Skip to main content

Tech looks to analytics skills to bolster its workforce

Addressing the analysis talent shortage

With demand for analytics skills outstripping supply, what tactics can technology companies take to address their talent shortage?

AI and IoT technologies are generating more and more data, but that data doesn’t mean much if organisations can’t use it effectively—one reason why the tech industry has increasingly sought employees skilled in analysis.1 In 2020, for the second time in four years, the number of jobs posted by tech companies for analysis skills—including machine learning (ML), data science, data engineering and visualisation—surpassed traditional skills such as engineering, customer support, marketing and PR, and administration (see figure).

Another most notable trend over the past eight years: the relative decline of the importance of core engineering skills. While engineering remains a critical asset, the rise in cloud and XaaS services has affected computer and hardware roles such as server administrators, computer hardware support technicians and professionals who work on the hardware side of router and storage management.2 The COVID-19 pandemic has hit electrical and hardware design engineering roles harder than others in the tech industry.3 By contrast, even as the pandemic was worsening business conditions in spring 2020, tech majors’ job openings for data analyst, data engineer and data architect roles continued to trend high.4

Tech companies have long been at the forefront of attracting professionals with advanced analytical skills5 and since 2014, tech recruiters have particularly targeted professionals with math and statistical skills, looking to harness their ability to study and analyse data to help solve real-world business issues.6 The race to AI has accelerated the crunch, as the top Silicon Valley companies have ramped up their workforce aggressively, focussing on advanced analytical skills such as ML, natural language processing, data engineering and data visualisation and image processing.7 Demand for data scientists and ML and AI specialists began surging in 2016.8

Tech companies continue to ramp up data scientist and data analyst talent.9 However, with businesses across industries scrambling to acquire AI talent and to increase their own data-driven decision-making, demand for data analytics professionals will likely outstrip the available talent for some time.10

Tech companies can navigate this complex talent landscape with a blend of tactics, such as taking a deliberate approach to recruiting new analytical talent, tapping existing workers’ potential and fostering strategic partnerships.

Considerations for tech industry execs

Selective hiring. Governed by strategic business objectives, executives can take a selective approach to assess whether analytics specialists are really needed, or if the goals can be accomplished with automated tools, XaaS programmes, or AI-based services.

Focussed and targeted reskilling. Leaders can look to elevate current employees’ skill levels on specific data and analysis fields such as ML, data analytics, data modelling, data architecture and data engineering.

Strengthening partnerships. Tying up with academia and universities, business incubators and accelerators, and the startup ecosystem to tap and bring the best and most-relevant data and analytics professionals into the fold.

Technology, Media & Telecommunications

Deloitte’s Technology, Media & Telecommunications (TMT) industry practice brings together one of the world’s largest group of specialists respected for helping shape many of the world’s most recognised TMT brands—and helping those brands thrive in a digital world.

Learn more

  1. Note on methodology: Tech companies’ job postings data is based on data posted by companies falling under the following NAICS codes: computer and peripheral equipment manufacturing, semiconductor and other electronic component manufacturing, manufacturing and reproducing magnetic and optical media, software publishers, data processing, hosting, and related services, and computer systems design and related services. We have combined and shown aggregated job posting numbers for four skill categories at rank No. 1 in the figure: information technology, business, sales, and finance. The “analysis” category includes the following subskills: data analysis, data science, ML, data visualization, statistical modeling, math modeling, statistical software, math software, data mining, and validation. “Engineering” includes electrical and computer engineering, engineering design, simulation, engineering management, process engineering, circuitry, mechanical engineering, signal processing, electronic hardware, industrial engineering, and automation engineering.

    View in Article
  2. Leslie Stevens-Huffman, “Tech jobs in danger of becoming extinct ,” Dice, October 24, 2017.

    View in Article
  3. For instance, over the course of March 2020, electrical engineering job postings declined 14 percent, and hardware design engineers was down 26 percent. Karen Field, “Upended by COVID-19, job market for tech craters ,” Fierce Electronics, April 8, 2020.

    View in Article
  4. Ritika Pradhan, “Data science jobs continue to be in-demand ,” Udacity, May 25, 2020.

    View in Article
  5. Magnimind Academy, “5 reasons to move to Silicon Valley for a data science job ,” November 13, 2019.

    View in Article
  6. Braingainmag, “Why math majors are in red hot demand ,” April 24, 2015.

    View in Article
  7. Alison DeNisco Rayome, “7 tech companies that hire the most data scientists ,” TechRepublic, June 18, 2019.

    View in Article
  8. Boris Plavljanić, “Most in-demand tech jobs in 2019 ,” Infinium, March 28, 2019; Forbes, “13 top tech skills in high demand for 2018 ,” December 21, 2017.

    View in Article
  9. Bejamin Beck, “The 15 best tech jobs boast top salaries, high satisfaction, lots of openings ,” Digital Trends, October 5, 2020.

    View in Article
  10. David Jarvis, The AI talent shortage isn’t over yet , Deloitte Insights, September 30, 2020.

    View in Article

The authors would like to thank Jeff Loucks for his counsel and suggestions, and Divya Tewari and Shruti Panda for their data analysis and research support.

Cover image by: Viktor Koen

Did you find this useful?

Thanks for your feedback

If you would like to help improve further, please complete a 3-minute survey