Workforce Planning with Skills and Credential Data

From credential data to workforce decisions.

This course is part of the ICoBC Credentialing Skills Framework (ICSF). It develops the skill "Skills-based talent practice" (VRITE pillar: Impact) at level L4 "Assure", stage A "Ability". At this level you lead skills-based practice across an organisation and connect credentials with job architecture and workforce planning. At the Ability stage you practise on realistic tasks in a controlled setting. You work with cases and sample data, not with live employee records.

Who it is for

• HR, talent acquisition and mobility teams (C2)
• Executive sponsors such as the CHRO, CLO and CDO (C4)
• L&D and corporate academy leads (C1)

Prerequisite: ICB-P16 Skills-Based Hiring and Internal Mobility with Credentials. You should already know how credentials act as an evidence layer for hiring and mobility decisions.

Learning outcomes

After this course you can:

1. Build a view of skills supply and demand using credential data.
2. Identify gaps and choose between building, buying and borrowing skills.
3. Connect credentials with job architecture.
4. Govern skills data quality and privacy.

Workload. The course is a blended cohort programme with 10 hours of notional learning. This file covers the self-paced part. Live and workshop elements appear as facilitator notes.

How it is assessed. You produce a skills gap analysis for one job family. A person assesses it with the rubric in the section "Assessment and badge". You also answer a final knowledge check. Nothing in this file grades your task.

Badge. On successful completion you earn the badge "Workforce Planning with Skills and Credential Data (Ability, L4)", alignment code ICSF-TALN-L4-A. The badge evidence is your gap analysis.

Status of topics in this course

• Skills-based hiring and internal mobility: established. It is widely discussed and practice varies. Credentials provide the evidence layer.
• AI skills inference: emerging. It is common in HR software. It may fall under high-risk use when used for employment decisions.

Status as of November 2026; check before publishing.

Case used throughout. You work as a workforce planning lead at the corporate academy of a fictional mid-sized logistics and manufacturing group called Nordhafen Group. All names and numbers are illustrative.

Skills data

From credentials to a skills inventory

Workforce planning asks a simple question: which skills do we have, and which do we need? Credential data helps answer the first half. A credential, such as a digital badge, a certificate or a degree record, is evidence that a person was assessed against a standard. Each credential can carry skills information: a skill name, a level, an issuer and a date.

A skills inventory collects this information across the workforce. Credential data is only one source. Other sources include self-assessments, manager ratings, project records, learning records and, in some organisations, skills inferred by software. Each source has a different strength of evidence.

A useful way to think about evidence is a ladder:

• Assessed and verifiable: a credential from a known issuer, with a defined standard and a verification link.
• Observed: a manager or peer confirms the skill in real work.
• Claimed: the employee states the skill in a profile.
• Inferred: software suggests the skill from text or activity.

Your inventory should record the evidence type next to every skill record. Without it, a planning view mixes strong and weak data and gives a false sense of certainty.

Building the supply view

Supply is the number of people who hold a skill at a defined level, now and in the near future. To build it, follow these steps.

1. Choose a skills taxonomy or framework. Use one common vocabulary. If your organisation has several, map them to one reference.
2. Collect skills records from each source and tag them with the evidence type.
3. Remove duplicates and match skill names to the common vocabulary.
4. Count people per skill and level. Count only records that meet your evidence rule, for example "assessed or observed".
5. Adjust for time. Credentials expire or become outdated. Include expected leavers, retirements and planned moves.

Supply is a range, not a single figure. State your assumptions next to the number.

Building the demand view

Demand is the number of people with a skill at a level that the business needs. It comes from strategy, not from the inventory. Sources include the business plan, project pipelines, product roadmaps, regulatory changes and job requisitions. Translate each plan item into skills and headcount. For example, a new automated warehouse needs people who can maintain robotic systems.

Demand is always uncertain. Use scenarios, such as a base case and a growth case, rather than one forecast.

Figure: an illustrative supply and demand view. The numbers are invented for teaching.

Worked example and practice

Worked example: Nordhafen's first supply view

Amira Okafor leads workforce planning at the Nordhafen corporate academy. The board plans to automate one warehouse within 18 months. She needs a supply view for "robotics maintenance".

1. She picks the group's skills taxonomy and maps the academy's badge skills to it.
2. She pulls three sources: academy badge records, manager-confirmed skills from performance reviews, and self-declared skills in the HR profile.
3. She tags each record: badge records as "assessed", manager records as "observed", self-declared as "claimed".
4. Her evidence rule is "assessed or observed". Under this rule she counts 12 people at working level. If she counted claimed skills too, the figure would be 31.
5. She reports supply as "12 confirmed, up to 31 if claims are verified" and adds a note that two of the 12 retire within the planning period.
6. Demand from the automation project is 30 people at working level. The shortfall is between 18 and 19 on the confirmed figure.

Amira shows the range, because the choice of evidence rule changes the answer. All numbers are illustrative.

Practice activity: Build a supply and demand view

You receive a small sample table (provided by your facilitator or created from the model below) for one job family with 20 to 30 people. Each row lists a person ID, a skill, a level, a source (badge, manager, self-declared, inferred) and a date.

1. Define your evidence rule in one sentence.
2. Count supply for three skills under your rule and under a looser rule.
3. Write demand for the same three skills from a short business scenario.
4. Present supply and demand in a table, with a range for supply and a note on assumptions.

Keep this table. It becomes part of your assessed gap analysis.

Check your understanding: Skills data

Gap analysis

Defining and sizing the gap

A skills gap is the difference between the demand for a skill and the supply of that skill at a defined level and time. A gap analysis puts supply and demand side by side and decides what to do about the difference.

Good gap analysis answers five questions for each skill:

1. How large is the gap, and in which period?
2. How critical is the skill for the business?
3. How quickly can the gap close by different routes?
4. What does each route cost, and what risk does it carry?
5. Who owns the decision?

Not every gap deserves action. Rank gaps by two factors: business criticality and size. A small gap in a critical safety skill may matter more than a large gap in a skill that is easy to source.

Also look for surpluses. A skill with more supply than demand is a source of internal mobility. Surpluses can fill gaps elsewhere through reskilling, which links back to your work in ICB-P16.

Build, buy or borrow

Three routes close a gap.

• Build: develop current employees through learning, credentials, projects and mentoring. This route is often slower to start, builds loyalty and uses the corporate academy.
• Buy: hire people from the external market. This route can be fast for common skills, but costs more and carries onboarding risk.
• Borrow: use contractors, partners, consultants or temporary assignments. This route gives flexibility and speed. Knowledge may leave when the contract ends.

Use clear decision criteria. Typical criteria are time to fill, cost, availability in the market, strategic importance, how long the need will last and the risk of losing knowledge. A skill needed for two years only may suit borrowing. A skill that is core to your future products usually suits building, with some buying to start.

Most gaps use a mix. For example, buy two senior specialists to lead, build ten technicians through a credential programme and borrow a vendor-neutral integration partner for the first six months.

Figure: a simple decision flow for the build, buy or borrow choice.

AI skills inference in gap analysis

Some HR software infers skills from text such as CVs, job descriptions and project records. This practice is common in HR software and is an emerging topic. Inference can fill gaps in an inventory quickly. It also carries risks. Inferred skills sit at the bottom of the evidence ladder. They may be wrong, they may carry bias from training data, and employees may not know that the inference happens.

Treat inferred skills as hints for review, not as facts. When you use inferred data in gap analysis, label it, keep it separate from assessed data and test a sample against human judgement. When inference supports decisions about hiring, promotion, task allocation or dismissal, it may fall under high-risk use. In that case legal and regulatory duties can apply, such as human oversight, transparency and documentation. Ask your legal and data protection colleagues which rules apply to you.

Status as of November 2026; check before publishing.

Worked example and practice

Worked example: Closing the robotics maintenance gap

Amira's analysis shows a shortfall of about 18 people in robotics maintenance within 18 months. She works through the five questions.

1. Size and period: 18 people, needed by month 18. A first group of 6 is needed by month 9 for commissioning.
2. Criticality: high. The warehouse cannot run safely without them.
3. Speed: buying from the market is possible for 4 experienced technicians, but the market is tight. Building takes about 12 months for technicians with an electrical background.
4. Cost and risk: buying is expensive and slow to onboard. Building is cheaper per person but misses the month 9 target. Borrowing from an integrator gives immediate capacity but leaves knowledge outside the company.
5. Owner: the head of operations owns the decision, with the academy and HR as advisers.

Her recommendation, with illustrative numbers: buy 4 experienced technicians to lead, borrow 6 contractors for months 1 to 12, and build 14 technicians from the surplus pool of 25 warehouse operators through an assessed credential programme. She also notes a surplus in manual picking skills as a source for the build route.

Practice activity: Choose a route for three gaps

Use your supply and demand view from the first module.

1. Pick three gaps from your table. Include one critical gap and one surplus.
2. For each gap, answer the five gap analysis questions in one or two lines.
3. Choose build, buy, borrow or a mix, and give two criteria for your choice.
4. Write one sentence on how you would treat inferred skills data if it were in your sources.

Check your understanding: Gap analysis

Job architecture

What job architecture is

Job architecture is the structure that organises jobs in an organisation. It usually defines job families, job functions, career levels and job profiles. A job family groups related roles, for example "Warehouse operations". A level shows the scope and complexity of work. A job profile describes the purpose, tasks and requirements of a role.

Traditional job architecture describes jobs by titles, tasks and qualifications. A skills-based approach adds skills to the profile: which skills the job needs, at which level, and which are essential or desirable. This gives workforce planning a common language. Demand from business plans can be written as jobs and skills. Supply from credentials and other evidence can be matched to the same skills.

Attaching skills and credentials to jobs

To connect credentials with job architecture, work in four steps.

1. Define skills per job profile. For each profile, list the core skills and the level needed. Keep the list short. Five to ten core skills per profile is usually enough.
2. Distinguish requirement types. Mark each skill as essential at entry, essential within a set period, or desirable. This avoids rejecting people for skills they can learn on the job.
3. Link credentials as evidence. For each skill, list which credentials count as evidence. Use ones with a clear standard and a known issuer. Accept equivalents. Where no credential exists, name other evidence such as a work sample or manager observation.
4. Link profiles into pathways. Show how skills carry between profiles. A path from "Warehouse operator" to "Robotics maintenance technician" shares some skills and needs a few new ones. These pathways support mobility and make the build route visible.

Credentials are evidence of a skill. They do not replace judgement. Completing a course or earning a badge does not prove that a person can do a whole job. Treat credentials as one input to selection and development decisions.

Avoid two common mistakes. The first is to list too many skills per job, which makes profiles unusable. The second is to turn a credential into a hard barrier when other valid evidence exists.

Figure: job architecture with skills and evidence attached to one profile.

Worked example and practice

Worked example: Writing the robotics technician profile

Tomasz Nowak, an HR business partner at Nordhafen, works with Amira to add skills to the new profile "Robotics maintenance technician".

1. He lists six core skills: mechatronics basics, robot fault diagnosis, preventive maintenance, safety management, technical documentation and teamwork on shift.
2. He sets requirement types. Mechatronics basics and safety management are essential at entry. Robot fault diagnosis is essential within 12 months. Technical documentation is desirable.
3. For each skill he lists evidence. Safety management accepts an assessed academy badge, an equivalent certificate from a professional association or a manager-observed work sample. He writes "accept equivalents" in the profile.
4. He draws a pathway from "Warehouse operator". The operator already holds safety management and teamwork. The gaps are mechatronics basics and fault diagnosis. These become the learning outcomes for the academy's build programme.
5. He records that the badge is evidence for one skill and not a statement that the holder can perform the whole job. Hiring and promotion decisions also use interviews and work samples.

Practice activity: Attach skills and credentials to one profile

Choose one job profile from your job family.

1. List five to eight core skills with levels.
2. Mark each as essential at entry, essential within a period or desirable.
3. For each essential skill, name acceptable evidence, including at least one non-credential option.
4. Draw or describe one pathway from a neighbouring profile and list the skills gap along that pathway.

Check your understanding: Job architecture

Governance

Data quality

Skills data supports decisions that affect people. Poor data leads to poor decisions. Governance sets the rules to keep the data fit for use. Start with data quality.

Check the data against five dimensions:

• Accuracy: does the record show what is true?
• Completeness: are required fields filled, such as skill, level, date and source?
• Timeliness: is the record current? Skills and credentials change.
• Consistency: do sources use the same vocabulary and levels?
• Provenance: do you know where the record came from and who can confirm it?

For credential data, provenance includes the issuer, the standard, the assessment method and whether the credential can be verified. Where possible, use open, standards-based credential formats so that data moves between systems without manual retyping. Examples of such standards appear in the sources section.

Assign ownership. Every skills data set needs a named owner, a refresh cycle and a way for employees to correct errors. Measure quality with simple indicators, such as the share of records with an evidence type, the share older than 24 months and the number of correction requests.

Privacy, fairness and AI

Skills data about identifiable people is personal data. In many places, including the European Union, data protection law applies. Core principles include a clear purpose, a lawful basis, data minimisation, transparency, accuracy, limited storage and security. Employees should know what is collected, why, who sees it and how to challenge it. Ask your data protection officer and legal team to confirm which rules apply to your organisation and country.

Plan with the least data needed. Aggregate where you can. A workforce plan usually needs counts by skill and job family, not named individuals. Use individual records only where the purpose needs them, such as a mobility match, and limit access.

AI skills inference needs extra care. It is an emerging practice that is common in HR software. If inferred skills influence decisions about hiring, promotion, task allocation or termination, the use may fall under high-risk rules. Typical duties in such cases include risk management, data governance, documentation, transparency to affected people and human oversight. Check the rules that apply to you.

Status as of November 2026; check before publishing.

Practical safeguards for inference:

• Tell employees that inference is used and for what purpose.
• Keep inferred skills labelled and separate from assessed skills.
• Do not use inferred skills as the sole basis for a decision about a person.
• Test for different results across groups, and fix problems you find.
• Let people see and correct their data.
• Keep a log of model changes and decisions.

Figure: a four-step governance cycle for skills data.

Worked example and practice

Worked example: A data review at the academy

Nordhafen's data protection officer, Elena Marchetti, joins Amira for a quarterly review of the skills data set used in workforce planning.

1. Purpose and owner: the purpose is "workforce planning and internal mobility matching". The owner is the head of workforce planning. The data is not used for performance ratings or pay.
2. Quality check: a sample of 100 records shows that 88 have an evidence type, 9 are older than 24 months and 3 have inconsistent skill names. Elena and Amira set a target of 95 per cent with evidence type by next quarter. These numbers are illustrative.
3. Privacy: the planning report uses counts by skill and job family only. Named lists are limited to three mobility advisers. Employees can view and correct their own records in the HR portal.
4. AI inference: a tool suggests skills from project descriptions. The team labels these as "inferred", excludes them from the confirmed supply count and writes a note that the tool may not be used as the sole basis for any decision about a person. Elena flags that any change to use inference for selection would need a fresh legal review.
5. Monitoring: they agree a simple log of corrections and a yearly review of group differences in inferred results.

Practice activity: Draft a governance one-pager

For your chosen job family, write a one-page governance note.

1. State the purpose of the skills data and name an owner.
2. List three quality indicators and a target for each.
3. List the personal data you will use and how you will minimise it.
4. State your rule for inferred skills in one sentence and name two safeguards.
5. Say who you would consult about legal requirements.

Check your understanding: Governance

Assessment and badge

The assessed task: skills gap analysis for one job family

You produce a skills gap analysis for one job family. Use the Nordhafen case or, with permission, a case from your own organisation using synthetic or fully anonymised data. A person assesses your work using the rubric below. This file does not grade your submission.

Task brief

Write a report of up to six pages, plus tables, with these sections.

1. Context and scope: the job family, the planning horizon and the business drivers.
2. Supply and demand view: a table or chart of supply and demand for at least five skills, your evidence rule, assumptions and a supply range.
3. Gaps and routes: a ranked list of gaps with criticality, and a build, buy or borrow recommendation for at least three gaps with reasons and risks.
4. Job architecture link: one job profile with core skills, requirement types, accepted evidence including non-credential evidence, and one pathway from a neighbouring profile.
5. Data quality and privacy: a one-page governance note with purpose, owner, quality indicators, minimisation approach and your rule for AI-inferred skills.
6. Limits and next steps: the main weaknesses of your data and what you would do in the next quarter.

Do not claim that any credential proves job competency.

Rubric

Pass rule

• Score at least 2 on each of the four outcome criteria and at least 10 points overall out of 15.
• Pass the final knowledge check with at least 8 correct answers out of 10.
• Both conditions are needed to earn the badge.

Badge and evidence

On passing, you earn the badge "Workforce Planning with Skills and Credential Data (Ability, L4)", alignment code ICSF-TALN-L4-A. You submit your gap analysis as the badge evidence. The badge states what you did in a controlled setting. It does not state that you can perform any particular job.

Final knowledge check

Sources and further reading

Status as of November 2026; check before publishing.

The sources below are official texts, standards bodies or framework owners. Check current versions and links before publishing.

• ICoBC, Credentialing Skills Framework (ICSF) and the VRITE quality model. Published by the International Council on Badges and Credentials. See the ICoBC website, icobc.net, for the current versions.
• 1EdTech Consortium, Open Badges standard. Specification for verifiable digital badges. See the 1EdTech website, 1edtech.org.
• 1EdTech Consortium, Comprehensive Learner Record (CLR) standard. See the 1EdTech website, 1edtech.org.
• World Wide Web Consortium (W3C), Verifiable Credentials Data Model. See the W3C website, w3.org.
• European Commission, Europass and the European Skills, Competences, Qualifications and Occupations classification (ESCO). See the European Commission ESCO portal.
• European Parliament and Council of the European Union, Regulation (EU) 2016/679, the General Data Protection Regulation (GDPR). Official text in EUR-Lex.
• European Parliament and Council of the European Union, the Artificial Intelligence Act (Regulation (EU) 2024/1689), in particular its provisions on high-risk systems in employment. Official text in EUR-Lex.
• International Organization for Standardization, ISO 30414, Human resource management: Guidelines for internal and external human capital reporting. See the ISO website, iso.org.
• ICB-P16, Skills-Based Hiring and Internal Mobility with Credentials, ICoBC Kursbibliothek (prerequisite course).