The Human Side to Data: Shaping analytics for real-world impact
We are more than numbers!!!
More Than Just Numbers
The world has become more data-driven. We are almost overwhelmed with metrics, dashboards, and numbers. But behind every dataset lies a story of people, struggles, aspirations, and real-life impacts.
As data analysts, our challenge — and privilege — is to connect these dots in ways that don’t just appealingly present facts or numbers but spark change, inform decisions, and resonate on a human level.
This article is a journey through my own experiences with data and the profound role empathy plays in my approach to analytics. Because, ultimately, the most valuable insights aren’t just the ones that highlight trends but those that connect us to the lives behind the numbers. Those numbers are people, friends, family, colleagues, neighbors, strangers, YOU and I.
1. Seeing Data as a Reflection of Real People
The best analytics should start with empathy. Every data point represents someone’s reality — financial challenges, healthcare access, educational opportunities, or business goals.
When I analyze, I focus on “cleaning data” and respect the stories embedded within it.
This mindset shift, from data as numbers to data as narratives, creates relevant and actionable insights.
A recent project I worked on analyzed healthcare access disparities by income level. I had to pause and realize that these figures represent real individuals, and pay particular attention to present findings to highlight their struggles and the potential for change, rather than treating them as mere statistics, because I’m one of those numbers.
When you’re analyzing numbers, you are analyzing people. Your results have a way of impacting real lives. This is why I respect human-centered analysts and analysis. The irony is this, people will pay more for analysts who can understand and relate to their pain points, as well as get the job done.
2. The Storytelling Arc: Data should be understandable and relatable
Data storytelling is about transforming insights into stories people can understand, relate to, and act upon. I have another article on this, and I can’t wait to share it. Data storytelling. A compelling data story has three essential parts:
Context: What question are we answering, and why does it matter?
Insights: What is the data telling us, in simple terms?
Impact: What can we do with these insights to make a difference?
By framing my analyses around these three elements, I help stakeholders understand the “why” behind the data, not just the “what.” My delivery must make sense. It should help the stakeholders or audience understand their conclusions.
Storytelling is an art.
I watched a Netflix documentary of a not-so-liked famous person and just listening to him talk about his entertainment business, I realized that after an episode or two, I almost agreed with his BUSINESS ETHICS, even when I did not care about him. That is the power of storytelling.
The delivery has one mission: understanding and relatable.
In my work, I focus on narratives that drive action, whether it is improving operational efficiency, making better investments, or addressing community needs. Data should be understood.
3. Designing visuals that connect rather than impress
Visualizations are powerful, but so many times, they’re made to look flashy rather than serve the audience.
I love dashboards. I have spent months building dashboards that had to do with nothing, I love looking at dashboards.
Dashboards says “All that data is here, click here, filter here and you have what you want”. But I love clarity more than I love dashboards. I have downloaded tons of tableau dashboards that inspired me and some that made me feel like I have to be an architect to create a dashboard.
My approach is to design visuals that clarify rather than complicate, ensuring that each chart or graph directly supports the narrative I want to convey. It’s not about impressing with design skills — it’s about making sure insights are intuitive and meaningful.
For example, in my healthcare analysis, I used a straightforward income distribution chart that immediately communicated access gaps. A simple line graph highlighting trends in access disparities was more impactful than a complex, multi-layered chart. The goal? To make viewers understand the urgency without needing a data background.
A line chart can sometimes translate a message more than all the twists and turns, and I also like the twists and turns.
Use “create” for stakeholders, leave “develop” for your resumé.
4. Turning Insights into Actions: Data should inspire, not overwhelm
I don’t really like using the word “ultimate”, BUT, the ultimate purpose of data is to empower decisions.
Performing, conducting, and carrying out data analysis has taught me that the most actionable insights are often the simplest. This “less is more” approach allows stakeholders to see, understand, and act without feeling overwhelmed.
My former boss used to say “Give me the ABCs only”. Not all stakeholders know how SQL, Tableau, Python, Apache, Hadoop, R, PowerBI, or even Microsoft Excel work. As an analyst, empathy means remembering that you were once like this, and you need to help others to understand insights.
When stakeholders can quickly grasp the key takeaways, they’re more likely to implement changes, fund solutions, or make informed choices. In a recent engagement, distilling a lengthy analysis of social determinants of health into three key recommendations led to immediate actions by decision-makers — a testament to the power of clarity.
I believe in testing every process, receiving feedback, and optimizing feedback. This way, you can involve non-data enthusiasts in your analytical process.
5. Fostering Empathy in Data Teams: Human-centric data changes culture!!!
Empathy isn’t just a soft skill; it’s a core value that every data team can benefit from. It transforms how we handle data, interact with clients, and build trust within our teams. By focusing on the human side, I’ve found that data teams communicate better, create more relevant reports, and ultimately provide deeper value to the organizations they support.
Culture in this context means humanity. Communication. Person-based. Human-centric. HAND-HOLDING.
Time is money, but happiness and satisfaction are more expensive. And guess what, you can achieve both of them.
My goal with each project is to encourage myself and my team to see beyond numbers, to ask themselves who or what each data point represents, and to challenge themselves to create insights that drive positive impact.
Closing Thoughts: Data professionals should be change makers.
We are more than analysts; we are storytellers, translators, and, most importantly, catalysts for change. In every dataset, there is a potential to make a meaningful difference — if we look for it. We can gradually close the gap between insights and impact by rooting our work in empathy and focusing on the human side of data.
Be data-informed, data-driven, but not data-obsessed — Amy
Thank you for reading. I hope this edition of “Data According to Me” inspires you to embrace the human side of data.
Biz and whimsy: https://linktr.ee/ameusifoh
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