The HR function and its performance are increasingly dependent on information systems. However, the number of talent management applications in use remains low. According to the andrh 2021 study, it is around 25 to 30% of companies with 300 to 5000 employees, and 62% of companies with more than 10000 employees.
Promoting the adoption of these tools requires making the data more reliable, harmonizing the tools in place, and thus making decisions more objective.
Let’s start this vast subject with the core of Neobrain’s business: the creation and management of skills repositories.
How to make a skills framework reliable?
Good Practice # 1: Choose the appropriate skills collection methodology
To build a reliable system, you must first ensure that you collect and structure the input data in a rigorous manner. Three approaches to data collection are possible, depending on your company's culture:
A. Top-down: skills are defined by top management.
“What skills do we need? “
- The advantage of this approach is that it allows us to integrate a prospective approach. Business experts and managers can instill a long-term vision of the skills that the company will need tomorrow.
- The main limitation is to have a data model that does not reflect the skills used on a daily basis by the main actors: the employees and their managers. In the very short term, the risk is poor appropriation of the tool and the addition of skills outside the repository.
B. Bottom-up: skills are collected by the employees themselves.
“What are the skills used today?“
- The advantage is to first obtain an exhaustive list of skills. Subsequently, these will be reviewed by the project team to select the most important ones, as well as cross-cutting skills.
- The Limitations are to create a necessary rationalization step before finalizing the repository. Frustration may also arise if certain previously mentioned skills are not included.
C. Hybrid: combination of skills from HR, management, and employee skill integration.
"What's the reality on the ground?"
- The advantage lies in the involvement of all stakeholders for increased relevance: future skills will be considered, as will those currently in use. You will also limit the declaration of skills that fall outside the reference framework when employees declare their skills.
- The limitation of this collection method is that it generates a longer process.
Good practice #2: Standardize the formulation of skills
Before we get into describing the universal formulation that enhances the consistency and reliability of your repository, let’s define what a skill is.
What is a skill?
A skill is defined by the ability to act in a given situation by drawing upon relevant knowledge, know-how, and interpersonal skills.
The notion of skill only makes sense when articulated with a specific context. Furthermore, it is possible to add a behavioral description for the skill within the Neobrain mapping solution.
The Taxonomy and Ontology of skills
To make your approach as relevant as possible, we recommend adopting the concepts of taxonomy and ontology.
- Taxonomy is the process of creating a hierarchical 'parent-child' relationship to organize jobs and skills into families. The classification above illustrates how your data is structured: the role of “Credit Risk Data Analyst” is part of the “Data Analysis” sub-family, which is itself part of the “Data” family.
- Ontology , on the other hand, calls for the representation and linking of jobs, skills, and further training through a common skill language. In this way, you can link HR data from initially distinct domains. The ontology will allow you to link an employee profile to an internal vacancy, or to attach a training course to a specific skill. This modeling is essential to make your mapping as consistent and reliable as possible.
Reliability through good data processing practices
Best Practice # 3: Enhance the diversity of skill data sources
Employee self-assessment of their skill levels encourages their commitment. This approach has several limitations: an employee overestimates or underestimates their skills depending on their environment and their own perception of reality. Cognitive biases of over- or under-estimation in evaluations must therefore be complemented by several data sources:
- Direct manager's evaluation. This evaluation is itself potentially subject to discussion, but it encourages dialogue between managers and employees. This evaluation method is relevant for hard and soft skills.
- Conducting tests. This modality will therefore have to modulate the evaluation of the skill concerned. Essentially for technical skills.
- Serious games, with applications for hard and soft skills
- 360-degree feedback for soft skills
- Recommendations from other colleagues should be considered with great care.
- The results of the annual professional interviews on both dimensions
- Data enrichment through training, certification: hard skills
- The career committees and talent reviews are also an opportunity to reaffirm the evaluations previously obtained for the two types of skills.
Best Practice # 4: Use artificial intelligence to improve skills’ reliability
Artificial intelligence offers a complementary ally to make your skills repository more reliable, then its mapping:
- Detect the skills of employees from their resumes, LinkedIn profiles. New skills will emerge, the decision to integrate them or not will be in the hands of data governance.
- Generate skills adapted to your sector of activity. Our system aggregates data from several sources such as business observatories.
- To carry out the reconciliation between jobs with transferable skills, this step will be likely to facilitate mobility.
- Continuous enrichment. Once your tool is deployed, you can choose to have Neobrain continue its search for new skills in your domain. In this case, suggestions will be sent to your data governance to add or not to your repository.
Focusing on making your data reliable is an exercise that requires listening to the information collected from employees after defining and structuring your information system. Capturing external data is also likely to enrich your analyses and perspectives.
making it reliable through good organizational practices
Good Practice # 5: Form a governance team for skills management
One of the main pitfalls encountered by companies is to implement this approach without having the means to manage it. The “governance of skills” is a practice that ensures the quality of data from its registration to its destruction. It relies on men and women who share the desire to design a solution in which information is decompartmentalized, where each piece of data is synchronized with the various modules (annual interviews, career committees, training, etc.).
The objective of creating a skills governance system is to improve the relevance of your business repositories and ensure they evolve over time.
What are the 3 roles of the skills governance team?
- The Competency Owner.
He is the guarantor of the semantic integrity of skills. That is, he/she ensures good practices in the formulation of these skills and their meaning for the population. He/she conducts interviews and reviews with the business experts and the local HR or HRBP.
- “The business expert”
It describes the main tasks associated with the jobs it oversees, to formulate activities (sets of tasks) and the resulting skills.
- The “business sponsor
He/she validates the strategic skills contained within the family of professions that concerns him/her. He/she participates in the prospective vision of the organization’s skills.
The quality of collaboration between these three roles strongly influences the dynamism of your skills repository. We recommend that you establish a frequency for updating skills and enriching it with external information sources validated by your governance team.
Want to better govern your skills data? A full article covers the topic.
Check your understanding of the process by taking the quiz.
