Moving from data to insight and from insight to action

There are different types of organisations in this world and those different types of organisations – be they businesses, charities, third-sector bodies, or Government departments – spawn fundamentally different cultures that endure.

In my space – where we work to empower organisations to make smarter use of data, to drive evidence-based decision-making – there’s a nice distinction to be drawn between how companies move from data to insight and then from insight to action. It’s possible to evaluate the degree to which an organisation uses data to drive performance on a simple 2×2 matrix by how good (or otherwise) they are in both domains. This is captured in the graphic below and the four pen portraits that follow. As the great British statistician, George Box, said in the 1970s, “All models are wrong, but some are useful”. My teams and those client organisations I work with find this model to be very definitely useful.

1. Stuck in the Data Mud

Those that are weak at moving from data to insight and weak at moving from insight to action we can characterise as “Stuck in the Data Mud”. They’re paralysed by over-analysis, risk averse, and ruled by their heads. They’re always searching for that next, last, elusive crumb of data that will finally convince them of what they should do. Their teams that have responsibility for making sense of data – be they termed insight, research, or data science – are siloed from the rest of the business. They love data, but they don’t know how to answer that all-important question “So what?” and so cannot make sense of what the data mean.

A framework for understanding how data flows through organisations (C) 2024 The Insight Agents Ltd

2. Thinkers not Doers

Those that are good at moving from data to insight – who really understand how to answer “So what?”-type questions – but stop there we term Thinkers not Doers. This is because, in the quest to become data-driven and evidence-based, while they can get to a profound understanding of their customers or the issues that matter most to them, they lack the imagination and the entrepreneurial spirit required to leap from insight to action. They can’t forge the transition from a profound understanding to a useful one.

Thinkers not Doers are data hounds who know how to build a data infrastructure. They get systems talking to one another and can triangulate different data sources to give a rich account of the truth. Living in a data-driven, data-first culture, they successfully integrate data science with other functions in the business, not as a slave function but as a wellspring of energy and creativity. They’re rational AND emotional, driven by ideas, and see the value of exposing the R&D team to both internal and external stakeholders.

But that’s where it ends. They find it incredibly challenging to innovate or develop new products, services, or offers based on their understanding of what the market might want or need.

3. Doers not Thinkers

By contrast, there are also organisations that find the data to insight journey hugely challenging. They can’t work out what the data mean, and they often seek help from third party research and analytics partners to do that part of the evidence-based decision-making journey for them. It’s not that they think it’s beneath them, but they’re data-shy, gut-based, ruled by their hearts not their heads. They tend to be victims of Confirmation Bias and “That’s The Way We Do Things Around Here” syndrome, stifling innovation because they can’t join the dots between multiple data sources.

However, once they get to insight (or are led there by their insight and analytics partners), they spring into action. They’re very able indeed at addressing the more interesting and often productive “Now what?”-type questions; “What should we do as a result?”

The problem with subcontracting insight generation to third-party partners is that this approach can miss nuance and subtlety. Partners can only ever, by their very nature, have a partial view. Doers not Thinkers might appear to get a lot done. But if that action is headed in the wrong direction, this can leave these organisations outflanked by more genuinely insightful competitors. Doers not Thinkers are farmers rather than hunters, and while their activity levels may appear to be frenzied, frenzied activity in the wrong direction can be hugely counterproductive.

4. Doers who Think

The final category has a thumb up in both camps. Doers who think are adept at both strategy and tactics. As evidence-based data storytellers, they integrate data science and analytics capability in all relevant functions (which today means all functions). Blending the emotional and the rational, all their briefing documents and templates lead off with questions or statements focused on the insight that drives the brief. Doers who Think are entrepreneurial hunters and innovators who regularly outflank the competition because they have a through line from multiple, integrated data sources from “So what?” to “Now what?”. Their data management and integration systems are embedded in the business and action is calm and considered rather than frenzied.

Upping your data, insight, and action game

Moving from data to insight and insight to action requires guardrails and frameworks. This is because thinking about thinking – getting metacognitive as philosophers say – is hard. As the Princeton psychologist, Daniel Kahneman, says in his 2011 book Thinking, Fast and Slow, “Thinking is to humans as swimming is to cats. They can do it, but they prefer not to.”

Insightful thinking is particularly difficult because it’s a different mode of cognition from analytical thinking, which is how most of us are rewarded at school, university, and in work. But frameworks for insightful thinking do exist and do work reliably. Frameworks like our own STEP Prism of InsightTM, which sits at the heart of my 2020 book, How To Be Insightful: Unlocking the Superpower that Drives Innovation.

‘How To Be Insightful’ is also the second module in our increasingly-popular online training course, Using Data Smarter. To find out if this is the course for you or your team, why not complete our data storytelling scorecard. 12 questions – all of them smart – and three minutes of your time, and we’ll send you a personalised report showing where you’re already doing well as a data storyteller and also where you might need to focus to improve. Either click the link above or click on the image below.