1、With the supply of talent growing fast, make the most of opportunities inside and outside your company. By Chris Brahm, Arpan Sheth, Velu Sinha and Jessica Dai Solving the New Equation for Advanced Analytics Talent Chris Brahm is a Bain average calculated based on a sample of companies in each indus
2、try Sources: LinkedIn; S company websites; company annual reports Consumer goods 3 Solving the New Equation for Advanced Analytics Talent Building an advanced analytics team Companies should not expect to fill this gap entirely by wooing experienced talent from other em- ployers. The most analytical
3、ly mature sectors plan to expand their teams fastest, and employees are most interested in working for companies with well-established track records in analytics, our recent survey of more than 200 industry participants found. Creative, flexible approaches for expanding the talent base include build
4、ing centers of excellence for pools of hired analytics experts, and also retraining capable existing employees and giving them access to automation tools. Importantly, rather than trying to do everything in-house, a tiered talent strategy should focus a core, in-house analytics team on strategic tas
5、ks while tapping offshore data hubs, third-party service firms and crowdsourcing for other work. Even the most sophisticated companies leverage a combination of internal and external supply chains for analytics capabilities. What is the optimal blend of advanced analytics roles? How are teams best c
6、onfigured? The exact bal- ance varies depending on the sector and maturity of a companys analytics practice, but teams will draw from eight key roles (see Figure 2). With companies hiring to create balanced advanced analytics teams, certain skills are in higher demand. Figure 2:Aneffectiveadvancedan
7、alyticstalentpoolincludeseightkeyroles These four roles make up more than 70% of any given advanced analytics team across US industries Data architect Use case product manager Data scientist Data analyst Data engineer DevOps engineer Machine learning engineer UI developer Core data architect: Decide
8、s and executes data architecture strategy; oversees data management tasks Database administrator: Manages data infrastructure day to day Engages in exploratory analysis to understand trends that will create value for the business; generates analytical approaches and models Aggregates, integrates and
9、 summarizes large data sets combining structured and unstructured information Develops scalable tools and technologies specifically for machine learning use cases Oversees execution of advanced analytics use cases from development through production Prepares dashboards for internal and external comm
10、unication Supports continuous deployment of use cases into scalable production environment, automating processes where possible Completes process by translating algorithms into tools, reports and other automated solutions Sources: The Business-Higher Education Forum, “The Quant Crunch,” 2017; Intern
11、ational Institute for Analytics; LinkedIn search; industry interviews; Bain AA Talent Survey (n=226) 4 Solving the New Equation for Advanced Analytics Talent A mixed global outlook The growth in advanced analyticstrained talent will occur primarily outside the US (see Figure 3). Long the largest mar
12、ket for analytics talent, the US will retain that top spot, but other global tech powers are catching up. The outlook is especially bright in India, where two trends are simultaneously expanding the talent pool. First, STEM undergraduate and graduate degree holders, whose programs of study emphasize
13、 data and analytics skills, continue to join the workforce in increasing numbers. Augmenting that is Indias deep existing ecosystem in information technology, especially in programming and systems integration. Outsourcing firms and the India IT centers of global corporations house many ideal can- di
14、dates for learning new advanced analytics skills. These two sources have combined to make India a vital hub of analytics expertise and have fueled the growth of its analytics outsourcing industry. The future is less clear for China. Thanks to its relatively supportive regulatory environment and ac-
15、cess to consumer data, China is often described as winning the race to dominate artificial intelligence and advanced analytics. But on advanced analytics talent, the country may need to accelerate. Recent trends point to a growing focus on talent in China, including a significant expansion in data s
16、cience Figure 3:Globaladvancedanalyticstalentisexpectedtodoubleby2020 China Slight surplus in supply with structural mismatch expected Small to medium gap expected Medium to large gap expected 65,000 2018 2020 United States 180,000 310,000 India Equilibrium to small gap expected 210,000 75,000 190,0
17、00 125,000 170,000 Western Europe Note: Advanced analytics talent refers to data architects, data scientists, data engineers and machine learning engineers Sources: LinkedIn; National Center for Education Statistics; UNESCO; International Institute for Analytics; Council on Integrity in Results Repo
18、rting; India Ministry of Human Resource Development; China Ministry of Education; Edison Project; Bain AA Talent Survey (n=226); industry participant interviews Current and projected advanced analytics talent in four regions 5 Solving the New Equation for Advanced Analytics Talent bachelors degree p
19、rograms and high levels of US recruiting and pay among Chinese digital natives and technology companies. Without an expansion in supply, talent could become a bottleneck, slowing analytics progress in China, especially for companies in traditional sectors. Breaking the talent bottleneck The historic
20、al shortage of analytics talent has caused many organizations to rely on a combination of internal and external advanced analytics expertise. This hybrid model also turns out to be a good match for the breadth of advanced analytics expertise needed in the future. A multilayered approach will continu
21、e to make more sense than full vertical integration for many companies. When possible, companies should develop critical mass internally in the most important aspects of advanced analytics, such as data-science team leadership and model development, and tap the external supply chain for less- critic
22、al skills, like tactical data management and model maintenance. Today, only 30% of companies are fully integrated in advanced analytics. The other 70% augment their internal skills with some combination of offshore outsourcing, freelancers, advanced analytics consultants and crowdsourcing. Part of d
23、eveloping this talent ecosystem involves harnessing shadow analytics talentthat is, taking people currently walking the halls and helping them develop new analytical skills. Existing employees already know the company, the industry and how to operate effectively across the organization. Part of deve
24、loping this talent ecosystem involves harnessing shadow analytics talentthat is, taking people currently walking the halls and helping them develop new analytical skills. Existing employ- ees already know the company, the industry and how to operate effectively across the organization. Many have the
25、 quantitative background to learn analytical skills, and nearly a quarter of Bain survey respondents report that their companies have implemented advanced analytics training programs. IBMs Data Scientist Academy, for example, is an eight-day boot camp followed by individual online learning, and incl
26、udes shadowing IBM data scientists and a capstone project. Airbnbs Data Universi- ty offers more than 30 classes in data awareness, collection, visualization and data at scale, and more than 500 employees (1 out of every 8) took part during the universitys first six months. Other train- ing options
27、include free MOOCsmassive open online coursesand paid retraining programs. 6 Solving the New Equation for Advanced Analytics Talent Some companies with a good data science workbench and the right set of data engineering tools also use advanced analytics automation to enable people without strong cod
28、ing skills to build models and engineer data. Of survey respondents currently using technologies to automate advanced analytics tasks, more than half say these technologies enable existing staff to be rapidly trained to take on re- sponsibilities of a data scientist or data engineer. Fostering the t
29、alent ecosystem Embracing this tiered approach will help companies today and in the future. It allows them to tap into the rapidly growing global talent supply and to create a more flexible model, all while leaving room to redeploy existing talent in new and creative ways. Companies cannot afford to
30、 let a bottle- neck in analytics talent slow them down, and they dont have to. 7 Solving the New Equation for Advanced Analytics Talent 8 Solving the New Equation for Advanced Analytics Talent Shared Ambition, True Results Bain & Company is the management consulting firm that the worlds business lea
31、ders come to when they want results. Bain advises clients on strategy, operations, technology, organization, private equity and mergers and acquisitions. We develop practical, customized insights that clients act on and transfer skills that make change stick. Founded in 1973, Bain has 57 offices in
32、36 countries, and our deep expertise and client roster cross every industry and economic sector. Our clients have outperformed the stock market 4 to 1. What sets us apart We believe a consulting firm should be more than an adviser. So we put ourselves in our clients shoes, selling outcomes, not proj
33、ects. We align our incentives with our clients by linking our fees to their results and collaborate to unlock the full potential of their business. Our Results Delivery process builds our clients capabilities, and our True North values mean we do the right thing for our clients, people and communitiesalways. For more information, visit
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