How To Manage The Skills Gap
In the late 90’s McKinsey declared the war for talent and argued that, in a knowledge economy, having the right people is even more important than having the right strategy or technology. Recruiting and retaining the “best and the brightest” became a corporate mantra.
Yet today, the firm is more concerned with the skills gap. In data science, for example it estimates a shortfall of 140,000 to 190,000 data scientists and 1.5 million managers who have the skills needed to use the insights to drive decisions. But even that understates the problem.
With technology accelerating change in the marketplace and automation replacing highly skilled workers with robots, the decision to invest in any particular set of skills is far from obvious. Empty platitudes about “upgrading skills” and “investing in our people” will not suffice. We need to start thinking seriously about viable strategies to manage the skills gap.
The Evolving Challenge
There is nowhere that success breeds failure as quickly or as unexpectedly as the tech industry. One time stalwarts like DEC, Borland and Lucent are long gone and mostly forgotten, supplanted by firms that, in many cases, have themselves fallen on hard times or gone out of business altogether.
Through it all, one company that has been able to stay at the forefront of technology is IBM. One of the strategies it employs in order to stay on top is its academic initiative, which partners with universities in order to ensure that corporations develop and maintain the skills needed to get the most out of its products and services.
When I spoke with Jim Spohrer, who runs the university program at IBM, he stressed that its curriculum has to be continually transformed. For example, a firm that invested in the “best and brightest” business analysts five years ago would still be unprepared for the big data environment of today. Other areas, like cognitive computing are completely new.
Spohrer sees the marketplace in a state of constant flux between the skills needed for transformation and the supply of people who have them, so IBM works hard to help its partners and customers stay ahead of the curve. Yet that’s become far more involved than simply hiring the “right people.” You need a coherent strategy.
A New Era of Talent
We desperately need to change the way we think about talent. In 1997, when McKinsey published its report, the lines dividing companies and industries were fairly clear. Competition really was like a war and the resources you brought to battle largely determined whether you would succeed or fail. That’s changed substantially.
In today’s semantic economy, it’s not so important what assets you own, but what you can access. The basis of competition is no longer between firms, but ecosystems. Open architectures and cloud technology have made it easier to tap outside resources—such as partners and highly skilled contract workers—to get things done.
Consequently, a new era of talent has arisen in which the fundamental decision managers have to make is not who to hire, but which skills to develop in-house and which can be accessed through an external ecosystem. Many firms, for example, focus on research and design and contract out manufacturing and logistics, while others see those as core skills.
There are, of course, never cut and dried answers to these types of questions. The future is always uncertain. But we do need a clear framework for thinking about them.
Focusing Of Platforms
Taso Du Val is someone who deals with the skills gap everyday. As Founder and CEO of Top Tal, his clients depend on him to deliver world class developers with the most sought after skill sets. He also advises them on what capabilities they need in-house to work with advanced programming environments.
Du Val sees a crucial distinction between platforms and individual technologies. While he can deliver top talent in any given technology from around the world, his clients must have adequate platform expertise in order to be able to make use of it. Otherwise, the enterprise will not be able to integrate the new capabilities effectively.
So he sees the problem not so much as figuring out which skills or technologies to bet on, but in recognizing the emerging platforms that make use of them. He gives three criteria for understanding whether a particular platform is viable and relevant.
1. Opportunities To Innovate: As new platforms emerge, they can completely change our conceptions of what’s possible. If a new capability has the potential to transform your business, then it’s not something you want to outsource. You need at least a core skill set in-house.
Du Val points to examples like Hadoop, which makes big data possible and IBM’s Watson API, which allows firms to develop cognitive computing and personalization services. Outside of tech, many firms are also realizing that they need to build internal capabilities for content and open innovation. New platforms require new skills.
2. Internal Efficiency: Often new platforms arise that vastly increase operational efficiency. In web and internal development, Du Val points to platforms such as Python and Ruby as vast improvements over PHP, an earlier technology, because of how much easier they make it to build products.
Platforms that increase internal efficiency often have significant switching costs, so the decision to adopt a new platform often means significant up-front investment. However, when a core strategic capability is at stake, this investment can be crucial. For example, Wal-Mart’s continual investment in its logistics platform has been key to its success.
3. Difficulty of Transition: Another thing that managers need to take into account is the difficulty of transition. While cognitive computing is a true cutting edge technology, IBM’s investment in resources has made it relatively easy to train people for. Hadoop requires some training, but the barriers aren’t that high and big data is becoming an imperative.
Du Val’s firm helps ease the transition by not only providing skilled developers in emerging platforms like Hadoop, but also offers training of in-house staff. In some cases, he has seen his clients achieve as much as a 100x efficiency improvement, so the benefits can far outweigh the costs.
However, Du Val also points out that sometimes the switch isn’t worth the effort. Facebook, for example, still works in the old PHP environment, because it has determined that the increase in internal efficiency wouldn’t be worth the switching costs. It has found ways to make the old technology work with little or no drop in performance.
Developing Dynamic Capabilities
It’s become clear that platitudes like “war for talent,” and “recruiting the best and brightest” no longer suffice. Today, every corporation must work not only to strengthen capabilities, but to continually evolve them. This has become clear in not only high-tech categories, but also in old line industries such as publishing and retail. Nobody is safe anymore.
IBM’s Spohrer argues that the problem isn’t skills per se, but developing dynamic capabilities that can sense and then seize opportunities by transforming the enterprise in the context of a strategic direction. In an earlier age, that could be done by focusing on the product and the customer, but now we also need to take emerging platforms into account.
Perhaps, most importantly, developing dynamic capabilities requires a culture of change. The New York Times, for example, has long been aware of the strategic need to develop its digital business and has made significant investments in that direction. However, it has struggled with the cultural shifts needed to make that aspiration a reality.
As Columbia’s Rita Gunther McGrath has put it, we can no longer focus our efforts on learning to plan, we now must plan to learn and that’s especially true with regard to talent. It’s no longer a war against distinct adversaries, but a continuous struggle to stay relevant. In an age of disruption, the only viable strategy is to adapt.