The Value of Measuring Employee Skill Data
Lachlan, a people analyst at a global company, sits down with the organisation’s Chief Strategy Officer (CSO). The CSO quickly gets to the point, stating: “Lachlan, I need your help finding a specialist to head up a new business. The opportunity is ripe—so we need to get started as soon as possible. By the end of the week, I need you to have identified a leader with the following skill set: experience in front- and back-end development, working knowledge of Amazon Web Services (AWS), knowledge of the South East Asian IT market, and fluent in Mandarin. We’ve got roughly 60,000 global employees, so surely there must be someone who fits the bill—right?”.
Lachlan pauses for a second before responding: “I honestly don’t know. I can certainly reach out to some contacts and look at the different datasets that we have internally, but I’m not 100% certain that I can find someone with the precise skill set that you’re looking for”.
The CSO frowns and purses her lips. “Okay, well how about externally? Can you find me someone from outside the company?”. Upon hearing this, Lachlan immediately looks relieved, replying: “Definitely—I can give you a detailed shortlist of specialists by close-of-business tomorrow. Would that be okay?”. The CSO smiles and nods, before adding: “How is it though that you can’t find me someone from our global internal population of 60,000, but you can provide me a list of people in 24 hours among a global population of approximately 8 billion people?”
Lachlan pauses, considering the problem at hand. After a few seconds, he responds simply: “Well, it’s all down to the quality of data at my disposal”.
This anecdote illustrates a common problem among companies of all sizes—most organisations are unaware of the skills residing within their employee population. The notion of employee skill data has garnered considerable interest of late, with interested parties ranging all the way from:
Tech-savvy vendors looking to apply rapidly advancing Artificial Intelligence (AI);
People analytics professionals wrestling with a variety of challenges;
CHROs looking to provide a personalised employee experience at scale;
Chief Learning Officers (CLO’s) looking to provide a tailored learning offerings to employees; and
Talent Acquisition heads looking to mobilise a greater number of internal transfers through a Talent Marketplace.
So what unites these disparate parties? Well, it’s ultimately the realisation that old skills management practices—specifically, an inability to catalogue the “gold” within the organisation—is untenable.
This article explores the two fundamental considerations when it comes to skills management. First, how do you acquire your employees’ relevant skill data? Second, what practical value can you then generate from this wealth of information?
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How to Acquire Employees’ Skill Data
The introductory anecdote highlights one key issue: an employee skills catalogue entirely depends upon quality data. So how about asking individual employees to complete and maintain their own employee skills profile (i.e. a company-specific profile similar to LinkedIn)?
For starters, this is unsustainable. Employees might forget to update their profile, feel like they’re too busy, or simply not see the benefits of this activity.
As a result, organisations must:
Minimise the amount of effort needed from each individual employee.
Fully exploit existing data as intelligently as possible.
Generate clear-cut benefits for both individuals and wider organisations alike.
And it appears that advancements in AI—specifically Natural Language Processing (NLP)—might hold the key. NLP has already been used widely within the HR industry, especially in the field of Talent Acquisition. This technology, when used correctly, connects and distils numerous data sources to make previously unknown connections.
For example, the following data sources—while often scattered across the HR / IT landscape and laying dormant—already contain a wealth of valuable information about individual employees.
Recruitment data: Resumes, cover letters, assessments, etc.
Career history data: Roles previously held by individuals within an organisation, role titles, job descriptions, project descriptions, leadership roles, awards won, etc.
Learning & Development data: Courses undertaken and completed, development objectives, mentoring, certifications, 360-degree feedback, psychometric assessments, etc.
Performance data: Performance objectives, performance appraisals, written feedback by peers and stakeholders, etc.
External data: Professional networking sites, professional publications, patents, blogs, etc.
All of these sources (well, all aside from external data) have long been standard data collection mechanisms within the HR industry. If finance is considered the home of numbers-based insights, then HR is undoubtedly the home of text-based insights. Organisations can therefore automate the engineering of skill data by combining:
The various forms of employee data currently held in disparate organisational HR/IT systems.
Next-generation NLP technologies that can perform realistic interpretations of text-based data.
The above image visually demonstrates this process. By analysing and applying relevant skill data, organisations can achieve evidence-based decision-making when it comes to personnel matters. Needless to say, this brings considerable value to both individual employees and the broader organisation.
The practical value you can generate with skill data across the employee lifecycle
It’s all well and good acquiring relevant skill data, but how can it be put to good use? If used correctly, skill data can guide a wide variety of employee touchpoints within an organisation. For instance, the very process of identifying the right person for a specific role (as per the introductory example) is a foundational application of employee skill data.
The following diagram gives an overview of the areas where skills data can be put to good use for the advantage of the organisation and the advantage of the employee.
The following table lists several other potential applications, both from the perspective of the employee and the organisation.
The above list, while not exhaustive, illustrates the various ways in which skill data can underpin many major employee touch points within an organisation. The greatest benefit of skill based personnel decision making is that it provides an objective, comprehensive, and close to real time perspective of the organisation - an outcome previously beyond the reach of organisations.
Why skills could be the new business currency
Skills management has been around since the eighties. However, it has historically been complicated, highly resource-intensive, and, even worse, yielded questionable tangible value to the organisation as a whole.
But that’s all about to change. Technological advancements mean that organisations can identify and manage their employees’ skill sets more easily than ever before. The above discussion detailing prospective applications of skill data demonstrates the breadth of utility, and potential for objective enhancement of personnel decision making across the employee lifecycle. Moreover, skill data provides a globally universal currency for organisational decision making, thereby enabling personalised employee decision making at scale that transcends geographic boundaries.
By maintaining a continuous overview of the capabilities that lie within their organisation, they’ll be able to meet the future head-on—no matter what challenges they face.
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ABOUT THE AUTHORS
Adam McKinnon is the People Analytics Insights Lead at QBE. Drawing upon a multi-disciplinary background in Psychology, IT, Epidemiology and Finance, Adam is an advocate of asking two questions in his work: So What? and Now What? He employs Machine Learning and Natural Language Processing to synthesise the scale of multinational companies, making it understandable and usable, so that organisations can embrace employee centricity in their decision making.
Mikaël Wornoo is a Founder & the Chief Product Officer at techwolf.ai. TechWolf is an AI back-end layer that uses internal company data and labour market data to make skill gaps measurable in organisations. Mikaël has a Computer Science Engineering background and is a member of the advisory board at HR.com.