Data needs specialists but its nothing without regular people .
In many organisations, there’s an unspoken division: technical people handle the data, and everyone else uses the reports. But this mindset is breaking down—and for good reason.
The truth is, data is everyone’s responsibility now.
Across industries and roles, people are making daily decisions that depend on data—whether it's a product manager prioritising tasks, a teacher analysing student performance, or an football manager reviewing their squad selection. Yet too often, the work of ensuring that data is accurate, meaningful, and fit for purpose is left solely to those with “data” in their job title.
This is a huge missed opportunity.
In todays world I believe that the word data is massively misunderstood, and in a rapidly expanding field people could struggle to see its application to the position.
However, I believe this is due to it being misused in its most basic format and this is because of the way it is portrayed to us in society, our workplaces and the information we interact with on a daily basis.
What is it when the word data comes into your head? Science? Numbers? Maths? Maybe even Microsoft Excel? None of these are wrong - its a personal perspective and they do come into my head when I think about the word Data. Nonetheless, the point that I want to make is there is so much more to the four lettered word than this! Data is information and this can come in many formats, quantitative, qualitative, numerical and textual. In the modern world there's so much we can collect, process, analyse and interrupt that it all fits under the data umbrella. But why does this matter, and why am I writing about it?
Across many industries there are designated data teams that work across an organisation to support with creating, developing and improving data processes and pipelines. The development and growth of these teams is a clear indication of their importance. However, those closest to a process are often best positioned to improve the data it generates. This importance comes from the belief that if we want a desired impact from data in what we do, we must have a true understanding of what it is. As the title of this suggests this has to come from everyone, not just specialists in the field. Everyone works with data, not just individuals that have a direct link to the technical processing of the information.It usually the non-traditional data roles that produce this critical information, and these are the people that can easily flag or fix errors, which leads to the data quality improving. When they don’t, problems compound.
The key is: this isn’t a statistical problem. It’s a process problem. A human one. No amount of downstream data cleansing can fix broken inputs created upstream by flawed processes or misunderstood workflows. That’s why building data quality must start not in the data team, but at the point of origin—with the people who generate and interact with the data every day.
Therefore, collaboration is key and a major mindset shift is needed, data work can’t work in silos. It doesn’t start with ingestion and end with dashboards—it begins at the source, often with non-technical people entering or interpreting information.
The most effective organisations create feedback loops where:
Technical teams make tools and insights accessible - empower don’t isolate
Domain experts own and refine the data they touch - keep what you use
Data leaders build a culture of curiosity and responsibility - license to dig deeper
This turns data from a specialised resource into a shared asset, which in my opinion makes it far more valuable. Then when you have better data, you gain better outcomes. When people outside of data roles are empowered to question, fix, and improve the data they work with, outcomes improve across the board:
Data teams waste less time chasing down bad inputs
Decision-makers gain trust in the numbers
Organisations move faster and smarter
Ultimately, a high-functioning data culture isn’t built on technical skill alone—it’s built on shared ownership. Because in a data-driven world, being “not a data person” can’t be an option anymore and as ‘data people’ we can help this shift. An easy way to create this link and help people relate is to identify the importance of context. Even the cleanest, most structured dataset can be misleading if it’s missing context—and that context often lives with regular people. An analyst can display trends and patterns and highlight outliers but to translate this to meaningful insight, it requires wider background knowledge.
The explanations don’t live in databases—they live in informal conversations, meetings, and people’s lived experience of the work. That’s why it’s critical to include regular people in the data conversation. Their insight doesn’t just fill gaps—it plugs the data into reality. Without that context, we risk treating data as objective truth without wider information being wrapped around it that leads to insights that potentially mislead rather than inform. By involving people who understand the nuances behind the numbers, we can move from shallow reporting to truly informed analysis.
To summarise, data isn't just a technical asset—it's an organisational responsibility. The best outcomes happen when everyone, from front-line staff to leadership, understands their role in shaping, questioning, and improving the data they use. Technical professionals and educators have a unique opportunity—and obligation—to lead this shift by fostering data literacy, enabling collaboration, and building systems that support shared ownership. Because in the end, data isn’t just something we analyse. It’s something we all create, influence, and depend on.
Key Points to take away
1. Data Is Everyone’s Job
Data isn’t just for analysts or engineers—anyone who records, interprets, or acts on information is part of the data process. People closest to the work often generate the most critical data and are best positioned to improve its quality.
2. Context Gives Data Its Meaning
Clean data without context can still lead to poor decisions. The insight behind the numbers often comes from non-technical people who understand the nuance, history, and reality behind what’s being measured.
3. We Need a Culture Shift, Not Just Better Tools
Real data improvement starts upstream—with shared responsibility, open feedback loops, and a mindset that values collaboration over handoff. This shift must be supported by data teams, not dictated by them.
4. Data Literacy Should Be Universal
Everyone should be equipped to ask questions, spot red flags, and understand the impact of data. It’s not about turning everyone into a statistician—it’s about creating a culture where curiosity and responsibility are encouraged.
Resources
Book - People and Data: Uniting to Transform Your Business - By Thomas Redman
Podcast - The Human Side of Data – DataFramed by DataCamp
Interviews and case studies that show how context and interpretation give data its power.