Skills benchmarking

How ML-powered skills insights are guiding talent strategy and development

by Vinod Bakthavachalam and Emily Glassberg Sands

… and check out the MIT Technology Review article on this release! …

With technology increasingly transforming the way we work, companies need deep talent intelligence to stay competitive. This includes understanding how employees’ skill profiles stack up to market, where skills gaps are, and how to close them.

Since launching Coursera for Business two years ago and partnering with more than 1,400 companies, we’ve seen strong demand for talent measurement insights to power our customers’ learning, staffing, and hiring strategies. Today, we’re thrilled to launch Skills Benchmarking, a powerful new Coursera for Business tool that arms companies with the actionable data they need to develop a strategic workforce transformation agenda.

Skills Benchmarking dashboards are built atop our Skills Graph, which maps a robust hierarchy of skills to the content on the platform, as well as to the careers, companies, and industries that require them. Machine learning models trained on anonymized learning and assessment performance data from Coursera’s vast learner and content base provide a birds-eye view of your company’s talent benchmarks against a defined peer group. Companies can customize this peer group based on any combination of industry, geographic region, and company size. With this information, Coursera can help tailor a curriculum to exact learning needs, cultivating strengths and shoring up weaknesses.

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Skills Benchmarking also makes it easy to identify top individuals by broad competency areas like Data Science or by more granular skills like Statistics and Machine Learning, along with the individual’s percentile in each skill relative to all Coursera learners.

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Companies of all sizes and across industries are taking advantage of this new level of talent insight. Early customers, including Adobe and UNDP, are already using Skills Benchmarking to inform their L&D efforts and have found it particularly helpful for understanding their competitive talent positioning, and for identifying employees with specific mission-critical skills.

Consider the sample company shown below. Zooming in on Data Science reveals specific strengths and areas for growth: while strong in the classical competencies like Statistics and Data Visualization, the company is weaker than its peers in Machine Learning. Since Machine Learning is a crucial application of Data Science today, this company changed its L&D strategy and is now doubling down on strengthening its employees skills in that area.

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As a more general view of the data, consider learners working in the tech industry and acquiring software engineering skills on the platform. We can compare their skill level by occupation, level of seniority, and level of formal education. The x-axis measures average estimated skill in software engineering based on our skills benchmarking technology, with estimated skill increasing from left to right. The results are illustrative and align well with intuition:

  1. Consider software engineering skills across the 10 largest occupations among tech workers on Coursera. Software engineers and product managers (many of whom were formerly engineers) rank highest, followed by consultants and researchers.
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We’re excited about the potential of Coursera’s unique learner and skill data to help companies better understand, train, and deploy their talent. Want to explore how our technology can inform your learning strategy and support your talent management? Visit Coursera for Business today.

Interested in applying data science to education? Coursera is hiring!

Coursera Engineering

We're changing the way the world learns!

Vinod Bakthavachalam

Written by

I am interested in politics, economics, & policy. I work as a data scientist and am passionate about using technology to solve structural economic problems.

Coursera Engineering

We're changing the way the world learns! Posts from @Coursera engineers and data scientists.

Vinod Bakthavachalam

Written by

I am interested in politics, economics, & policy. I work as a data scientist and am passionate about using technology to solve structural economic problems.

Coursera Engineering

We're changing the way the world learns! Posts from @Coursera engineers and data scientists.

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