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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 DaiSolving the New Equation for Advanced Analytics Talent Chris Brahm is a Bain average calculated based on a sample of companies in each industrySources: LinkedIn; S company websites; company annual reportsConsumergoodsnullSolving 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 analytically 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 building 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 tasks 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 configured? 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: null effective advanced analytics talent pool includes eight key rolesThese four roles make up more than 70% of any given advanced analytics team across US industriesData architectUse case product managerData scientistData analystData engineerDevOps engineerMachine learningengineerUI developerCore data architect: Decidesand executes data architecturestrategy; oversees datamanagement tasksDatabase administrator:Manages data infrastructureday to dayEngages in exploratory analysisto understand trends that will createvalue for the business; generatesanalytical approaches and modelsAggregates, integrates andsummarizes large data setscombining structured andunstructured informationDevelops scalable tools andtechnologies specifically formachine learning use casesOversees execution of advancedanalytics use cases fromdevelopment through productionPrepares dashboards forinternal and externalcommunicationSupports continuous deploymentof use cases into scalable productionenvironment, automating processeswhere possibleCompletes process bytranslating algorithms intotools, reports and otherautomated solutionsSources: The Business-Higher Education Forum, “The Quant Crunch,” 2017; International Institute for Analytics; LinkedIn search; industry interviews;Bain AA Talent Survey (n=226)nullSolving 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 market 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 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-didates 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-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 science Figure 3: nullobal advanced analytics talent is expected to double by nullnullChinaSlight surplus in supply with structural mismatch expected Small to medium gap expected Medium to large gap expected65,00020182020 United States180,000310,000IndiaEquilibrium to small gap expected210,00075,000190,000125,000170,000WesternEuropeNote: 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 Reporting; India Ministryof Human Resource Development; China Ministry of Education; Edison Project; Bain AA Talent Survey (n=226); industry participant interviewsCurrent and projected advanced analytics talent in four regionsnullSolving the New Equation for Advanced Analytics Talent bachelors degree programs 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 bottleneckThe historical 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 continue 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- critical 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 developing 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 developing 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 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 includes 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 include free MOOCsmassive open online coursesand paid retraining programs. nullSolving 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 coding 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 talent ecosystemEmbracing 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 let a bottle-neck in analytics talent slow them down, and they dont have to. nullSolving the New Equation for Advanced Analytics Talent nullSolving the New Equation for Advanced Analytics Talent
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