One of the most common complaints I hear from leaders is that the presentations they’re sitting through consistently fail to answer the critical question: ‘So, what?”
And most of the time, it’s because the presenter is missing a vital step in their data storytelling process.
The POET Technique™ (Purpose, Observation, Evaluation and Transition) fills that gap. It’s the Storylytics proprietary framework that shows the audience what data to focus on and what that data is saying. Most importantly, it tells them why that information is meaningful.
It’s that last part is what really matters.
The gap in traditional frameworks
We all know there’s no shortage of data communication frameworks out there. Some of the more popular ones include:
- SCR (Situation, Complication, Resolution),
- SCQA (Situation, Complication, Question and Answer)
- BLUF (Bottom Line Up Front)
- MINTO pyramid
While these models certainly have a place in business communication (they’re great at providing structure and effectively moving from question to answer), when it comes to data storytelling, they often only cover the beginning and end of the narrative.
They miss the juicy middle where meaning is uncovered through insight and judgment to give depth and relevance to the data.
And we’ve all seen what happens to recommendations when the audience can’t make sense of information that’s presented to them (hint: it vanishes into the corporate void).
How the POET Technique™ is different
The POET Technique™ is more than just a business communication framework; it’s a step-by-step process to help derive meaning from data.
Purpose: Why are you providing this information?
Observation: What key information do we need to understand?
Evaluation: Why it’s meaningful (the “So What?”)
Transition: Where do we go from here? (the “Now What?”)
When you use this framework, you get a sentence for each step which provides you with a mini-story and the foundation for your overall data narrative.
The power of the ‘E’ in POET
The real secret sauce of the POET Technique™ lies in the E: Evaluation. It’s calling out the step that most other frameworks miss. And that’s understandable, because it’s the most challenging part of the process.
I can’t count the number of times I’ve heard trainees begrudge ‘That darn E!’. But when they complete that step, they also say that it completely transformed the way they tell data stories.
Here’s why. The three main outcomes from a strong evaluation include recognizing risk, identifying opportunities and gaining a new perspective.
The ‘E’ effectively answers the most important question about the data you’re presenting: “Is it good?”
When you answer the question, you’re in a much better position to persuade your audience, influence decision-making and drive change.
Why the Evaluation step is often skipped
When we’re too close to the data and feel like we understand it thoroughly, our first inclination is generally to jump straight to action, from ‘findings’ directly to ‘recommendation’.
This happens in part because when we’re deeply familiar with the data, our instinct is to leap from findings straight to recommendations. The curse of knowledge kicks in, and we assume others share our understanding, so we skip showing how we got from the question to the answer.
The flipside of that is that we’re also afraid of the knowledge of others: that our perspective or approach will be challenged by those we perceive to be more informed or have a stronger point of view.
Both of these approaches generally lead to a rush towards approval, often without the full alignment of the team.
The risks of skipping evaluation
The role of a data storyteller is to create a clear, persuasive narrative that an audience can easily follow. But when we skip evaluation, it makes it nearly impossible for our audience to follow our train of thought from the data to the recommendations.
Essentially, it increases the cognitive load by making the audience interpret the data themselves. This is the danger zone for misalignment and the major cause of delayed decisions.
How the POET Technique™ results in better storytelling
What the POET Technique™ really teaches data storytellers to do is pause. To take a moment to consider what the data is saying and why that matters before jumping to action.
It requires the storyteller to take a clear position on the information and back it up, bridging that gap between knowledge and recommendation. This reduces the mental workload for your audience, making it easier for them to come to a decision.
The POET Technique™ adds an extra step that turns data into persuasive narratives that drive action.
Persuasion takes patience
In his popular Substack newsletter, Miller’s Book Review, writer and editor Joel J. Miller laments the decline of poetry in a world that feels like it’s always rushing to the finish line.
“But poetry doesn’t reward rapidity. It invites lingering, savouring, sometimes wrestling, and usually rereading many times over.”
Much like interpreting poetry, finding the meaning in data takes time.
The manic pace of work can make slowing down feel like an impossibility these days. But the POET Technique™ essentially forces you to step back.
That one important step requires you to take a beat and really consider the data, evaluate what it means and clearly communicate your point of view.
Rather than rush to a conclusion, you’ll create a narrative that fosters alignment with your team so that you can make better, more informed decisions together.
Want presentations that actually deliver meaning? The POET Technique helps teams tell more persuasive data stories using a proven proprietary framework. If you’re ready for narratives that resonate, let’s start the conversation.
