How to fill the analytics talent gap?

Citation

Harris, J.G. (2008), "How to fill the analytics talent gap?", Strategy & Leadership, Vol. 36 No. 5. https://doi.org/10.1108/sl.2008.26136eab.001

How to fill the analytics talent gap?

Article Type: CEO advisory From: Strategy & Leadership, Volume 36, Issue 5

Companies across many industries are facing a shortage of skilled analysts, a problem that has alarming potential to limit their strategy team’s effectiveness – and to blunt organizations’ ability to find opportunities for competitive differentiation.

Data-driven insights

This analytic talent shortage comes at a time when traditional bases of competition are rapidly eroding. Companies that come up with innovative features often find them easily and quickly replicated – perhaps half a world away. Product lifecycles continue to shrink while customer expectations keep changing. In this environment execution is paramount, forcing companies to make ever-smarter, better-informed decisions.

In turn, those decisions call for superior analytics – the extensive use of data, statistical and quantitative analysis, predictive models, and fact-based management. Using data-driven insights to make better decisions and extract maximum value from their business processes, companies such as Procter & Gamble, Honda and Samsung can readily identify their most profitable customers, speed up product innovation, optimize supply chains and pricing, and identify the true drivers of financial performance.

Accenture’s research confirms that high-performance businesses – those that substantially outperform competitors over the long term and across economic, industry and leadership cycles – are twice as likely to use analytics strategically compared with the overall sample and five times more likely to do so than low performers.[1]

Some innovative firms like Netflix use their data skills as a foundation for their business strategies. Doing so has helped the movie rental company grow from $5 million in revenue in 1999 to $1.2 billion in 2007. At the heart of Netflix’s business is a movie-recommendation “engine” based on proprietary software. Called Cinematch, the tool analyzes customers’ choices and feedback on the movies they’ve rented and recommends movies in ways that optimize both the customer’s taste and Netflix’s inventory.[2]

Other examples come from diverse industries. CEMEX has used analytics to optimize its supply chains so it can provide faster delivery than its competitors; as a result, the company can charge a premium for its cement products. Between 2000 and 2005, agricultural equipment maker John Deere saved $1.2 billion by employing a new analytical tool to better optimize inventory.[3] And financial services provider Capital One is very open about its use of data analysis to differentiate among customers based on credit risk, usage and other characteristics and to match customer characteristics with appropriate product offerings.[4]

Three clusters of analytics expertise

In our experience, analytics skills in high-performance companies tend to cluster into three groups: the senior management team; the professional analysts; and the staff whose use of the outputs of analytical processes has become critical to their job performance.

The high performers are reliant on their analytics elite – the professional number crunchers who design and carry out experiments and tests in order to define and refine analytical algorithms and to perform data mining and statistical analyses on key data. Most have advanced degrees – often PhDs – in fields such as statistics, operations research, marketing research, and applied mathematics.

Also necessary: the efforts and insights of the analytics “infantry” – the employees who increasingly must gather and interpret data in their daily jobs. Some firms, such as Capital One, hire large numbers of managers with some analytical background.

The talent shortage

There are plenty of anecdotes about the severity of the talent gap. At one leading pharmaceuticals multinational, the need for analysts for the marketing science group is encouraging executives to look at forming a custom outsourced unit. “These positions can take months to fill if they can be filled at all,” says one industry observer. The pharma sector is having trouble with finding specialists in areas such as biostatistics and analytical chemistry.[5]

All of which puts more of a premium on working conditions for analytics staff. We know of several organizations that have lost analysts who felt they were treated largely as “spreadsheet developers.” In other cases, compensation rates are sub-par, especially compared to those in the financial services sector. “I don’t hear Wall Street complaining that they are not able to attract enough analysts,” says one observer.

The natural answer might seem to be to outsource analytics functions. Indeed, there is a strong argument that talented quants have far more incentive to work for an independent analytics firms than for in-house analytics support functions. Much as highly creative people choose to work in ad agencies, many top analysts prefer to work at a specialty provider where they constantly have interesting work as well as access to competitive training, talent management and defined career paths.

Looking outside the organization is certainly an option. It can make sense to offshore raw number-crunching and report publishing for the occasional analysis or to bring in specialized expertise that is needed only sporadically. But an organization’s distinctive capabilities are generally too strategic to rely exclusively on outsiders. There is a strong case to be made for keeping in-house the data and analysis for strategic functions such as customer analytics.

Checklist for action

Easing the analytics skills crunch isn’t simply a question of getting HR to work harder to hire more quantitative experts. It calls for a broader perspective of the problem, coupled with the creativity to invent fresh solutions. Assuming that analytics is viewed as a core competency, here are eight ideas that may help business leaders avoid the worst effects of the shortage:

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  Appoint a top analytics champion. In organizations such as insurers and risk management firms, executive titles such as “senior vice president of customer insight and analytics” or even “chief analytics officer” are not uncommon. There is value in making one executive responsible for analytics across the company. This analytics champion would be tasked with continual improvement of the company’s business intelligence systems. The role would also signal the legitimacy of analytical activities both inside and outside the organization.

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  Put your best analysts where they’ll do the most good. Companies must make sure that their analytical talent is focused on the company’s “big impact” problems. For example, in a pharmaceuticals company, it may be far more important for bioclinical analysts to be able to help a new drug meet FDA trials windows than for marketing analysts to segment customers differently. The prerequisite to appropriate redeployment: A detailed inventory of the organization’s analytical skills and strategic decision processes.

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  Make discovery of analytical talent everybody’s job. The identification of and outreach to the best analysts cannot and should not be left up to HR. Yes, the hiring process itself is likely still an HR administrative function, but the networks and relationships that are key to finding analytical talent must be owned by everyone in the organization.[6] An appropriate role for HR is development of processes for hiring and rewarding analytical talent.

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  Recruit your “next decade” talent. While most of the previous suggestions can have an impact fairly quickly, you also have to open up more options for building and sustaining analytics talent long-term. That’s why it is important to build tight links with the best graduate school programs worldwide – programs with robust reputations for rigorous use of and training in analytics. Sponsorships and internships are just two of the proven ways companies can establish relationships with academic institutions that teach analytics – and to start getting to know individuals in their talent pool.

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  Upgrade your hiring practices. It’s not enough to hire qualified analysts. If a company is to really leverage analytics, all of its new hires must demonstrate analytical aptitude. Capital One, for example, requires every potential employee (including senior executives) to take a mathematical reasoning test.

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  Rethink your analytics org chart. Most high-performance companies have centralized their top analysts to some degree. (The formal units are sometimes called Business Intelligence Competencies Centers.) Procter &Gamble, for example, took analytical groups that had been dispersed around the organization and combined them to form a new Global Analytics group as part of the IT organization. Pooling analytics talent – or at least networking it more cohesively – can stretch the resource quite a bit.

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  Let social networks solve analytics problems. Your analytics expertise does not have to be on your payroll – or even in your home country. More than 40 percent of P&G’s products reflect some element of collaboration with external partners. The company’s Connect + Develop initiative welcomes involvement from retirees, suppliers, entrepreneurs, venture capitalists and others. Some companies are using Web-based “idea marketplaces” such as www.Innocentive.com and www.NineSigma.com to post requests (and rewards) for solutions to some of their trickiest problems.

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  Get fresh eyes on your data problems. Analysts who routinely analyze the same data and look at the same business problems tend to fall into a rut. Rotational assignments for analysts keep them on their toes and help them bring fresh perspectives to different parts of the business.

With mounting evidence that analytics are pivotal to competitiveness, business leaders would do well to figure out whether they have adequate access to the necessary skills – and if not, where they expect to source those skills.

Notes

1. Thomas H. Davenport and Jeanne G. Harris, Competing on Analytics: The New Science of Winning, Harvard Business School Press, 2007.2. Netflix quotation from Jena McGregor, “At Netflix, the secret sauce is software,” Fast Company, October 2005, p. 50.Other Netflix information comes from the company website (www.netflix.com); Mark Hall, “Web analytics get real,” Computerworld, April 1, 2002; Timothy J. Mullaney, “Netflix: the mail-order movie house that clobbered Blockbuster,” BusinessWeek Online, May 25, 2006; online at www.businessweek.com/smallbiz/content/may2006/sb20060525_268860.htm?campaign_id=search; and a telephone interview with Chief Product Officer Neil Hunt on July 7, 2006.3. Tallys H. Yunes et al., “Building efficient product portfolios at John Deere,” Carnegie-Mellon University, Working Paper, April 2004.4. Capital One Financial Corp. 2005 Annual Report on Form 10-K, http://library.corporate-ir.net/library/70/706/70667/items/189408/2005ARFinal.pdf 5. “Pharma cos. facing talent shortage in specialty areas,” Boston Business Journal, October 19, 2007.6. Peter Cheese, Robert J. Thomas and Elizabeth Craig, The Talent Powered Organization: Strategies for Globalization, Talent Management and High Performance, Kogan Page, 2008.

Jeanne G. HarrisJeanne G. Harris is an executive research fellow and director of research at Accenture’s Institute for High Performance Business (jeanne.g.harris@accenture.com)