How to unlock actionable insights from data – regularly

According to IDC, the overall global datasphere reached 64 zettabytes in 2020. That’s a LOT of information, and you’d think with all of that available, businesses would be able to unlock actionable insights pretty easily. But that “Eureka!” moment is just as elusive today as it was before the advent of big data.

While most teams within organizations already understand how insights work and how to implement them for strategic plans and tactical action, the challenge lies in doing so regularly and reliably. Too often, insights are viewed as moments of divine creative inspiration – but this is not the case.

Consistent insight generation is actually a teachable skill that can be mastered with the right frameworks in place. By establishing the right processes, teams can use insights to drive regular “Aha!” moments and guide effective business strategy.

What does a valuable insight look like?

In the broadest sense, an insight can simply be defined as something interesting. This could be something that either extends our knowledge beyond what we already know or challenges preconceived ideas. Insights often occur when we suddenly view a problem from a new perspective, allowing us to make connections or identify patterns that were previously unnoticed.

To recognize an insight that adds value in a business context, however, a more precise definition is needed. For an insight to be valuable, it must be both non-obvious and generalizable. It needs to tell us something beyond itself. But most importantly, it must be actionable. If it’s not possible to make changes based on an insight, there is little chance of it leading to a positive outcome. Once project teams understand what type of insights to look for, they can shift their focus to identifying them regularly.

The good news is that there’s a framework teams can adopt to unlock actionable insights, much more regularly.

The three steps to reliably generating insights

Generating consistent insights is often seen as a dark art – you’ve got to have the knack, or the instinct. Actually, there’s a three-step process that can help.

Step 1: Establishing the knowledge baseline

Thoroughly assessing the existing knowledge of a project – identifying assumptions, uncovering hidden biases, and understanding the relationships between various data points – is the entry point of establishing a knowledge baseline.

For example, if your company is conducting market research for a new product, it’s essential to evaluate the target audience’s preferences, industry trends, and competitors’ offerings. This initial assessment forms the foundation upon which to build insights and uncover areas for innovation. (The bonus is that for a lot of businesses, this is the everyday insight all teams need as part of the business 101.)

Step 2: Analysis

Once teams have established baseline knowledge, it’s time to delve deeper into the data through three primary avenues. The first and most obvious approach is to dive straight in with some exploratory data analysis. This is about collecting new information or examining existing datasets more closely.

So, for example, if your company is looking to improve customer satisfaction, you might conduct surveys and interviews to gather additional feedback or analyze existing customer support logs to identify recurring issues.

Another approach (and potential game-changer) is viewing data through different lenses, such as taxonomies, explanatory models, or predictive tools.

For instance, a taxonomy could be used to categorize customer complaints by type, while an explanatory model might illustrate the relationship between staff performance and customer satisfaction; and a predictive tool could forecast the impact of proposed solutions on future customer satisfaction levels. Here, the key is to challenge the project’s common sense by applying frameworks that are unexpected.

Finally, uncovering connections between seemingly unrelated contexts or sectors can also yield valuable insights. For example, a project team could examine customer satisfaction strategies employed by successful companies in other industries. Let’s say you operate in retail – they could look at hospitality or e-commerce, and adapt these strategies to suit the retail environment. (This is otherwise known as analogous design.)

Step 3: Insights elaboration

Once the analysis methods have generated some insights, it’s time to flesh them out. Critical to this is adding a “so what” — explaining why the insight is important and why it affects the project.

One way to do this is by looking for edge cases. Averages are typically well-known. Outliers and edge cases, on the other hand, are often surprising – but it’s important to evaluate how significant they are as a signal.

Also, keep an eye out for contrast. Many well-known macro-trends contain counter-trends. Sometimes these are the result of one maverick doing things differently, but counter-trends can also be an early sign of a pendulum swing. And while statistical relevance is essential, even anecdotal or non-generalizable findings can offer valuable takeaways.

For instance, an innovative solution adopted by a company in a different industry might inspire a groundbreaking new approach in your field. Keep in mind that innovation often stems from what is not average or mainstream, so providing compelling examples to support insights is crucial for their effectiveness.

Making insight generation a teachable skill

Generating consistent insights is a skill that can be cultivated by anyone with the right approach. By systematically evaluating the knowledge baseline, analysing data from various perspectives, applying frameworks, and elaborating on tensions and gaps, it’s possible to unlock the potential for innovation and empower individuals and project teams to contribute meaningfully to the success of your organization.

With this framework in hand, project teams can confidently embark on innovation projects in the full knowledge that actionable insights are out there, waiting to be discovered.

Written by Jonathan Kahan.
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