55 in the Oval Office. (No spoilers, I promise.) In just a few minutes of dialogue, we have a complete and hilariously accurate case study on the highs and lows of the data storytelling process. Stop reading right now and cue it up—you won’t regret it. The Train W ..read more

Getting to the “so what” is the most challenging stage in progressing from observations to action because it can be difficult to find the patterns in data, draw conclusions, and gain alignment. But it’s far from impossible. What, so what, now what – WTF? You’ve likely already heard “what, so what, now what” by this point in your career. It has become a fairly common phrase in data analysis and storytelling over the last decade or so. But where did it come from? The phrase actually originated from a brainstorming model designed to help teams reflect on experiences or events so that they can ide ..read more

In all of my years working in research and analysis, the most frustrating part is watching great data go nowhere. Data should be a driver of decisions and action. Otherwise, why bother collecting it? It never fails—whenever I tell an audience “visualization isn’t storytelling,” I’m met with looks of surprise and doubt. But then I explain the role each plays in connecting with decision makers, and I can almost hear the click as everything falls into place. That’s when I know a data storyteller is born. The most effective way to make an impact with data is to structure it like a story. Think abo ..read more

It may be hard to believe, but the phrase “data storytelling” really only entered our lexicon during the last five to seven years. Before that, you were much more likely to hear the still-used phrase “actionable insights.” Unfortunately, data storytelling seems to have become synonymous with data visualization—a feature of technology. That’s a shame, and, said even more plainly, a mistake. But, visualization makes conclusions from data obvious. Don’t we need it? We do, but visualization on its own is not storytelling. Let me briefly pause here and be clear: I think visualization plays an impor ..read more

Gartner has a swanky definition, but basically, data literacy means having the ability to read, work, think critically and collaborate with others using data. Data literacy is not about being able to produce analytic models. Instead, data literacy is about creating a culture of curiosity rooted in data. What can we learn about the business? What questions can be asked and answered through data? As you look at building data literacy within your organization, there are two things to remember: First, data literacy must be balanced with business acumen to be effective, and secondly, data literacy ..read more

A little while ago, I wrote an article called “What’s An Insight? (And Who Decides?)” which drilled down on the differences between truly insightful findings and…everything else. In a nutshell, insight boils down to something that wasn’t already known that is relevant to the problem at hand. It provides new perspective and equips the audience with the confidence to take meaningful action. On the “Is It Insightful?” matrix (see right), anything that falls in the top right obviously has the potential to be insightful, but I know what you’re thinking—what about the other quadrants? Examining what ..read more

I don’t think there’s any question that “insights” are the holy grail of analytics. Everyone wants them, many claim they can provide them, but few report actually getting anywhere with them. It’s the genesis of the well-worn phrase, “Data rich but insight poor.” So why—with all we have at our disposal today, from advanced analytic methodologies to visualization tools that claim insights at your fingertips—are we not consistently getting true, meaningful insights? I think the answer starts with a quick hit of perspective: Just as beauty is in the eye of the beholder, insight is in the mind of t ..read more