Why your data culture hinges on both democratised soft skills and hard skills

As data and AI move from specialist domains into the fabric of everyday work, “democratisation” has become one of the most overused terms in the enterprise lexicon.

For some leaders, democratisation means giving everyone access to dashboards. For others, it means teaching non-technical teams to write SQL, build models, or automate workflows. Both interpretations contain elements of truth. But when they are treated as interchangeable, organisations quietly sabotage their own attempts at data culture transformation.

At Data Literacy Academy we often speak about how democratised soft skills and democratised hard skills are fundamentally different capabilities, serving different purposes, for different populations.

Understanding that difference is key to unlocking an organisational culture that is truly data and AI literate.

The skills gap most organisations misdiagnose

Despite years of investment in analytics platforms and AI tooling, most organisations still report frustration with how data is used in practice. Decisions remain slow, evidence is selectively interpreted and often insights fail to lead to concrete action. And responsibility for “getting the data right” remains trapped inside specialist teams.

This is not because business professionals lack access to data. It is because they lack the human skills required to work with it responsibly.

Research consistently shows that the majority of value from data and AI is realised not through advanced modelling, but through better everyday decisions. McKinsey estimates that data-driven organisations are 23 times more likely to acquire customers and 19 times more likely to be profitable, largely due to improvements in decision quality rather than technical sophistication alone. Yet most enablement programmes still start with tools. Yet, truly data-literate organisations are not defined by how many people can build dashboards, but by how reliably evidence is used to inform prioritisation, trade-offs, and risk assessment.

What democratised soft skills actually mean

Democratised soft skills are the human-centric capabilities that allow people to think, communicate, and decide with data confidently, without relying on technical experts to mediate every step.

They are not “soft” because they are vague or optional. They are soft because they are conceptual and behavioural, shaping how people reason rather than what buttons they press.

In practice, this means enabling people across the organisation to interpret evidence, ask meaningful questions, understand limitations, and communicate insights clearly. A key component is building critical thinking into decision-making, not just generating more charts.  And it enables people to challenge assumptions, recognise bias, and collaborate productively with technical teams.

When these skills are not democratised, data literacy becomes brittle. Business teams depend on analysts to interpret results for them. Insights arrive late, stripped of context. Decisions are made based on partial understanding, or worse, false confidence.

Gartner has repeatedly warned that poor decision-making literacy, not lack of data, is one of the biggest constraints on analytics value, estimating that through 2026, organisations that fail to embed decision intelligence will lose significant competitive advantage.

Soft skills are what turn access into understanding.

Why soft skills scale, and hard skills don’t

The uncomfortable truth is that most people in an organisation do not need to become hands-on data practitioners to create value from data and AI. They need to become competent users of evidence.

Studies consistently show that only a minority of roles require deep technical interaction with data. IBM’s global analytics research found that while demand for data literacy is universal, advanced technical data skills are concentrated in a relatively small percentage of roles, with the majority of employees needing interpretive and decision-oriented capabilities instead.

This is why democratised soft skills matter so much. They scale horizontally across the organisation. They reduce bottlenecks not by teaching everyone to code, but by removing unnecessary dependency. Improved decision quality then happens at the point where most decisions are actually made: in meetings, trade-offs, prioritisation discussions, and customer interactions.

Most importantly, they prevent misinterpretation. As AI lowers the barrier to generating outputs, the risk shifts from “can we analyse this?” to “do we understand what this means?” Soft skills are the guardrails that prevent democratisation from becoming distortion.

Democratised hard skills: powerful, but targeted

Democratised hard skills address a different problem, and of course they are profoundly valuable, when focused on the right audience.

These skills aim to make technical capabilities accessible beyond traditional data teams, enabling more people to work hands-on with data. This might include building dashboards, cleaning datasets, running basic statistical analysis, querying databases, or using light automation to streamline workflows.

When deployed thoughtfully, these skills reduce pressure on data teams and accelerate experimentation. Google’s research on analytics maturity shows that organisations enabling self-service analytics in targeted business roles experience faster insight generation and higher adoption of data products.

But democratised hard skills do not scale universally. They require aptitude, time, and ongoing practice. When rolled out indiscriminately, they often concentrate capability in the same technically inclined minority, while leaving the broader workforce untouched.

This is where many organisations go wrong. They equate democratisation with tooling, and tooling with culture change. The result is more dashboards, more reports, and more activity, without a corresponding shift in how decisions are made.

The 80/20 problem most data strategies ignore

Roughly 80% of business professionals need stronger soft skills more than stronger hard skills when it comes to data and AI. They need to understand outputs, not build pipelines. What matters is making sense of evidence, not necessarily engineering it.

Hard-skills-only strategies systematically miss this group.

They train the 20% who already lean technical and label the rest as “non-data people”. And they wonder why data culture never takes hold.

BCG’s research on AI and analytics adoption highlights this exact failure mode: organisations that over-index on tooling without investing in decision literacy struggle to scale value beyond pilot teams. Data culture does not emerge from access alone, it takes a shared understanding and language to grow.

Why Data Literacy Academy leads with soft skills

Data Literacy Academy’s emphasis on soft skills exists to fill a gap in the market that so often already caters to those in the data team or who are self-driven to pick up hard skills. But for the most part, this does not include the majority of business people in large organisations.

We start with how people think, reason, and decide with data because behaviour is the limiting factor in data maturity, not technology. Once people share a common language for evidence, assumptions, and decision-making, technical capability has a solid ground to build from as foundations are essential in shaping the way people think about the hard skills too. And when soft skills are the focal point, all of the other components of data culture like tech adoption, governance, security etcetera rest within a greater framework of understanding for every individual.

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