Recently I’ve been seeing a growing trend from IT teams and data leaders preaching the importance of data literacy. Don’t get me wrong, there needs to be, all‑round, a better understanding of how data can be applied—or misapplied—to almost any problem, BUT (and this is a big but) data is only the means to an end; it’s the kinetic change in an outcome because of that data and calculus that creates value.
So why the big push for data literacy, and—if it’s not the right push—what do I mean by solution literacy?
Data Literacy — placebo for asking all the questions you should have
Once again this series is quickly becoming a confessional for the sins of my past. As technologists we love to design a technical solution and define the path to get there as a data problem. How many times have you sat there and presented your “must‑be‑perfect” solution and watched your customer’s soul drain as they try to understand exactly what it is you’re saying to them? (I can admit I’ve done this more than once.)
We all need to recognise that to our customers, success is rarely a technical outcome, but rather something that creates a kinetic change in how a decision, process, or outcome is executed.
If you interrogate data long enough it will give any answer you want it to.
The truth is, it’s far easier to define a problem that can be implemented if we define it as a data problem. After all, that’s what most of us spent hours upon hours studying. We understand the tools, the teams needed, and most of us enjoy seeing it all come together. Yet without clearly understanding what is being impacted, it gets very hard to measure or optimise for performance and relate that back to the customer.
It’s at this point we need to recalibrate how we understand the transformation being asked for and tackle this in a new way — enter Solution Literacy.
Yes, I made up Solution Literacy — but it makes sense when you think about it
Recently I’ve been spending a lot of time talking to executives who are either implementing Applied AI use‑cases or at the beginning of their journey creating (or recreating) an Applied AI strategy. The common thread? Too much focus on the tools and tech, and not enough focus on the problem itself.
So how do we judge our team’s level of solution understanding?
We just might have known the answer all along.
It turns out there’s a well‑documented framework for validating literacy and understanding. Many of us will remember it from school: reciprocal teaching — a dialogue between teacher and student built around summarising, questioning, clarifying, and predicting.
Try this experiment
Bring your project team into a meeting and workshop these four steps:
Summarise – Write a 150‑word outline of the project without talking about any technology.
Clarify – Identify areas that need clarification and list options for those uncertainties.
Question – Think bigger. What else might be possible? What may have been missed?
Predict – Ask the team to predict outcomes. What might come next?
Review the four stages with the team. Do they agree? Would the project stakeholder understand it? If the answers are yes and yes, write it up and share it.
Congratulations — you’re on your way to developing clear solution literacy across your team. Now it’s time to unleash all the data literacy you can find.
Good luck.