Simplifying skills intelligence platforms - introducing the breadth vs depth concept for your Assessment and Interview Intelligence needs

Our previous blog post explored the definition of skills intelligence and how platform providers help organisations solve their skills challenges. 

However, one significant obstacle to this is the fragmented skills intelligence marketplace. 

The marketplace is crowded, and while some point solution providers claim they are comprehensive, no platform covers all skills, roles, and applications. 

As Josh Bersin has noted: “We have yet to find a company that uses 1 platform for everything. So while we may, at some point, find a single ‘Skills Cloud’ capable of storing every skill for every job in the company, that goal has yet to be achieved.”

With that in mind, let's summarise how skills intelligence platforms differ, especially when implementing Assessment and Interview Intelligence technologies.

How they organise skills demand data

Skills intelligence platforms can use 1 of 2 ways to organise demand-side skills data and create taxonomies.

1. Inference. Platforms using inference to understand skills demand scrape massive amounts of internal and external skills data from sources such as CVs and job descriptions. They then use AI and machine learning to determine the ontological relationships between skills and skills groups, creating foundations for Assessment tools.
2. Design. This approach uses input from company stakeholders (such as hiring managers and recruiting teams) to create a taxonomy that defines skills and maps them to roles and organisations. This method often underpins more sophisticated Interview Intelligence solutions by incorporating human expertise with Skills Science methodologies.

How they source skills supply data

Skills intelligence platforms can pull from 3 sources to create skills profiles for individuals. 

1. Self-reporting. Self-reporting can be as basic as looking at the skills an individual has tagged themselves with on their LinkedIn profile or creating a spreadsheet and having a manager verify it.
2. Inference. Similar to how inference is used to create role skill taxonomies, this approach scrapes secondary data from various sources (e.g. CVs and performance reviews) and infers employee skills.
3. Verification. Due to their low cost, self-reporting and inference can cover a wide range of skills. However, they lack accuracy. If you want a verified skills profile that includes proficiency for robust Assessment and Interview Intelligence applications, you'll need to use a mix of data sources, including manager/peer feedback, interviews, credentials, and assessments powered by Skills Science.

How to understand skills intelligence platforms

So, there are many options for buyers to consider, and it can quickly become confusing. 

However, we can simplify our understanding of the skills intelligence landscape by categorising the underlying technology into 2 camps: breadth platforms and depth platforms. 

Breadth platforms 

Breadth platforms identify skills. On the skills demand side, they aggregate labour market data to label and define skills, create skills taxonomies, and identify skills adjacencies. On the skills supply side, they can build individuals’ skills profiles by scraping data from job descriptions, CVs, and technology platforms such as Human Capital Management systems. 

Key things to note: 

• They use AI to make inferences about skills. 
• They are more focused on speed than on accuracy. 
• They do not cover skills proficiency.
• Limited Interview Intelligence capabilities.

Some examples of breadth players include Lightcast, a provider of integrated jobs, skills, roles, and occupational data, and Eightfold.ai, a talent marketplace and talent management platform.

Depth platforms

Depth platforms verify skills through comprehensive Assessment methodologies. They allow people to generate granular and accurate data about skills in an efficient way, augmented by AI and Skills Science. For instance, depth platforms may use a combination of online assessments, manager/peer feedback, self-assessment, and observation to accurately and efficiently verify technical and behavioural skills. On the skills demand side, these platforms create role skills profiles detailing the technical and behavioural skills required for success and at what proficiency level.

Key things to note:

• They use a combination of humans and AI technology to verify skills
• They ensure greater accuracy through Skills Science methodologies
• They cover technical and behavioural skills, as well as their proficiency
• Advanced Interview Intelligence capabilities with comprehensive Assessment tools

An example of a depth player is Spotted Zebra, which provides high-confidence, granular skills data through Interview Intelligence and Assessment solutions to support hiring, reskilling, and development decision-making.

What can we do with this knowledge? ‍

Differentiating between depth platforms and breadth platforms helps us simplify the complex skills intelligence marketplace. Crucially, because depth and breadth platforms each have specific strengths, this distinction also helps us identify which platform is best suited to address our HR priorities—whether you need basic skills mapping or comprehensive Interview Intelligence and Assessment capabilities.

To learn more about this, download our guide. It will help you understand which HR decisions are best served by depth platforms and which by breadth platforms, particularly when implementing Assessment and Interview Intelligence solutions.

FAQs

1. How do skills intelligence platforms address the challenge of the fragmented marketplace, and what are the key differences between breadth and depth platforms in terms of their approach to organising skills data?

Skills intelligence platforms address the challenge of the fragmented marketplace by offering different approaches to organising skills data for Assessment and Interview Intelligence applications. Breadth platforms typically use inference, scraping massive amounts of data from various sources like CVs and job descriptions, and then employing AI and machine learning to understand ontological relationships between skills. Depth platforms, on the other hand, focus on verification through Skills Science methodologies, using a combination of methods such as online assessments, manager/peer feedback, and observation to ensure the accuracy and granularity of skills data for Interview Intelligence solutions.

2. What are the advantages and disadvantages of using inference versus design in organising demand-side skills data, and how does each approach impact the accuracy and comprehensiveness of skills intelligence platforms?

The advantages of using inference in organising demand-side skills data include the ability to quickly aggregate labour market data and create skills taxonomies using AI. However, this approach may lack accuracy compared to design, where input from company stakeholders is used to define skills and map them to roles and organisations through Skills Science principles. While inference can cover a wide range of skills for basic Assessment needs, it may lack precision. Design, though potentially more accurate and better suited for sophisticated Interview Intelligence applications, requires more human input.

3. In what ways do breadth and depth platforms differ in their sourcing of skills supply data, and how does this difference influence the accuracy and reliability of skills profiles generated by these platforms?

Breadth platforms typically source skills supply data through self-reporting and inference using AI. Self-reporting involves individuals tagging their skills on platforms like LinkedIn. Inference scrapes secondary data from sources like CVs and performance reviews. While these methods are cost-effective and can cover a wide range of skills, they may lack accuracy for comprehensive Assessment applications. Depth platforms, however, prioritise accuracy by combining human input with AI technology to verify skills through Skills Science methodologies like online assessments, manager/peer feedback, and observation. This ensures greater precision in skill profiles for Interview Intelligence solutions, particularly regarding proficiency levels.

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