Many businesses have already taken an important first step: they structured a skills framework and started to map their know-how. These approaches made it possible to better understand human capital and to establish a common language.
But one question remains: how do you move from visibility to action?
Because in many organizations:
- trade-offs remain difficult to objectify
- the priorities for action lack clarity
- HR decisions are still largely intuitive
The challenge is no longer mapping skills. It is knowing how to use them to decide.
This article invites you to take this step: transform your data skills into concrete levers for managing and activating talent.
Mapping skills: useful... but insufficient
Mapping skills is often the first step in skills approaches. And rightly so.
It is a structuring moment: we review the professions, we formalize the know-how, we start to share a common language.
This is where a lot of organizations take an important step forward.
What we observe on the ground
For most of our customers, this phase marks a real breakthrough.
- The repository exists.
The mapping is in place.
The first analyses appear.
And for a while, that's enough: exchanges become clearer, managers find their bearings better, HR finally has a structured base.
But very quickly, a limit appears
Once the mapping has been carried out, a question always comes up: “What do we do with it now?”
Because while cartography allows you to see better, it does not tell you what to do.
What it really allows
- See the skills available
- Compare Levels
- Structuring a common language
- Objectify some discussions
It is an indispensable base.
What it does not allow, by itself
- prioritize actions
- arbitrate between training, recruitment or mobility
- Linking skills to business challenges
- pilot in time
Mapping provides clarity. But it does not yet give direction.
The tipping point
This is often when the process slows down. Not because of a lack of data, but because of the lack of framework to use it.
The cartography then becomes:
- a useful photograph... but punctual
- a tool consulted... but not very activated
- a base... with no real operational extension
So the subject is no longer mapping. But to transform this reading into decisions.
And this is precisely where the main challenge for HR today is: move from skills data... to its concrete use in arbitration.
Why do HR teams have trouble managing skills?
Despite the investments made in recent years, the transition to piloting remains difficult. What we are seeing is not a lack of tools, but a series of very concrete obstacles, at each stage.
Problem 1 — Unreliable or outdated data
In many organizations, skills data exists... but it remains fragile.Two difficulties always come up:
- Identify what is changing: distinguishing obsolete skills from emerging ones remains complex, in rapidly changing business environments.
- Get reliable evaluations: employees and managers struggle to position themselves clearly, due to the lack of shared points of reference.
Governance is still insufficient
Maintaining a living repository requires a clear organization:
- Competency Owner: it is the guarantor of the coherence of skills and their structuring.
- Business expert: it translates activities into concrete skills.
- Business sponsor: it conveys the strategic vision and validates the priorities.
Without this governance, the update becomes irregular... and the data quickly loses value.
Problem 2 — Skills are not used in HR processes
Even when data is available, it often remains outside of operational decisions. HR processes continue to work... without it.
What we observe in concrete terms
Result: the skills data exists... but remains theoretical.
It is neither at the heart of exchanges or decisions.
Problem 3 — No clear framework for deciding
Even when the data is available, a challenge persists: What to do with it in concrete terms?
What we observe on the ground
- Decisions taken on a case-by-case basis: 82% of HR managers arbitrate without a structured framework (IDC, HR maturity 2025)
- Lack of reliable indicators: only 19% regularly monitor skill gaps (Workday Metrics 2026)
- Lack of decision-making process: 64% of businesses don't turn analytics into action (Skillsoft, State of Skills 2025)
What we are seeing is not a lack of intention. HR teams have already come a large part of the way: structuring, formalizing, making visible.
But at this stage, a slippage often occurs. Mapping becomes a point of arrival, when it should remain a starting point.
In the exchanges we have with HR directors, the same question comes up, sometimes formulated simply: “Alright, now that we can see better... how do we decide?”
And that's where it all comes in. Because as long as skills remain a support for analysis, they provide insight. But as soon as they become a medium for arbitration, they really transform decisions.
Integrating skills into HR Processes
It is often at this stage that the nature of skills management changes. As long as the skill remains stored in a repository or map, it informs.
But when it enters HR processes — people reviews, training, training, mobility, succession — it starts to produce something else: choices, priorities, trade-offs.
In other words, the skill becomes a active decision variable.
This switch is also very concrete on the IS level: skills data no longer live alone. They are combined with other signals — performance, aspirations, training history, training history, business criticality, availability, initial risk — and then they are processed to generate a directly usable output.
This is where HR data really comes in handy: not when it describes, but when it guides action.
It is also what makes it possible to get away from a logic that is still all too frequent: that of training driven primarily by compliance or obligation, rather than by the skills gaps actually observed in the organization.
This table of the various HR processes fed by skills data clearly shows the change in logic.
In a classical approach, competence is a store of information.
In a controlled approach, it becomes a Decision-making input, in the same way as performance, aspirations or business constraints.
It's no longer just a repository topic. It is a subject of data orchestration, HR governance and the ability to arbitrate.
And that's precisely when an organization starts to change: when competencies stop being “documented” and become used.
Structuring the trade-offs: Build, Buy, Borrow
At this point, the topic is no longer just finding a skills gap. The subject is choosing the correct answer.
Do we need to develop skills internally? Recruiting from outside? Or mobilise external expertise temporarily?
This is precisely where the logic Build/Buy/Borrow becomes useful.
In fact, HR is already deciding between these three options. But without a clear framework, these choices are often guided by urgency, habits or market tensions.
However, when they are based on a consolidated reading of skills — current level, criticality, timeliness of need, capacity to increase skills, context of transformation — these arbitrations change in nature.
A very concrete example
Take the case of a French bank confronted, in 2026, with the rise in the use of AI in analytics professions.
Reading the skills data reveals a clear signal:
- one Critical gap On competence AI predictive analytics
- A population of vastly undersized data analysts thereupon
- a business projection indicating an increase in 30% of needs by 2027
From there, three scenarios become possible:
- Build : training existing talent
- Buy : recruit profiles that are already operational
- Borrow : quickly secure the need from the outside
And it is precisely the data that makes it possible to decide.
Modeling under the Build/Buy/Borrow triptych
Reading this case
In this example, the right decision is not “Build” or “Buy” or “Borrow” taken in isolation.
The relevant decision is hybrid :
- Build to sustainably treat the core of the internal gap
- Borrow to secure the immediate need for a pilot project
Result:
- a lower cost than a 100% recruitment scenario
- a deadline compatible with business needs
- and a reinforced internal pool for the future
So we are not talking about a fixed model here. We are talking about an ability to choose, based on objectified elements.
Conclusion
Basically, the subject is no longer about structuring skills. It is knowing how to use them to decide.
Because while organizations hesitate, costs add up — without always being visible:
- avoidable external recruitments
- formations started too late
- projects slowed down due to lack of available skills
- employee departures with a lack of prospects
Inaction doesn't show up in a budget. But it can be found everywhere else.
However, the figures are well known:
- 87% of businesses face skills gaps (McKinsey)
- Replacing an employee can cost up to 200% of their salary (Gallup)
But the real issue is simpler: sometimes we pay the cost of inaction.
The difference is that it is diffuse, uncontrolled, and growing.
How much longer can you decide without actually using your skills data?
