Organisations have never had more access to data, analytics and AI. The technology is more powerful, the infrastructure is more sophisticated, and the ability to generate insights is greater than ever. Yet one fundamental challenge remains: getting people to actually use these capabilities to make better decisions.
For Elvis, this is where the real data conversation begins.
Technology solves a technical problem, and the adoption problem is human.
These are two very different measures of success. A dashboard sitting on a server that nobody opens is ultimately of little value. The more important question is not “Do we have the tools?” but “Are our people using them to make better decisions?” That question requires a cultural answer.
Every major wave of enterprise technology investment has eventually encountered an adoption challenge: Enterprise Resource Planning (ERP) systems in the 1990s, Customer Relationship Management (CRM) platforms in the 2000s. Today, data and analytics are facing a similar challenge.
Elvis Odiase is a France-based data analytics expert and the founder of Data First Workplace™, a platform focused on advancing data culture and analytics adoption across organisations. A graduate of Université de Rennes 1, France, and the Federal University of Technology, Owerri, Nigeria, his work sits at the intersection of data, people and organisational culture.
He developed the Adoption Intelligence Framework™, which helps data leaders bridge the gap between data investment and adoption, and is the author of Decisions Over Dashboards. He convenes the National Data Culture Symposium (NDCS) in Nigeria and the Nigeria Data Awards.
The tools have become increasingly powerful, but technology does not change behaviour on its own. People change their behaviour when they understand why something matters to them, trust the information they are being asked to act on, and work within a culture that encourages and rewards the use of data.
For organisations seeking to close this gap, the approach should begin with an honest assessment of their existing data culture: understanding where they are today, identifying the specific human and behavioural barriers limiting adoption, and building an adoption programme designed to address those barriers directly.
From data to decisions: what it really means to be data-driven
It means that when a decision needs to be made, the first instinct is to ask, “What does the data say?”, not as a formality or simply to confirm an existing belief, but as a genuine input into the decision-making process. It also means being willing to challenge assumptions when the data tells a different story. In a truly data-driven organisation, data literacy is not a skill reserved for the analytics team; it becomes a baseline expectation across the organisation.
At the heart of this is trust. Adoption is in many ways a downstream consequence of trust. If people do not trust the number on a dashboard, they are unlikely to act on it. Instead, they may return to their spreadsheets, rely on instinct, or defer to a manager’s opinion. Organisations seeking to improve analytics adoption therefore need to examine trust first:
- Where does it exist?
- Where has it broken down?
- And why?
These questions are at the foundation of the Adoption Intelligence Framework™, which was developed to map the organisational, cultural and behavioural barriers that influence trust and analytics adoption. The premise is straightforward: you cannot fix what you have not diagnosed.
The trust factor: why people act on data they believe in
Trust in data is built over time. It comes from consistent accuracy, transparent methodology, and honest communication when something goes wrong. Employees begin to trust data when they repeatedly see it produce reliable insights relevant to their work. But that trust can disappear just as quickly when an error is discovered and nobody acknowledges it, or when leadership selectively uses numbers to support a conclusion that has already been reached.
For leaders, trusting data requires something more. It means being willing to act on findings even when they are inconvenient: restructuring a team because the performance data points in that direction, cancelling a project because the evidence does not support it, or acknowledging publicly that a strategy is not delivering the expected results.
Trust in data is not built by asking people to trust it. It is built through consistent action.
When the numbers say no: navigating data that contradicts leadership
This is perhaps the ultimate test of an organisation’s data culture. How leadership responds when the data challenges its expectations reveals a great deal about how seriously the organisation is prepared to use evidence in its decision-making.
Rather than responding defensively, leaders should approach unexpected findings with curiosity: “What is the data telling us that we may not have understood?” The objective is not to make the data fit the expectation, but to understand what the evidence is revealing and allow that insight to inform the next decision.
The real danger is when uncomfortable findings are met with resistance. Organisations may begin questioning the methodology or searching for reasons to discount the result, not because the data is flawed, but because the conclusion is inconvenient. A mature data culture is not one in which the numbers always confirm what leadership wants to hear. It is one in which leadership is willing to listen, investigate, and change course when the evidence demands it.
Fairness in the numbers: building trust through consistent data practices
Consistency matters because people pay attention not only to the data itself, but also to how the organisation uses it. If employees see that data-driven standards are applied selectively, they may begin to question whether the organisation is genuinely committed to a data-driven culture. You cannot build a sustainable data culture where evidence is rigorous for some decisions but optional for others.
Bridging the divide: connecting data teams, leaders and end users
Collaboration plays an enormous role in building trust and driving adoption. The challenge is that these groups often speak very different professional languages. Data professionals tend to speak in terms of methodology, accuracy and technical validity. Business leaders think in terms of outcomes, priorities and risk, while end users are often concerned with how data fits into their day-to-day work.
This communication gap is one of the most underestimated barriers to adoption. When these groups operate in silos, organisations can produce technically brilliant dashboards and analyses that never influence a single business decision.
That same culture must also create room for people to question the data. A culture where people cannot challenge a number is not truly a data culture. In fact, some of the most dangerous dashboards are the ones nobody questions. Organisations must make constructive challenge part of the way they work: normalising questions in meetings, recognising people who identify errors, and ensuring that challenging the data is seen as a contribution rather than a confrontation.
Recognising the champions
Every organisation has people who naturally gravitate towards data: those who build unofficial dashboards, ask for evidence in meetings, or take the time to help colleagues understand what the numbers are saying. These people are your culture carriers.
Recognising and elevating them does more than reward individual effort; it sends a clear signal about the behaviours that are valued. Recognition is not simply a reward. It is a cultural signal. What an organisation chooses to recognise tells people what good looks like and what behaviours are worth repeating.
The inspiration behind Data First Workplace™
“Honestly, frustration. I kept seeing the same pattern across organisations: significant investment in data tools, but limited returns from those investments and very little attention given to understanding why. The industry conversation was largely centred on technology. The human side of the equation was often treated as an afterthought.”
“I created Data First Workplace™ because I believed, and still believe, that the next frontier of competitive advantage lies in building stronger data cultures and improving analytics adoption. So I decided to build what I believed was missing.”
The AI workplace: why data literacy matters more, not less
The workplace of the future will look less like a command centre filled with dashboards and more like an environment where data fluency is as fundamental as financial literacy. AI will take on much of the heavy lifting involved in analysing data, but that does not make human understanding less important. If anything, it raises the value of interpretation, judgement, context and action.
When AI can generate an answer in seconds, the more important questions become:
- Does the answer make sense?
- What does it mean for our organisation?
- What should we do with it?
One piece of advice for building stronger data cultures
Stop measuring your data maturity by the tools you have deployed and start measuring it by the decisions your people are making differently because of data.
The tools are a means to an end. The real goal is better decisions, made with greater confidence and less reliance on politics, assumptions or hierarchy. People change when leadership models the behaviour, recognises and rewards it, and everyone from the boardroom to the frontline understands that data is not the responsibility of the data team alone. It belongs to everyone.
