In today’s data-driven landscape, transforming complex data into compelling narratives is an essential component of modern business. Andrew Mason, head of data at Grainger, the UK’s largest listed residential landlord, explains how effective storytelling can bridge the gap between data and decision-making. From the importance of data literacy to the impact of generative AI and the productive side of failure, Mason shares how data leaders can navigate the challenges of modern business and foster a positive data culture within their organisations.
Why is data storytelling essential in the modern business world?
Storytelling is the foundation of data communication. You can put lots of hard work into data preparation, but if you can’t communicate your point effectively, that hard work has gone to waste.
Often, the intended audience struggles to resonate with data because it is not conveyed properly. We call it data storytelling, which is really just a way of saying effective data communication. In our world, especially for data professionals, it’s more about story selling; everyone is a salesperson in data. You’re selling insights to the business. So, using data effectively is about selling the story back to the business to encourage an action.
What action do you want people to take? It’s the same when data is used in campaigns outside of the direct business world, whether it’s to get donations for charities or gain votes in politics. It’s about the action that’s being sold, the trick is how to make it appealing to the viewer through data.
In any walk of life, communicating data effectively is important because time is money. We live in a society of instant gratification. People don’t have time to understand charts or decipher algorithms, they need actionable insights to make decisions as quickly as possible.
Using data effectively is about selling the story back to the business to encourage an action
What do you consider to be the key elements of successful data storytelling?
Interestingly, when I ask people this question in a business context, people often say it’s important to include a beginning, middle and end – but that’s not true. In business, issues rarely have a clear end. We are always moving forward and on to the next action.
Fundamentally, when you’re framing a story, it’s important to focus on the characters in context. Who are the big players? What’s their situation? What goal are they trying to achieve? If they don’t act, what’s the potential consequence? The same story can be structured differently for various audiences, pulling on different threads to engage people with different objectives. The last part of any story is about the route to resolution. What did we do? How did we do it well? You can celebrate an outcome, but it doesn’t mean it’s the end; it will inevitably lead to more questions.
What is the current state of data literacy? What hurdles still need to be overcome?
Data literacy is still in its infancy and remains somewhat misunderstood. In the data industry, we often argue semantics, but what’s important is understanding its meaning within your organisation.
For me, it’s about bringing the business along on the data transformation journey, rather than keeping it confined to an ivory tower. As data leaders, we need to emphasise the importance of data literacy, so that no one in the workforce is left behind. For example, at Grainger, which is a 112 year old business, there has never been a greater focus on and recognition for data, as there is now. But a key part of this evolution is breaking down the barriers, showing and delivering the value, and bringing people with you.
Any kind of digital transformation in a business can make people feel uneasy, as it alters long-standing practices. But today, data literacy is crucial. Every employee should be comfortable making data-driven decisions. Just as you wouldn’t roll out other technologies without training, you shouldn’t do so with data either.
How has your experience in the NHS shaped your approach to leadership at Grainger?
Over six years at the NHS, one of the biggest lessons I learnt was the power of cross-collaboration. The NHS is a vast organisation made up of various entities, each specialising in different areas. To achieve effective results, working together well was essential. In data, we rarely deliver value on our own; while we might generate insights, it’s up to the operational teams to implement them.
For example, my team at the NHS provided data to help reduce antibiotic prescriptions in young children, a major initiative to prevent immunity build-up. This involved working closely with many other parts of the NHS, so we were an important cog in a large machine, but just one cog nonetheless.
What’s the biggest challenge you’ve overcome in the last year?
In a single word, it would be ‘failure’. We’ve built a high-functioning team at Grainger, pushing the boundaries of what has been done before. With about 40% of the business using our data platform, we’ve significantly exceeded the industry standard of 25%.
However, as we ventured into advanced analytics, we’ve had to take a lot of learnings when building two specific predictive models that didn’t deliver as we had hoped initially. This was deflating for the team who are eager to get it fully right and meet stakeholder expectations from the get go. On a positive note, we upskilled ourselves and through those initiatives, we uncovered valuable insights.
What is the biggest change on the horizon in your industry?
The buzz around generative AI could be a positive force in the data world. We’ve discussed the importance of data literacy and skills, and AI might help bridge that gap. There are already tools like ThoughtSpot that allow you to ask questions directly about data. For instance, if you want to know how many vacant units are in a building, you can simply type that question instead of extracting data manually. This capability can significantly enhance the accessibility of data. That’s what I’m most excited about – if vendors get it right, it could be transformative.
