In its 2018 Workplace Learning Report (you can get the report here) LinkedIn surveyed approximately 4,000 professionals to get a holistic view of modern workplace learning. One important result was the growing importance of skill management. Take a close look at the below summary from this report. Of the top seven themes, four can only be addressed through a modern skill management system.
Let’s look at each of these four themes. The first two are about skill gaps (current and future). The second two consider changes in skills over time and how to assess skills and competencies.
Identifying trends to prevent future skill gaps
Skill gaps come in two forms, current and future. Strategic leaders obsess about future skill gaps, operational leaders are more concerned with the current internal skill gaps (see the next point). It is not easy to identify and prevent future skill gaps. It depends on two things, both of which most companies struggle with:
What skills do we have now and how are we using them?
What skills will we need in the future?
Predicting the future is difficult. Think back to 2007 when Geoffrey Hinton, Yoshua Bengio and their collaborators started to make rapid advances with deep learning. How many of us forecast that the skills needed for machine learning would become mission critical to so many companies in just ten years? What trends are building now that we are not aware of? The fashion industry has experts in trend analysis and trains people on how to do this. How many HR or talent management experts are trained on this I wonder.
There are two ways to get insight into future skill gaps. One is to remember what science fiction writer William Gibson said.
“The future is here, it is just not evenly distributed.”
In other words, get out and look around. See what is happening in pockets of your organization that could spread. Look at how companies in other industries are changing. Spend time with small innovative companies and even get into University research labs. Innovation is often seen as the place for people in technology and product development. It is just as important for HR and talent leaders to spend a meaningful amount of their time investigating the future.
How does one investigate the future? Note the use of the word ‘investigate’ rather than ‘predict.’ Most of the trends that will impact HR and talent are not predictable in the conventional sense of the word. Well established trends based on demographics and basic technologies can be predicted, but these interact in complex ways to determine skill trends.
One approach you can take is to apply scenario planning to skills. Resilient and adaptive companies will be building skill portfolios that are robust across several different future scenarios. There is a rich body of practice around scenario planning that can be applied here, and people fortunate enough to work with companies that have their own future scenarios can leverage these into their own work.
A key word above is ‘portfolio.’ To build an adaptive and resilient organization think of your skills as a portfolio. You want to make some investments in high risk-high return skills (an investment in deep learning ten years ago is an example of a skill investment that would have been high risk then and would be delivering high returns today). You want to have other investments in skills that you are highly confident you will need (today mine operators are making investments in augmented reality).
Just as with product development, you will want to spread your investment across basic skills that everyone in your industry needs, differentiating skills that give you a competitive edge in target markets, and long-tail skills from which your future differentiation may emerge.
Deliver insight on internal skill gaps
Understanding what skills your organization will need in the future may sound intimidating, but we should all understand what skills we are using today and where the skill gaps are, right? Not really. In fact very few companies actually know the real skills being used in specific roles on specific projects.
Some companies have comprehensive competency models (and TeamFit is investing in tools to build competency models), but these are generally developed and implemented top down and are a step removed from active work. To really get insight into internal skill gaps you need to do three things.
- Know what skills are present
- Track how they are being used
- Understand which skills are driving performance
Top down approaches fail to address each of these. The vestigial skill lists in HRIS, talent management and learning management systems are more or less useless when it comes to getting any real insight into what skills are present and how they are being used.
To know what skills are present you need to activate conversations, let people suggest skills to each other, and have a way to look at the many different ways in which skill evidence surfaces. This includes everything from conversations on collaboration tools like Slack, contributions to Github or StackOverflow for software engineers, interactions on LinkedIn Groups for consultants, and so on. No one data source will ever provide insight into skills.
Skill use is best tracked by connecting skills to project management systems and work product (the reports, presentations and other deliverables that are part of day-to-day work). Connect a simple skill conversation to every project record and work product. This does not need to be deep or comprehensive. Noting the 3-5 skills used on 100 projects will tell you more than a list of 100 skills and competencies.
At the end of the day, what really matters is understanding which skills are contributing to performance. This is where new technologies like machine learning come in. If you have data on outcomes and data on skills, you can look for the patterns that connect the two. This is the future of skill and performance management. It is how we can help each other improve our own performance.
How to track skill development
Our skills change over time. We learn new things and let old skills decay.
How have your own skills changed over the past five years?
Most of us would struggle to answer this question as we don’t keep records and even if we do, we don’t make the time to stop and reflect. A system such as TeamFit can help track how skills at the individual, team and organizational level are changing over time, but just tracking this information is not enough. We need to carve time out of our schedules to stop, reflect and discuss where our skills are developing and where we want to go. This is a cultural thing, and will need a cultural change. It is summer, put aside a few hours to look in at yourself and to have conversations with your colleagues.
How to access skill competencies
Competency assessment is something we all struggle with. The root causes for this are revealing.
- Most of us have not been trained in self evaluation (see the above comments on self reflection)
- Tests are not good measures of performance in the field (the same is true of most certification programs)
- We do not connect skills and competencies with actual performance
At TeamFit we are working hard to open up our evidence model so that we can take many different kinds of things as evidence. This is a step in the right direction. There is no one size fits all model when it comes to competency assessment. But it is just a step. The real answer here is to collect as much information as possible, have rich and networked skill models, and to be able to connect skills to performance. As noted above, this problem will ultimately be solved using machine learning. Machine learning needs data. So the path to better assessing skill competencies begins with data.
The changes washing through the economy mean that developing, understanding and applying skills will become more important than ever. Skill development (and not talent or learning) is a strategic imperative and the organizations that develop expertise here will outcompete their rivals and be able to develop adaptive and resilient business models.