Putting the Human back in the Data: Using Data to Lead Change

When you think about data, what is the first thing that comes to mind? Do you love it? Or do you think you’re “not a numbers person”? Do you have concerns about security? Or validity of the data you collect? Or do you think of the technology that you use? Does your mind jump straight to the targets set by the leaders in your organization? And whether or not you’re likely to achieve them?

We all have different reactions to the notion of data and recognizing and understanding that is one of the first hurdles that we need to overcome. Our perceptions of data are influenced by our experience in school math lessons, different work environments; and interactions with and expectations of our colleagues, teams, and organizations throughout our career. If you think negatively when you hear the word “data”, I’d encourage you to think about “people” and “potential” first, rather than hard data, or reducing people to numbers. I certainly do.

Numbers are insignificant in spreadsheets and visualizations alone—we desperately need to remember that while some of us collect millions of data points daily, most have come from human beings, and all should be used by human beings.

I’m a data storyteller, and although it is all about the numbers for me, it is also not about the numbers. Numbers are insignificant in spreadsheets and visualizations alone—we desperately need to remember that while some of us collect millions of data points daily, most have come from human beings, and all should be used by human beings. Unfortunately, when we focus only on building data lakes, data strategies, and the best security, we might forget that the data is actually about humans and is being collected for humans to use.

Regardless of the organization and role that you find yourself in, your priority in using data should be to get to the point of data storytelling, and put people at the forefront of your conversations and thinking. Data storytelling is where we think about the insights in the data, and how we communicate them and use them to lead change. When we look for insights, either as individuals or teams, we trawl through the oysters to find the pearls1 and choose the trends that we can do something about (or shift) in our sphere of influence.

Once we have identified the pearls in the data, we need to be able to share these insights and explain them to others in a way that engages them in the story and helps them see the urgency in action. If we are unable to do this, there is a good chance that the insight will not be acted on, and it will remain as something that was interesting but not actioned. By being able to communicate the story of the insight and engage others in the story we then, in teams, decide on potential action and next steps. At all stages of data storytelling, we engage people in the conversations, thinking, and decision making, and we think about how the data and insights reflect human experience, and what it means for people in your organization or team moving forward.

One of the main ways that we can ensure that we put humans back into the data through the process of data storytelling is to insist on colleagues, teams, and our organization always being data-informed rather than data-driven. Organizations that promote data-driven strategies run the risk of focusing on the numbers rather than people; whereas organizations that are data-informed, ensure that their understanding of people, contexts, and other contributing factors influence the decisions that they make.

Data-informed versus data-driven

                When talking about the ways in which data can be used, there is an important distinction to be made between being data-informed (which is what we want to be) and being data-driven (what we do not want to be). Being data-driven is like a horse wearing blinders in a horse race—they can see the finish line and the goal, but they can’t see what is going on on either side of them. They race towards the finish line, with minimal distractions, and a limited understanding of what other horses and riders are doing. Data-driven organizations are ruthless around the numbers. They move staff on if they don’t meet targets; they change their product lines to increase market share, and they callously make all the big decisions based on what the numbers suggest will work. I do not believe that organizations should aspire to be data-driven, because despite the fact that I am a numbers person, the data (particularly if you’re relying on one piece of quantitative data) can never tell you the whole picture.

                Conversely, being data-informed is like being a racehorse without blinders. They can see the goal and the finish line, and they know what they are aiming for, but they can also take in the speed of horses around them, their position relative to others, and slight shifts in movement from horses on all sides of them. There is a finish line, they are working towards it, but they are aware of the context they’re in. Being data-informed in business is much the same. When you are data-informed, you use the numbers and rely on them to provide information about where you are going and what you need to do to improve, but you also incorporate your understanding of context, people, the financial climate, market demand, and company culture into the decision-making process. When you are data-informed, you don’t make decisions driven by the data—you make decisions that are informed and influenced by the data. Organizations should always aspire to be data-informed if they want to effectively harness the power of data but never be driven by it.

                The aftermath of the September 11th United States terrorist attack is a tragic example of data-driven decision-making gone wrong. Ken Feinberg’s book What is Life Worth? The unprecedented effort to compensate the victims of 9/112and the subsequent film Worth, directed by Sara Colangelo3, both document Feinberg’s work as the US Government’s Special Master of the September 11th Victim Compensation Fund. This fund had the enormous challenge of compensating thousands of families for their losses due to the attacks. It was tasked with coming up with a dollar figure for each life lost, taking into consideration income, age, and marital status. Feinberg’s team’s initial approach was data-driven, as essentially there was a formula, where demographic details were entered to develop a payout figure for each person. The victims’ families quickly realized that this algorithm led to significant disparities in payout figures. They were angry. People questioned why their relative was not “worth” as much as others; it was heartbreaking. Over time, as Feinberg met more families and heard their stories, his approach changed. He learned of different contexts with partners and children, and he attempted to find solutions for longer-term illnesses beyond the two-year program. Ever so slowly, Feinberg and his team modified the fund, built trust with families, and achieved the threshold number of families signing up for the fund. In the end, the fund was responsible for more than 5000 families receiving over $7 billion in compensation. Although it was, in many ways, an impossible task, the initial data-driven approach was never going to work.

        Amazon founder Jeff Bezos is a successful business leader who is data-informed rather than data-driven. This might come as a surprise, as many people assume that Bezos is, in fact, data-driven. However, Bezos once said:

“People think of Amazon as very data-oriented and I always tell them, look, if you can make the decision with data, make the decision with data… But a lot of the most important decisions simply cannot be made with data.”4

Bezos advocates for a combination of data and gut to inform decision-making, rather than being driven solely by the data, and he is very comfortable talking about the importance of being data-informed. Take, for example, the launch of Amazon Prime. Bezos reported that the numbers indicated that Amazon Prime would not be successful. If he had considered the numbers only, he would not have pursued what is now a key element of Amazon’s success. Despite the numbers indicating it might not work, Bezos understood the broader context and emerging market around the idea and decided to go with his gut, despite what the data was telling him. Bezos said, “you collect as much data as you can. You immerse yourself in that data… but then make the decision with your heart.”4

Dr Selena Fisk is the author of I’m Not a Numbers Person: How to make good decisions in a data-rich world.5

1 CN Knaflic. Storytelling with Data: A data visualization guide for business professionals. 2015. (John Wiley & Sons).

2 KR Feinberg. What is Life Worth? The unprecedented effort to compensate the victims of 9/11, 2006. (Hachette).

3 Worth, directed by S. Colangelo. (Netflix, 2020)

4 Z. Mejia. “Amazon’s Jeff Bezos: This simple framework can help you answer the most difficult questions you face.” CNBC LLC. November 19, 2018. cnbc.com/2018/11/19/jeff-bezos-simple-strategy-for-answering-amazons-hardest-questions–.html

5 Selena Fisk. I’m not a numbers person: How to make good decisions in a data-rich world. 2022. (Major Street).


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Dr. Selena Fisk

Dr. Selena Fisk is the author of I’m Not a Numbers Person: How to make good decisions in a data-rich world.