How unlocking skills lies in capturing business data

Jeroen Van Hautte
November 10, 2023
3 min read

TL;DR

A successful skills transformation requires more than just HR data—it demands business-wide insights. Half of the skills demonstrated in business data cannot be found in HR data, making it critical for organisations to integrate both sources for a truly skill-based workforce.

  • HR data only tells half the story – It captures soft skills and career history but lacks real-time insights into functional expertise and technologies.
  • Business data is the missing link – Operational systems, project tools, and documentation provide real-time skill intelligence at scale.
  • Companies must act now – Integrating business data with HR insights enables more accurate workforce planning, better internal mobility, and strategic upskilling.

This guide explores why business data is essential for skills intelligence and how organisations can make skills-based work a reality.

The missing half of the skills equation

A skills-first approach is driven by HR but leveraged by the entire business. Relying solely on HR data creates blind spots, limiting an organisation’s ability to fully map and utilise workforce capabilities.

"If companies want a truly skill-based workforce, they need to look beyond HR data—business data holds the missing half of the equation." – Jeroen Van Hautte

To build a complete skills infrastructure, businesses must infer skill data from operational sources, not just HR records.

Skills are not just for HR

In discussions about skills strategy, the focus often shifts to HR. However, skills power every business function—just as finance teams manage company assets, HR oversees the organisation’s largest cost centre: its people.

Yet, a Skill-Based Organisation (SBO) should not be confined to HR. For skills-based work to thrive, the underlying skill infrastructure must integrate with business processes across all departments.

"Skills exist beyond HR systems. The best workforce decisions happen when business data and HR data work together."

The three layers of skill data

A complete skill intelligence strategy captures skills at different levels:

  1. Soft skills – Leadership, communication, collaboration.
  2. Functional skills – Industry and job-specific competencies.
  3. Knowledge & technologies – Hard skills linked to tools and technologies employees use daily.
"Traditional HR data gives us a view of an employee’s past. Business data tells us what skills they are applying right now."

Relying solely on soft and functional skills enables companies to improve recruitment and talent mobility. However, to connect skills to business operations, knowledge and technologies must be accounted for—something only business data can provide.

Connecting Skills to Business Operations

Business data allows companies to go beyond skill profiles and connect skills to actual work. Integrating operational systems—such as Jira for software teams—enables businesses to:

  • Identify which skills are needed for upcoming projects.
  • Detect skill gaps before they impact business goals.
  • Pinpoint bottlenecks—skills in high demand but held by only a few employees.
  • Assess risks—individuals with unique skills who could create knowledge gaps if unavailable.
  • Track adoption of new technologies across teams.
"If we treat skill data as a static asset, we miss the bigger picture. The real value comes from linking skills to ongoing work."

Capturing business data doesn’t just enhance employee skill profiles—it enables better planning, resource allocation, and workforce agility.

Skill data as a continuous journey

Expanding skills intelligence is not an overnight transformation. The process should be seen as an evolving journey, where value creation and data improvement go hand in hand.

A Three-Stage Approach to Building Skills Intelligence

  1. Start with HR & Learning Data – This provides an initial skills foundation, supporting internal mobility and recruitment.
  2. Layer in Business Data – By integrating project management, documentation, and collaboration tools, organisations create a more dynamic, real-time skill map.
  3. Scale Data-Driven Workforce Decisions – With a full AI-powered skills infrastructure, businesses can move toward predictive workforce planning and strategic talent allocation.

"You don’t need perfect data to start. The key is to build your skills intelligence progressively, layering business data on top of HR insights."

Getting started with skills intelligence

At TechWolf, we work with enterprises to identify existing internal data sources, assess their quality and relevance, and integrate them into a comprehensive, real-time skill framework.

By working with existing tech stacks and adopting an API-first approach, we help companies operationalise skill intelligence at scale.

"Companies that integrate skills intelligence today will be the market leaders of tomorrow. The future of work is skills-based, and the time to act is now."
Jeroen Van Hautte
Co-founder & CTO

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Using AI while interviewing at Techwolf

At TechWolf, we see generative AI as part of the modern toolkit — and we expect candidates to treat it that way too. We love it when people use AI to take their thinking to the next level, rather than to replace it.You are welcome to use tools like ChatGPT, Claude, or others during our interview process, especially in take-home assignments or technical exercises. We encourage you to bring your full toolkit — and that includes AI — as long as it reflects your own thinking, decisions and creativity.We don’t see AI as replacing your skills. Instead, we’re interested in how you use it: to brainstorm ideas, speed up iteration, validate your thinking, or unlock new ways of approaching a challenge. Great candidates show judgment in when to rely on AI, how to adapt its output, and where to go beyond it.

What we’re looking for:

Our interviews are designed to understand how you think, solve problems, and express ideas. Using AI in a way that amplifies those things — not masks them — is encouraged.

What to avoid:

We ask that you don’t submit AI-generated work without review, or present answers that you can’t fully explain. We’re not testing the model — we’re getting to know you, your skills, and your potential. If there are cases where we don’t want you to use AI for something, we’ll tell you ahead of the interview being booked.In short: use AI as you would on the job — as a smart assistant, not a stand-in.

Example: Programming with AI

In a coding challenge, you’re welcome to use generative AI to support your workflow — just like you might in a real development environment. For instance, you might use AI to quickly generate boilerplate code, look up syntax, or get a first-pass solution that you then adapt and debug collaboratively. What we’re interested in is your ability to reason through trade-offs, communicate clearly, think about complexity and iterate effectively — not whether you memorized the syntax perfectly. If using AI helps you stay in flow and focus on higher-level problem-solving, we consider that a strength. There could be some challenges where we won’t allow you to use AI - in that case we’ll tell you in advance, and will tell you why.

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