How to Maximize the Value of Competency Analytics

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How to Maximize the Value of Competency Analytics

Who wouldn’t want all the competencies in the world? We keep hearing that companies have a desperate need for talent. Some competencies are so important it is worth seeking them out and treasuring them. A top computer programmer works 10x faster and better than a second rate programmer; a great primary school teacher can transform a child’s experience of learning.

Defining the personality type for salesTo move from the pain of needing better skills to the pleasure of getting value from competency analytics, four steps are vital: (1) knowing what competencies your organization needs, (2) knowing what competencies your organization has, (3) placing people in the right roles and (4) building their ability to express their competencies.

What challenges will you face for competency analytics to be both practical and worthwhile? This article describes 4 challenges, a counter argument and the steps needed at each stage.

Challenge 1: what competencies do you need?

Can competencies be defined at all? Yes. A competency is ‘the ability to do something successfully or efficiently’. Words sometimes used interchangeably include skills, capabilities, strengths, personality traits, abilities, behaviours. Whatever the name is, having a specified list of what is needed is useful when we seek to recruit someone, promote someone or give career guidance. Sources such as ONet and ESCO classify as many as 15,000 skills typically needed across up to 3000 occupations.

For practical use, you can separate generic and specific competencies. Generic skills might be, for example, customer understanding, decision making, team-building. Specific skills might include customer service, underwriting, programming skills. Roles and skills evolve. A marketing expert 30 years ago might need copy editing skills to make messages effective; today, it is web algorithm design skills. In 10 years, it may be different again, but defining the competencies when you are recruiting is still the right starting point.

If you are building a competency taxonomy for your own organization, it makes sense to start from a generic collection of skills typical of your industry and your functions, and to adapt these to your particular needs. The level of detail needed is for the organization to choose; enough to define the new role; not so many as to weigh down the data capture process.

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In a relatively unchanging business, each department might not need to specify its competencies. HR could simply continue to recruit the same types of people employed before. But where the organization is changing, or if it has some functions that are changing rapidly, or where new employees do not always stay or succeed, there is a strong argument for using a competencies taxonomy to re-define what is sought. Competency taxonomies can be useful.

Challenge 2: Data Collection

Once the organization has defined what competencies are important, gathering data becomes the challenge. The first rule of data collection in 2019 is: make it ethical. Declare what you are doing and ensure active consent. You must make it acceptable and ideally beneficial to employees such that they will consent.

The second rule of data collection is: if you can’t make it fun, at least make it effortless. So the second challenge for competency analytics is this: if not fun, at least how do you make it no effort for employees who are busy with many other things?

Ease is everything. Compare timesheets with social media platforms. Everyone hates entering data on timesheets. By contrast, social media platforms make it unbelievably easy to collect data. People happily share where they are, who they are with, what they are doing and how they feel about it. People get value in submitting the information (as notes and pictures), and reflecting on them afterwards. What the platform does with their data is a whole other question[1], but as a generic data collection device, it has few peers.

So what options are there for collecting competency data without effort? Successful competency gathering techniques include: scraping data from LinkedIn (Cisco HR reported doing so with an opt-in from employees in 2015), leveraging data from Learning Management Systems (such as Fuse Universal), leveraging data from peer-to-peer communications platforms (such as Yammer, Microsoft Teams or even from email traffic), leveraging data from feedback systems (Dot Collector, 360-degree feedback, employee self-assessment, or patterns of internal feedback).

In all of these, competency data is captured as a by-product of the wider use-case. It is therefore potentially more useful as it is less biased; but at the same time needs to be carefully matched to the competency taxonomy to ensure that measurements are genuinely related.

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The images below are from Ray Dalio’s talk on radical transparency using Dot Collector, to get feedback on individual interactions. Data is captured in real time during meetings and lets everyone get feedback on how their input is received by others. Critically, the data collection is intuitive and natural – even fun. In the next section we see how the data can be applied to understand where people’s strengths are, and how they best can be joined toget

Challenge 3: getting the right people in the right place

So you have defined the competencies, measured people’s level of competencies. Now you need to select new recruits or existing employees for new roles. How do you get the best balance of skills in the right place, at the right time?

The first principle is to define the roles first, before thinking about the people. The roles are based on the work needed, and the work needed is based on the products and services that customers will buy. Once you understand the work needed, you can gather it together into roles and define the competencies needed per role. This should be reasonably quick and should be done by an expert in the function, with advice from an HRBP to gather the data.

The competencies needed for each role must be updated regularly, as new skills emerge. In Olympic sports, for example, athletes who used to be recruited and trained for their physical capabilities are increasingly now being trained in mental skills.

While in 2016 the UK Olympics team had 18 sports psychologists working on mental skills, it will nearly double this support to 30 sports psychologists at the 2020 Olympics.[2]

Recruiters looking for future star athletes have started to add ‘being open to learning,’ ‘hopeful and optimistic,’ ‘resilience’ and so on to their list of important characteristics.

Now who is right for each role? Sadly for HR professionals, people’s skills cannot be unbundled: you cannot recruit just their skills in one area. It is always a balance. In extreme examples – such as the race between U.S. presidential candidates we are used to candidates having a combination of qualities and imperfections. When Barack Obama stood in 2008, he explicitly spoke of his candidacy being ‘imperfect’.[3]

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Happily, when recruiting, we usually look at more than two candidates, but the example is still useful to remind us that every choice is a balance. The best fit of candidate to an individual role is always a compromise, based on the importance of different characteristics and how strong each candidate is in each one.[4]

It can also be the case that the ‘right’ combination of personalities in a team depends on who else will be chosen: a balance of energy and experience is a typical consideration for sports teams and political campaigns. The same person may also fulfil different roles at different stages of their career, or in different situations. Pret a Manger, a sandwich chain, addresses this simply by allowing the team to choose after 1 week whether a new joiner fits well and should remain with the team.

A more sophisticated method can be used by algorithms, as Bridgewater Associates claims it combines employees with different balances of measured skills – for example, “matching someone who is creative but unreliable with someone who is reliable but not creative” to form effective teams (Ray Dalio, 2017).

For an HR professional, it is critical to know from the business what level of competency is needed for each role and what weight they place on the different competencies that they identify for each role. For large scale organizational development, a major shift in direction or a large-scale change process, it is critical to gather that data at scale and pace, and then to verify the results afterwards.

One approach to doing this is to crowd-source the data from HRBPs working with managers, where a consistent description of the skill set is built and can be applied consistently in job descriptions. Screenshots below from OrgVue:

Setting competency targets

The counter argument

Just to check our thinking, we have to confront the contrary view that competencies can be damaging. Marcus Buckingham and Ashley Goodall have argued that giving feedback based on competencies is (1) unreliable, (2) damaging to performance (3) inappropriate because people are unique. “If you aspire to lead, your firm might use a 360-degree feedback tool to measure you against its predefined leadership competencies… but … in extrapolating from our own performance to what might create performance in others, we overreach.”

What is more, some companies might avoid the problems of complexity by relying on team judgement. Pret a Manger, as we described above, simplifies recruitment by recruiting for general customer service skills and then relying on the local team to decide after a week of side-by-side experience how individual personalities and skills will adapt and balance with each other.

Similarly, one global minerals company moved away from measuring detailed competencies in the early 2000s. It found detailed competency frameworks too complex, leading to conflictual discussions. A manager might end up in a debate as to whether a team member had shown, for example, ‘Level 3: mastery of tunnelling techniques’ or only ‘Level 2: capability in tunnelling techniques.’ Now the company collects scored data only on four fundamental leadership competencies: performance, collaboration, authenticity, growth and focuses performance discussions on the content of the job with ‘what went well’ and ‘even better if.’

It is worth emphasising here that all these counter-examples focus on the use of competencies in feedback discussions. It is still possible that an organization could recruit based on competencies and personality, while it could give feedback using a strength-based approach – based on positive examples of observed performance. These are two different HR activities and many authors – for example, Morrison 2015 – emphasise the importance of separating out competency analytics for recruitment from competency discussions for performance feedback.

Challenge 4: expressing the organization’s competencies in great work

So how do you apply competencies in today’s organizational life? As an interim step, the practical approach shown by Pret a Manger, and large companies that have simplified performance feedback probably make sense. A few metrics (5-10) may be enough. The perfect competency management platform doesn’t exist yet, so as Morrison, 2015 (p. 206), said: “…the business case isn’t compelling enough… to go through the effort of putting a competency framework in place.”

However, as Bridgewater, Cisco and others are showing, the effort/benefit trade-off is changing. The technology is starting to become available. Ray Dalio, CEO of Bridgewater argued in 2017:

“Whether you like it or not, radical transparency is coming at you fast and it’s going to change your life… you can gather all that data that you’re leaving about yourself, …and know what you’re like and then direct the computers to interact with you in ways that are better than most people can.

That might sound scary but I have been doing it for a long time and I have found it to be wonderful. My objective has been to have meaningful work and meaningful relationships with the people I work with and I have learned that I couldn’t have that unless I had that radical transparency and algorithmic decision making” (Ray Dalio, 2017).

“How well does radical transparency work? The data collection process needs to be intuitive, positive, aligned to the company’s culture and fun. Bridgewater is still working this issue – it acknowledges it has a high Year 1 turnover rate, and it has some challenging reviews on Glassdoor.”

Conclusion: a look to the future

Whether in the long-term the data is gathered as a side product of another activity (e.g. feedback in meetings, learning management systems, peer-to-peer communications) or via low-effort capturing (e.g. personality-profiling games[5]) the future shape of competency management is starting to emerge. The ideal application will have some of the following characteristics:

  • Easy competency taxonomies from existing libraries or companies’ own perspectives
  • Low-effort or no-effort competency data collection
  • Ability to clarify current role requirements for current employees and future recruits (e.g. with job descriptions)
  • Ability to help the HR team define future roles and model future teams
  • Ability to calculate competency gaps
  • Support for the career guidance conversation between managers and team members
  • Help for HR to select the right people in large-scale change situations
  • Feedback from individual success in role to redefine the required competencies
  • Ways of considering the combination of competencies at both an individual and team level
  • Feedback from team success to redefine the combination of competencies

Until the technology reaches an ease-of-use tipping point, the focus on competencies must remain for the moment light-touch and optional. Until it proves itself, people can use a limited form of competencies management (as in the example of the minerals company with 4 key leadership competencies). When the organization gets value from the ‘light’ version it can gradually build the case to extend competency management into new areas.

When the technology does reach its ease-of-use tipping point, when it starts to include feedback from performance into re-definitions of competencies, we expect to see a radical change in the value that HR can add and the skills that HR will need. It won’t be an immediate change,[6] but it will be a profound one that will roll gradually across industries and become an important new capability in HR.

For a copy of this article, the Elkjop-Cut-e 2014 case study or Concentra’s original 2013 study of competency analytics, please contact the author: [email protected]

[1] Michal Kosinski, David Stillwell, and Thore Graepel proved in 2013, for example, that personalities could be mapped using Facebook likes, highlighting the risk of micro-targeted commercial and political campaigning. See the 2013 article, an update on later work of Kosinski in The Guardian, and a rather lame joke: what is the difference between timesheets and Facebook? Timesheets is how nerds sell you their time; Facebook is how nerds sell your democracy.

[2] Annie Vernon, 2019, ‘Mind Games’ Bloomsbury, p. 9 footnote. The US Olympics team first brought a sports psychologist with it in 1988, according to Kremer & Moran, in the British Pyschological Society, vol 21, 8, 2008

[3] Barack Obama, 18 March 2008:  “I have never been so naive as to believe that we can get beyond our racial divisions in a single election cycle, or with a single candidacy — particularly a candidacy as imperfect as my own.”

[4] Kanye West described his bundle of skills as: “I am flawed as a human. I am flawed as a person. As a man I am flawed…but my music is perfect.” Rhetoric 10/10; humility questionable

[5] See, e.g. Arctic Shores ‘Sky Rise City,’ giving 37 metrics on the candidate from 30-40 minutes of playing time

[6] Michael Lewis, Moneyball, 2003. In baseball, it took from 1995 until 2003 (and a Hollywood film in 2011) for general manager Billy Beane’s statistical player-evaluation methods to be widely recognised as a game changer. Now most professional sports teams use similar data gathering and evaluation methods.

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