MIT Sloan Management Review

Solving the Advanced Analytics Talent Problem

Around the world, select urban areas dominate the advanced analytics talent pool. If your headquarters doesn’t happen to be in Silicon Valley, Beijing, or Bangalore, however, there’s hope.

著者：Chris Brahm

First published on 10月 03, 2019
min read


This article originally appeared on MIT Sloan Management Review. In the age of big data and AI, building strong advanced analytics capabilities is a strategic imperative for companies. But advanced analytics talent is hard to find, even in the cities that have the most of it. For companies operating outside of the centers of tech gravity—university-fed cities like San Francisco and Beijing—filling machine-learning or analytics posts can be a challenge, but it’s one that forward-thinking companies are meeting with a mix of creative approaches. Among U.S. technology workers, there’s a clear preference for jobs in cities with strong technology hubs and at companies with well-established track records in analytics. Recent Bain & Company research shows this tendency of advanced analytics talent to cluster in big cities is actually a global phenomenon. In the U.S., 10% of the pool of advanced analytics talent sits in greater metropolitan New York City, and 14% is in the San Francisco Bay area. Similarly, more than a quarter of India’s advanced analytics talent pool is located in Bangalore, with another 13% in Delhi. And the concentration is even more pronounced in China, where two cities—Shanghai and Beijing—account for fully half of the country’s analytics talent. So, what actions can organizations outside of these power centers take? Create Dedicated Outposts Within Tech Hubs One traditional approach is to establish dedicated centers of analytics excellence within these tech hubs. One large retailer, for example, has established labs in Silicon Valley and Bangalore, in addition to a team at headquarters that includes the company’s chief digital officer. Though talent is expensive in the hypercompetitive Bay Area, the company’s management has concluded it would be too difficult to build enough scale elsewhere, so their most sophisticated advanced analytics professionals, especially in data science, are located there. In Bangalore, a less-competitive and less-expensive market than Silicon Valley, the company taps into skilled local technologists. The analytics team at headquarters focuses on applying technology and building the company’s technology systems. In all three locations, business partners work alongside the technologists, coordinating with their counterparts in the other offices. Invest in Academic Centers In addition to staking outposts in established hubs, companies, including large digital natives, increasingly go further afield, investing in emerging analytics centers to meet their growing needs. With some companies hiring thousands of data professionals, more and more universities are adding and expanding artificial intelligence and data-science programs today. The most mature sectors for advanced analytics are the ones planning to expand their teams the fastest, and they are following the talent. Cities like Toronto, Montreal, Atlanta, and Pittsburgh have become important centers for Google, Facebook, Amazon, and Uber, based on the strengths of their university programs, and now smaller cities like Louisville, Kentucky, are beginning to enjoy a similar dynamic. Recently, Microsoft announced that Louisville—the home of the University of Louisville and a center for manufacturing and health care—would be the company’s new regional hub for artificial intelligence, data science, and work on the internet of things. Expand Reach With a Hybrid Approach Besides broadening their geographical reach, companies can also address hiring challenges by tapping into a tiered talent strategy that leverages the expertise of outside partners. In this structure, a core, in-house analytics team focuses on developing critical mass in strategic tasks such as data-science team leadership and model development. Offshore data hubs, third-party service firms, and crowdsourcing can then handle less-critical advanced analytics work like tactical data management and model maintenance. Combining internal and external analytics capabilities, companies are creating a hybrid model that matches the breadth of advanced analytics expertise they’ll need in the future. Today, only 30% of companies handle all advanced analytics in-house. The other 70% augment their internal skills with some combination of offshore outsourcing, freelancers, advanced analytics consultants, and crowdsourcing. Support Internal Training Opportunities Even for in-house work, there are creative, flexible approaches that can expand a company’s internal talent supply. Offering retraining and continuous learning programs to existing employees with deep STEM backgrounds to help them develop new analytical skills is one option. Today, companies including IBM, EXL, and Airbnb offer employees classes and training in data awareness, collection, visualization, and robotics. Free MOOCs—massive open online courses—and paid retraining programs also provide options for re-skilling an existing workforce. In all, nearly a quarter of Bain survey respondents report that their companies have implemented some form of advanced analytics training programs. Companies with a good data-science workbench and the right set of data engineering tools have yet one more option: using automation to support existing STEM employees who may not have strong coding skills to build models and engineer data. As a young field, advanced analytics often lacks experienced managers. By helping existing employees develop these new skills, companies tap into a group that already knows the company, the industry, and how to operate effectively across the organization. By finding smart ways to make the most of in-house potential, tapping into the growing global ecosystem of expertise, and being open to automation and other tools that might help bridge gaps, leading companies are drilling new sources of talent. To solve the issue of advanced analytics talent concentration, organizations need to think creatively and strategically, using a variety of approaches that will bring the best talent and resources into the fold. Chris Brahm is global head of Bain & Company’s Technology and Analytics Group and a partner in the firm’s San Francisco office. He was cofounder and chairman of VoloMetrix, acquired by Microsoft, and the chairman of TSO Logic, acquired by Amazon.

This article originally appeared on MIT Sloan Management Review.

In the age of big data and AI, building strong advanced analytics capabilities is a strategic imperative for companies. But advanced analytics talent is hard to find, even in the cities that have the most of it. For companies operating outside of the centers of tech gravity—university-fed cities like San Francisco and Beijing—filling machine-learning or analytics posts can be a challenge, but it’s one that forward-thinking companies are meeting with a mix of creative approaches.

Among U.S. technology workers, there’s a clear preference for jobs in cities with strong technology hubs and at companies with well-established track records in analytics. Recent Bain & Company research shows this tendency of advanced analytics talent to cluster in big cities is actually a global phenomenon. In the U.S., 10% of the pool of advanced analytics talent sits in greater metropolitan New York City, and 14% is in the San Francisco Bay area. Similarly, more than a quarter of India’s advanced analytics talent pool is located in Bangalore, with another 13% in Delhi. And the concentration is even more pronounced in China, where two cities—Shanghai and Beijing—account for fully half of the country’s analytics talent.

So, what actions can organizations outside of these power centers take?

Create Dedicated Outposts Within Tech Hubs

One traditional approach is to establish dedicated centers of analytics excellence within these tech hubs. One large retailer, for example, has established labs in Silicon Valley and Bangalore, in addition to a team at headquarters that includes the company’s chief digital officer. Though talent is expensive in the hypercompetitive Bay Area, the company’s management has concluded it would be too difficult to build enough scale elsewhere, so their most sophisticated advanced analytics professionals, especially in data science, are located there. In Bangalore, a less-competitive and less-expensive market than Silicon Valley, the company taps into skilled local technologists. The analytics team at headquarters focuses on applying technology and building the company’s technology systems. In all three locations, business partners work alongside the technologists, coordinating with their counterparts in the other offices.

Invest in Academic Centers

In addition to staking outposts in established hubs, companies, including large digital natives, increasingly go further afield, investing in emerging analytics centers to meet their growing needs. With some companies hiring thousands of data professionals, more and more universities are adding and expanding artificial intelligence and data-science programs today.

The most mature sectors for advanced analytics are the ones planning to expand their teams the fastest, and they are following the talent. Cities like Toronto, Montreal, Atlanta, and Pittsburgh have become important centers for Google, Facebook, Amazon, and Uber, based on the strengths of their university programs, and now smaller cities like Louisville, Kentucky, are beginning to enjoy a similar dynamic. Recently, Microsoft announced that Louisville—the home of the University of Louisville and a center for manufacturing and health care—would be the company’s new regional hub for artificial intelligence, data science, and work on the internet of things.

Expand Reach With a Hybrid Approach

Besides broadening their geographical reach, companies can also address hiring challenges by tapping into a tiered talent strategy that leverages the expertise of outside partners. In this structure, a core, in-house analytics team focuses on developing critical mass in strategic tasks such as data-science team leadership and model development. Offshore data hubs, third-party service firms, and crowdsourcing can then handle less-critical advanced analytics work like tactical data management and model maintenance.

Combining internal and external analytics capabilities, companies are creating a hybrid model that matches the breadth of advanced analytics expertise they’ll need in the future. Today, only 30% of companies handle all advanced analytics in-house. The other 70% augment their internal skills with some combination of offshore outsourcing, freelancers, advanced analytics consultants, and crowdsourcing.

Support Internal Training Opportunities

Even for in-house work, there are creative, flexible approaches that can expand a company’s internal talent supply. Offering retraining and continuous learning programs to existing employees with deep STEM backgrounds to help them develop new analytical skills is one option. Today, companies including IBM, EXL, and Airbnb offer employees classes and training in data awareness, collection, visualization, and robotics. Free MOOCs—massive open online courses—and paid retraining programs also provide options for re-skilling an existing workforce. In all, nearly a quarter of Bain survey respondents report that their companies have implemented some form of advanced analytics training programs.

Companies with a good data-science workbench and the right set of data engineering tools have yet one more option: using automation to support existing STEM employees who may not have strong coding skills to build models and engineer data.

As a young field, advanced analytics often lacks experienced managers. By helping existing employees develop these new skills, companies tap into a group that already knows the company, the industry, and how to operate effectively across the organization.

By finding smart ways to make the most of in-house potential, tapping into the growing global ecosystem of expertise, and being open to automation and other tools that might help bridge gaps, leading companies are drilling new sources of talent. To solve the issue of advanced analytics talent concentration, organizations need to think creatively and strategically, using a variety of approaches that will bring the best talent and resources into the fold.

Chris Brahm is global head of Bain & Company’s Technology and Analytics Group and a partner in the firm’s San Francisco office. He was cofounder and chairman of VoloMetrix, acquired by Microsoft, and the chairman of TSO Logic, acquired by Amazon.