How companies can win in the seven tech-talent battlegrounds

November 3, 2020

 

Here is an excerpt from an article written by Matthias Daub, Ranja Reda Kouba, Kate Smaje, and Anna Wiesinger for the McKinsey Quarterly, published by McKinsey & Company. To read the complete article, check out others, learn more about the firm, and sign up for email alerts, please click here.

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Companies have to hire the best, but that won’t be enough. They’ll also need to rethink how they reskill and upskill their people.
With the acceleration in digital, the demands on technology—for speed, flexibility, reliability, security, and value—have radically increased. For CIOs surveying how to transform their organizations, one tricky question is emerging: Where do I find the people to do all the work?
Few executives would debate the importance of talent or the difficulty that many have in attracting and keeping top people. But companies nevertheless aren’t treating tech talent with the urgency it demands. Respondents to a recent McKinsey survey report more significant impact from talent transformations than from any other technology-based play. Yet talent transformations are relatively rare. Only 27 percent say their companies have pursued one in the past two years, and just 15 percent believe they will do so in the next two years.  Amidst this reality, the increasing complexity of IT systems and the emergence of a broad range of new technologies, from cloud to artificial intelligence (AI) to machine learning, have increased the challenges. One European CEO and football fan explained it this way: if you gave him a big enough budget, he’d be confident he could put together a winning team. But a cricket team? He wouldn’t know where to start, since he doesn’t know anything about the game. He used the analogy to point out how hard it can be for leaders to know what talent they actually need.A few companies, however, have started to crack the code. Companies winning in this arena have identified at a granular level the tech skills they need to build value for the business, have developed a clear view of their present and future talent needs, and are intentional about finding both top talent and adaptable learners. Crucially, these leaders understand that it’s impossible to hire everyone you need; training and reskilling the existing workforce has to be a core part of the strategy to win the talent battle. Some 82 percent of global executives expect that reskilling and upskilling will be at least half of the solution to their persistent skill gaps.

Seven emerging tech-talent battlegrounds

To better understand what tech talent will matter most in the next three to five years, we spoke with hundreds of global CIOs, analyzed talent developments over two years across three global markets, and reviewed more than 30 cross-cutting tech trends. We then mapped relevant skills and roles to the most significant emerging tech trends and business needs. For example, given the increasing importance of using data to make better and faster decisions, the ability to rapidly build infrastructure and architecture for data (data-engineer skills) is likely to become more of a bottleneck than the ability to generate insights (data-scientist skills).

Through this analysis, we identified about 4,000 tech skills, which we broke down into seven battlegrounds, or clusters of need. (Note: while cultural and change-management aspects, including social and emotional skills, are also important, our research honed in on tech skills only).

Battleground Rationale Tech skills (sample set)
DevOps Faster and continuous delivery of features, more stable environments, and reduced operations time. (Read more.)
  • Agile product-life-cycle management
  • DevSecOps
  • Continuous integration and delivery (CI/CD)
  • Microservices architecture
Customer experience Significant shifts in customer behavior as a result of COVID-19 and rising customer expectations; need to deliver top experiences across a wide array of channels; prioritization of personalized over generic design (while maintaining privacy); continuous test-and-learn cycles. (Read more.)
  • Predictive/nudge analytics
  • Design thinking
  • Test-and-learn at scale
  • Automated testing
  • Prototyping
Cloud Infrastructure increasingly provided through next-gen cloud architecture, the time to market of services is vastly improved, and solutions are more easily scalable; acceleration of transformation and increased source of competitive value. (Read more.)
  • Kubernetes
  • Docker
  • Multicloud and hybrid-cloud architecture
  • Security
  • Smart distribution/metering
  • Edge computing
Automation Significant number of tasks automatable: about 22 percent of workforce activities across the European Union could be automated by 2030, for example, through end-to-end automation across development, testing, and deployment processes—accelerating development and reducing errors. (Read more.)
  • Cognitive AI
  • Robotic-process-automation (RPA) technologies
  • Automation anywhere
  • Machine learning
  • AI-enabled analytics
  • Quantum computing
Platforms and products Platform-as-a-service (PaaS) operating model provides foundation for development with reusable code; “building-block” product approach to development speeds up releases and makes process more flexible. (Read more.)
  • Life-cycle management across platform layers
  • Industrial Internet of Things (IIoT)
  • Vertical software as a service (SaaS)
Data management Need for real-time data-driven insights, data democratization (nonexpert users making advanced data queries), and acceleration of both data quantity and variability. (Read more.)
  • Use-case life-cycle management
  • Synthetic data
  • Data governance
  • Automated machine learning
Cybersecurity and privacy Data breaches are increasing while data-privacy concerns are resulting in varied regulatory changes, forcing companies to rethink security and compliance protocols. (Read more.)
  • Shift-left security
  • Automated testing
  • Zero-trust security
  • Data-protection law and practices

 

Significant skills gaps in these seven areas already exist, and we expect them to become more severe over time. Executives expect skills mismatches in functions that have already started adopting automation and AI technologies, according to McKinsey Global Institute analysis. The largest percentage of survey respondents (more than 30 percent) ranked data analytics, IT, mobile, and web design as the skills with the highest expectation of a mismatch over the next three years.

In Germany, 700,000 additional tech specialists are needed by 2023 to meet the economy’s demand for them.  For agile skills, demand will be four times greater than supply, and for big data talent, 50 to 60 percent greater. Globally, 3.5 million cybersecurity positions are projected to be unfilled in 2021.  

In addition to meeting the challenges of filling future roles, technology modernization requires knowledge of how to transition from existing systems, which are often written in outdated programming languages, such as LISP, ALGOL 58, or COBOL, and are understood mostly by an aging workforce.  

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Here is a direct link to the complete article.

Matthias Daub is a senior partner in McKinsey’s Berlin office, Ranja Reda Kouba is an associate partner in the Vienna office, Kate Smaje is a senior partner in the London office, and Anna Wiesinger is a partner in the Düsseldorf office.

The authors wish to thank the team of Jutta Bodem-Schrötgens, Florent Erbar, Teresa Keller, Anna Lena Robra, Hannah Mayer, Eileen Raßlenberg, Michael Scherbela, Surbhi Sikka, and Thaksan Sothinathan for their ongoing support and drive.

The authors also wish to thank Sapana Agrawal, Kerstin Balka, Sven Blumberg, Andrea Del Miglio, Anusha Dhasarathy, Vito Di Leo, Amadeo Di Lodovico, Karel Dörner, Desiree El Chebeir, Peter Jacobs, Shweta Juneja, Naufal Khan, Harald Kube, Mahir Nayfeh, Angelika Reich, Wolf Richter, Scott Rutherford, Henning Soller, Gisa Springer, Richard Steele, and Steve van Kuiken for their contributions to this article.

 

 

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