By taking action with conviction but also being able to look at impact more objectively, any potential failures can be seen as a way of gathering more data that tells them what might be likely or less likely to work in the future.

Today we have access to so much more data than we ever have had in the past, and many organisations want more employees to be data literate and data-informed. Yet people are often fearful of data and some can't even get beyond quite negative perceptions of it.

Often we look to quantitative metrics for baseline data because they serve as straightforward data, especially in areas like sales, revenue, or profit margins. However, in many scenarios, relying on qualitative data for your baseline is so valuable because this kind of data cannot be so easily retrofit.

In the realm of data storytelling and analytics, trust is paramount... as without trust, the use of the data falls flat and people won't use the data to its full capacity. To build trust, we have to improve the validity and reliability of the data we have, so that people are motivated to use the data and take appropriate evidence-informed action.

When writing your survey questions always keep in mind your end goal — what will be done with the data you collect and what CAN you do something about?

Understanding trends and insights is pivotal in the realm of data storytelling. When we sit with the data and ask ourselves what the trends and insights are, the next question is what to do about them

When it comes to operationalising the way we use data in our organisations, I focus on three separate, but interconnected areas: data literacy, data visualisation, and data storytelling. Even though the goal is to get to the point of data storytelling, these elements are not sequential steps or a linear progression from left to right; they are, in fact, more fluid.

The combination of qualitative and quantitative data unlocks profound insights into our customers, teams, and organisations, and helps us understand organisational dynamics.

In our work, and just like in our physical map, it is important to think about the types of data to use in triangulation, that will help broaden our understanding of the phenomenon that we're investigating. The data sets will be different (of course), but we want the three to align as closely as possible. A good way to think about it is, 'what three sets of data will tell me the most useful information about [this focus]'?

One of the questions that I'm asked more than anything else is to explain the distinction that I make between being data-informed and data-driven, and why it's so important to get right. I believe that we should only ever use the language of being 'data-informed', and that we should stop using 'data-driven' completely.