6

 

ANALYSTS

Managing Scarce and Valuable Talent

THOUGH COMPUTERS AND DATA drive analytical decision making, they are not nearly as vital as people; we’ve never seen an analytically oriented firm without plenty of analytically oriented people. Finding, developing, managing, and deploying analysts—the people who make the day-to-day-work of such organizations possible—is critical to a firm’s success.

Defining Analysts

In a large corporation, hundreds or perhaps thousands of employees have the word analyst in their functional titles, referring broadly to anyone who uses data or information. For our purpose in this book, we define analysts as workers who use statistics, rigorous quantitative or qualitative analysis, and information modeling techniques to shape and make business decisions—still a broad range of activity. To fully describe the challenges of managing analytical talent, we need to distinguish four types of analytical people. ¹

Analytical Champions

These champions aren’t the kind you’d see in a Gatorade commercial; they are executive decision makers who depend heavily on data analyses to make business decisions and who lead major analytical initiatives. Champions are the leading advocates on how analytical techniques and technologies can be used to guide decision making. For example, Tom Anderson of Capital One and then UPromise, profiled in chapter 4, falls into this category. Analytical champions have both strong business acumen and an appreciation of analytical techniques. They communicate well about analyses because they can articulate how analytics will benefit the organization. Often analytical professionals who have been promoted into senior management, such champions establish long-term strategies and specify how to achieve them. Their expertise lies in their understanding of the business and of how analytical techniques and technologies, such as trending, forecasting, and predictive modeling, and enterprise applications systems such as SAP or Oracle, can help achieve business objectives. They often provide guidance to others in their organization on IT systems or process-related topics.

Analytical Professionals

In the second category are the analytical professionals, the most proficient and knowledgeable employees across the range of quantitative skills. Analytical pros create advanced analytical applications by developing statistical models and algorithms to be used by others in the organization. Professionals typically employ advanced techniques such as trend analysis, classification algorithms, predictive modeling, statistical modeling, and optimization and simulation, as well as a variety of data-, Web-, and text-mining techniques.

Occupying the most creative level in the analytical cadre, professionals, like champions, provide analytical guidance to others in their organization. They are often involved in establishing long-term goals, specifying the best strategies to achieve them, and estimating the resources needed to accomplish them. Needless to say, you don’t become an analytical professional by reading Statistics for Dummies. Typically, these jobs require an advanced degree (often a PhD) in a quantitative field such as economics, statistics, operations research, or mathematics, or a specialty degree in a field like biostatistics, informatics, genetics, or applied physics. For example, health insurer HCSC’s special investigations department (which creates analytical solutions to detect fraud and does other specialized analytics) was led by Kyle Cheek, a PhD in political economy whose team included expertise in statistics, epidemiology, bioengineering, and business.

Professionals often have advanced technical skills, including coding in C++, SQL, and SAS. ³ The best professional analysts are not only technical and quantitative, but also skilled at explaining analytical problems and results in clear and nontechnical language. However, such a combination is difficult to find, and as we later argue, many professional analysts need translators to deal with mere businesspeople. Others in the business may view them with a mixture of awe and derision, referring to them as über-analysts, super-quants, or brainiacs. They typically make up only 5 to 10 percent of a company’s analyst cadre.

Analytical Semiprofessionals

The analysts in our third category apply the models and algorithms developed by professionals on behalf of the rest of the business. The majority of financial and marketing analysts are semipros. They may be sophisticated quants in their own right and may develop straightforward applications on occasion, but their primary role is to apply analytics to business problems for routine or specialized decision making. They are experts in data creation, collection, interpretation, and use—and in understanding the workings of a business through the structure and flow of its information. They perform complex queries and run models on data, link the analyses and insights to business results, and prepare business reports based on these analyses. Semipros are adept at working with analytical applications, visual tools for information analysis, and “what-if” tools, including marketing workbenches, financial planning models, pricing models, and sales forecasting models; statistical software, such as SAS or SPSS; and enterprise systems like SAP.

It’s particularly important for semipros to be able to translate the benefits of analytics into lay language for businesspeople—for example, by bridging the gap between hard-core analytical professionals and business managers. Some semipros are primarily business analysts—often MBAs with quantitative orientations. They are often process oriented and certified in process improvement methods, such as Six Sigma. Others are primarily information and decision analysts, usually with advanced degrees in computer science. Semipros may make up 15 to 20 percent of the total analyst cadre.

Analytical Amateurs

In the fourth category are the analytical amateurs, employees whose primary job is not analytical work, but who need some understanding of analytics to do their jobs successfully. By calling them “amateurs,” we don’t mean to be the least bit pejorative. On the contrary, amateurs are knowledgeable consumers of analytics who can apply analytical insights to their work. An amateur might be a business manager using data-driven insights to increase sales volume, a call center employee who relies on a “next best offer” recommendation in order to serve a customer effectively, or a warehouse manager who follows data-based advice about optimal inventory levels. Typically businesspeople who can enter and manipulate data using Excel spreadsheets and other basic information management tools, amateurs put the output of analytical models to work. They also summarize and report data to others in their organization. They include many of the most influential employees and executives in the business, people who combine information modeled by the professionals with their local data, knowledge, and experience to make analytics-based business decisions. Amateurs typically make up 70 to 80 percent (or more) of an organization’s analytical talent. To illustrate their range, we profile two very different analytical amateurs.

Analytical Skills

Quantitative skills are the core requirement for any type of analyst. But tuning a regression equation or manipulating a spreadsheet is only the beginning. Effective analysts need to be proficient not only with data, but also with people.

• Quantitative and technical skills are the foundation. Naturally, analytical professionals have more quantitative expertise than semipros, champions, and amateurs, but all analytical people must be proficient in the quantitative disciplines specific to their industry or business function: stochastic volatility analysis in finance, biometrics in pharmaceutical, and informatics in health care firms, for example. Analytical people must also know how to use the software tools associated with their type of analytical work, whether it be to build algorithmic models, define decision-making rules, conduct “what-if” analyses, or interpret a business dashboard.

• Business knowledge and design skills enable analysts to be more than simple backroom statisticians. They must be familiar with the business disciplines and processes to which analytics are being applied. They need enough general business background to work at the interfaces of business processes and problems. They also must have insight into the key opportunities and challenges facing the company, and know how analytics can be used to drive business value. ¹²

• Relationship and consulting skills enable analysts to work effectively with their business counterparts to conceive, specify, pilot, and implement analytical applications. Relationship skills—advising, negotiating, and managing expectations—are vital to the success of all analytical projects. Furthermore, an analyst needs to communicate the results of analytical work: either within the business to share best practices and to emphasize the value of analytical projects; or outside the business, to shape working relationships with customers and suppliers, or to explain the role of analytics in meeting regulatory requirements (e.g., utility company rate cases). For this reason, we’d modify Warren Buffett’s admonition from “beware of geeks bearing formulas” to “beware of geeks who can’t explain the benefits and limitations of their formulas” (though that may be a large proportion of geeks, admittedly). ¹³

• Coaching and staff development skills are essential to an analytical organization, particularly when a company has a large or fast-growing pool of analysts, or when its analytical talent is spread across business units and geographies. When analytical talent isn’t centralized, coaching can ensure that best practices are shared across the company. Good coaching not only builds quantitative skills, but also helps people understand how data-driven insights can drive business value.

In practice, we’ve found that few individuals come equipped with the full spectrum of skills we’ve listed (see “About the Analytical Talent Research” for the methods we used); analytical capacity is, apparently, not yet a Darwinian evolutionary priority. Therefore, a company needs the right mix of analytical talent in its ranks of analysts. For example, you must balance pros—who focus on more advanced analytical techniques—with semipros—who have a broader skill set, combining strong analytics with business design and management skills to link professionals to their customers. Figure 6-1 shows the relative strengths of each type of analyst across the major skill categories.

FIGURE 6-1

Typical skill proficiency levels by type of analyst

Source: Jeanne G. Harris, Elizabeth Craig, and Henry Egan, “How to Create a Talent-Powered Analytical Organization,” research report, Accenture Institute for High Performance, 2009.

Motivating Analysts

Securing the analytics skills you need isn’t simply a matter of getting HR to hire more quantitative experts. To find and keep analysts, you need an understanding of how they tick and an informed approach to managing them. First and foremost, analysts are motivated by interesting and challenging work that allows them to utilize their highly specialized skills. Challenge and complexity are essential for pros and semipros, particularly running sophisticated data analyses and developing new models and techniques. Like most analysts, Sharon Frazee, Vice President of Corporate Healthcare Analytics and Research at Walgreens, values the opportunity to do interesting work: “Money’s nice, but I get more excited about doing things that are interesting, not doing the same thing every day, and having opportunities for growth—to stretch my skills.” ¹⁴ Managers must be mindful of this fact as they design and organize their analyst roles. Perhaps the biggest demotivator for analytical pros is spending too much time on simple analyses and report generation instead of building and refining analytical models. We know of several organizations that have lost analysts who felt they were treated largely as “spreadsheet developers.”

Variety in their work and a sense of personal progress keep analysts interested and challenged. One grocery retailer found it could attract highly skilled MBAs to an essential but repetitive analytic and reporting job, but could not keep them for long before they became restless and sought out new challenges. Variety comes from fresh assignments and projects, “moving around” the business to expose analysts to strategic problems and priorities, and offering blocks of time for learning and experimentation. For small analyst groups or those without precedent for analyst career paths, the chance to develop new expertise is crucial: developing new knowledge of business functions, quantitative models, analytic techniques, and software packages are just few examples of such opportunities.

Analysts also want to do important work that makes a meaningful contribution. The models and applications they build must matter to the business. As Sharon Frazee told us: “I want the stuff I do to make a difference… Being able to do the kind of informatics work that actually gets applied, and to see things changed because of it, is a lot more important to me than a lot of other stuff.” Another big demotivator is feeling that only a fraction of one’s work is actually used. And if the business is not demanding important analytics work, the best analysts sense that it’s time to move on.

Analysts want to feel supported and valued by their organizations, but they also want autonomy at work—the freedom and flexibility to decide how their jobs are done. Managers should provide goals and resources, and then give analytical people freedom to organize their own work. Autonomy is not abandonment, however. Managers (and customers for that matter) need to recognize analysts’ work and make their contributions visible to senior management.

In addition to thinking about what analysts do, it is important to consider with whom they work. Analysts like to be surrounded by other smart and capable colleagues. Grouping analysts together—whether physically in functional groups, or virtually in communities of interest—keeps them motivated. It also promotes sharing of best practices, leading to knowledge spillovers, and allows companies to tailor career models and training opportunities to better suit the needs of their analysts.

Companies that can offer analysts the chance to work with other smart analysts and businesspeople have little trouble attracting analytical talent. Steve Udvarhelyi at Independence Blue Cross reports that their informatics organization offers just such an appealing environment that helps to attract analytical talent: “It’s a defined center of excellence. It creates a fertile ground for people to work with other people. It creates a critical mass where you get career opportunities, growth opportunities, and a good professional interaction.”

Designing meaningful jobs and ensuring opportunities for intellectual stimulation and career growth is only half the challenge. Our research points to several other factors important for organizations to hold on to their analysts. In particular, analytical people seek a strong culture of trust—where they feel like they are treated in a fair, consistent and predictable fashion, and where they believe that the other people in the organization are open and honest and act with integrity. Positive relationships with their immediate supervisors are also particularly important to analysts (especially semipros)—even more than they are to other employees. The absence of any one of these factors will often cause analysts to head for the exits.

Organizing Analysts

One of the most common questions we hear is, “What’s the best way to organize analysts?” When just getting started with analytics, it is easy enough to stash a handful of analysts in a functional department. But deciding how to organize and manage analysts (especially the highly skilled pros and semipros) becomes a management concern once executives begin to take analytics seriously. Because top analysts are a scarce and potentially very valuable resource, naturally you want to maximize their value to the business. It’s wasteful to have them locked up in one part of the enterprise working on low-value projects when there is high-value work elsewhere. It’s also wasteful to have them scattered across the enterprise working on small local problems and unable to band together in “critical mass” to tackle strategic initiatives. Organizational structure matters because it affects how you:

• Deploy people on the most important and value-adding work of the enterprise.

• Develop skills and experience that analysts need to maximize their potential.

For analytical professionals and semiprofessionals, organizational design boils down to two big questions (your analytical amateurs are by definition distributed across the enterprise):

• What’s the best way to group people to align them with business units for geographical and administrative convenience and to enable them to work with and learn from each other regularly?

• What are the best ways to coordinate among groups to impart an enterprise perspective, ensure that people are working on the most important projects, improve business performance, and provide ample opportunities for development?

The challenge in organizing analytical professionals and semiprofessionals is to get them working “close to the business” on the most important analytical initiatives while keeping them working “close to each other” to coordinate their efforts and for purposes of mutual learning and support.

In our research and discussions with companies, we’ve found that there may be no single right answer to how to organize your analysts—but there are many wrong ones. So we’ll try to help you avoid the common mistakes, while also showing what an organizational model should accomplish.

Companies with a lot of semipros often house them with the functions they serve, so financial analysts work in finance and marketing analysts work in marketing. On the other hand, when semipros are few and multipurpose, they may be grouped with the professionals. Some companies gather them into a centralized unit, so they can work with and learn from each other regularly. Others align their analysts with business units for geographical and administrative convenience. Figure 6-2 depicts five basic options for organizational structure of analytical professionals and semipros in large, multidivisional corporations. We discuss them in order from the most centralized to the most decentralized.

FIGURE 6-2

Options for organizing analytical talent

Centralized. In a centralized model, all analyst groups report to one corporate organization, even if they are assigned to different business units or functions. This centralization makes it easier to deploy analysts on projects with strategic priority; however, it can create distance between analysts and the business, especially if the analysts are all housed in the corporate location. One organization employing this approach is the candy manufacturer Mars, whose centralized Catalyst group has long-term funding and can be deployed strategically to work with any part of the business. Expedia has also recently formed a centralized analyst group.

Consulting. In a consulting model, all analysts are part of one organization, but instead of corporate deploying analysts to business unit projects, the business units “hire” analysts as consultants to their analytical projects. There are several benefits to this model: it is more market driven than the centralized model, and by consolidating its analysts it also enables an enterprisewide view of what’s going on. Even more important, it allows the consultants to educate and advise their customers on how to utilize analyst services—in other words, to make the market demand smart. This model falters under weak enterprise focus, poor executive leadership, or faulty targeting mechanisms, all of which create an environment in which analysts end up working on whatever project the business units choose to pay for (or whatever wheel is squeakiest) rather than projects that deliver the most business value. As with the centralized model, analysts may be resident in business units, but they report to the central consulting organization. United Airlines, eBay, and Schneider National all employ the consulting approach.

Functional. In a functional model, a single analyst group resides in whichever department or function is the primary consumer of analyst services, but the group may also provide consulting services to the rest of the corporation. The functional model allows analysts to migrate as analytical applications are completed, and the analytical orientation of the corporation changes. For example, the analytics unit may report first to operations or logistics, then later migrate to marketing. Fidelity, for example, employs the functional approach; the great majority of their analysts work in the “customer knowledge” group, which reports to marketing. However, its analysts can serve other parts of the organization as consultants when needed.

Center of Excellence. In a center of excellence (COE) model, analyst groups are decentralized. They reside in every major business unit or function that has an appetite for analytics, but all groups are members of (and perhaps report dotted line to) a corporate center of excellence for analytics. The COE model builds a community of analysts who can learn from each other by sharing experiences and best practices. Sometimes a strong COE can also double as a “program office,” looking across analytical initiatives, advising on project priorities and staffing, and facilitating when analyst groups need to borrow staff from one another. Both Capital One and Bank of America employ versions of this model: at Capital One the “heavy-duty” PhD statisticians are in a center of excellence; Bank of America has analysts spread around the bank, so it tries to overcome the model’s limitations by holding regular conferences and running an online portal to encourage communication among analysts.

Decentralized. In a decentralized model, analyst groups are associated with business units and functions without any corporate or consolidating structure. The decentralized model is the most prevalent according to our research, which reflects the immaturity of most corporations’ analytical capabilities today. This model makes it difficult to set enterprise priorities and to develop and deploy staff effectively through borrowing and rotation. We’ve found that the purely decentralized model is effective only in the rare case of a large, diversified, multibusiness corporation in which the businesses have little in common. Because it wouldn’t be a compliment, we won’t name any companies with decentralized organizational models—but there are plenty of them.

We have also seen variations on these models. Procter & Gamble, for example, starts with a centralized model, consolidating over 120 analysts in a central shared services organization. But the analysts, who are each assigned to a brand or business unit, work at locations around the world. Still other P&G analysts work on a consulting basis. We’ve also seen “combination” models—like decentralized analytics groups plus a corporate consulting cadre that provides specialized services and supplemental staff to business unit projects. Finally, corporations with autonomous business units often use a “federated” approach that installs analyst groups in each business unit, establishes a corporate group to drive enterprise initiatives, and coordinates all the groups with clear “guidelines of federation.”

Of course, the companies we studied all felt that they had sound reasons for the models they chose. The decentralized model emerged as the most popular approach, with 42 percent of respondents. ¹⁵ Unlike in high school, however, popularity isn’t everything. The ideal model ensures that your scarce and valuable analysts (1) are tasked with the most important analytical projects, (2) bring an enterprise perspective to bear, and (3) have ample opportunities for development and job satisfaction.

We think the centralized and center of excellence models (or a federated model combining elements of both) offer the greatest potential benefit for organizations ready to take an enterprise approach to analytics. Analysts in a centralized or center of excellence model have significantly higher levels of engagement, job satisfaction, perceived organizational support and resources, and intention to stay than decentralized analysts or those who work in consulting units. Centralized and center of excellence organizational models are most common in more sophisticated analytical enterprises. And both models were most often found in the industries that we’d expect to have advanced analytical capabilities, including retail, consumer goods and services, financial services, and health and life sciences.

Company size was less of a factor than type of industry, although we found that companies with fewer than a thousand employees were more likely to organize their analysts into one functional area, while larger companies (with over twenty-five thousand employees) were more likely to operate with a center of excellence model.

Managing Analysts

Vital to the long-term success of any analytically minded organization, analysts are often among the most difficult talent to locate, attract, and retain. However, few companies manage their analytical talent as a strategic resource. Because analysts are often scattered throughout the organization, many companies don’t even have a clear picture of who their analysts are, where they reside organizationally or how many they have. They certainly don’t recognize or manage them as a distinct and pivotal workforce segment that requires its own recruiting strategies, training and development plans, career paths or performance management processes.To effectively manage analysts, companies must: (1) define the organization’s needs for analytical skills; (2) tap into new and diverse analytical talent pools; (3) develop analytical and business skills; and (4) deploy analysts so that their efforts are aligned with the organization’s strategic goals. ¹⁶ The following sections describe these four activities.

Defining Resource Needs for Analysts

The first step in defining a company’s needs for analysts is to identify analytical capabilities that a company needs to achieve strategic and operational goals, determine which analyst jobs are “mission critical,” and inventory the analytical talent already in place to fill these needs. Armed with an understanding of their analytical resource requirements and current employee skills, companies can make better use of the analytical talent they have and plan their human resource needs more effectively. For example, one global consumer products company predicts its talent needs three years out, based on an understanding of where the business is going and what skills and capabilities will be needed. Bank of America reviewed its employees and identified a subset of its job descriptions as “analytical.” The company ultimately identified over two thousand people as analytical professionals, semipros, or amateurs.

FIGURE 6-3

Analytical skill assessment at a retailer

Before hiring for a retailer’s many open analytical positions, managers first evaluated the skills already available across the business (see figure 6-3) and compared them to where they were most needed. This review of skills and open positions found many people who were either under-or overqualified for their work. Realigning people with positions reduced the number of new hires and had the added benefit of greatly improving employee job satisfaction and engagement.

Tapping into New Sources of Analysts

Given the growing demand for analysts, astute business leaders must ensure that they can hire or source the necessary skills. Organizations used to recruit analysts just like they sought any other white-collar worker. Now, they must discover novel ways to access analytical skills—from using untapped skills of existing employees to finding new talent pools. Doing so ensures that analytical companies are not locked into particular locations or stuck in shrinking talent pools.

Just as adventurers on safari seek out game at the watering hole, you can most easily find analytical people where they naturally gather—analytics conferences like INFORMS (the operations research society), vendor-sponsored meetings, university groups, and industry interest groups. Analysts often prefer to live near quantitatively oriented universities and major financial centers where their skills are in highest demand. Companies looking for advanced skills may also turn to resources like social networking groups (such as LinkedIn), specialty search firms, and quant Web sites. ¹⁷ Even poaching analysts from high-performing competitors has become commonplace.

Getting the best analysts can require flexibility. One manager was able to fill several positions at once by allowing new hires to remain in the small town where they lived after their former employer moved their positions to a large city. Another way to gain access to top analysts is to tailor job descriptions to fit the individual. Kyle Cheek was originally hired into HCSC in the special investigations department, in a position created solely to facilitate the hiring of political economics PhDs. Although he had the analytical skills the company needed, his atypical background could otherwise have prevented his getting a job because he did not fit any position requirements.

Any company seeking an ongoing supply of internal analytics capabilities must also forge tight links with the best graduate school programs worldwide—programs with robust reputations for rigorous training in analytics. Sponsorships and internships can form close relationships with academic institutions. Dow Chemical, for example, enjoys a long-standing partnership with Central Michigan University and hires many of its graduates. Similarly, SAS endowed a Masters in Advanced Analytics program at North Carolina State, and companies have flocked to hire its alumni.

Some organizations go even further to connect with future recruits well before they hit the universities. Texas Instruments joined forces with the National Council of Teachers of Mathematics and CBS to sponsor an education program based on the television program Numb3rs,whose story lines are inspired by actual FBI cases in which mathematicians solve crimes and prevent terrorist attacks. By generating interest in math, science, and technology, these analytical organizations promote the development of analytical skills at an early age.

Traditionally, companies have either supplemented in-house analysts with independent contractors or outsourced work to specialist firms in North America or western Europe. But given the talent shortage in these markets, businesses are increasingly looking to emerging markets—such as China and India—to bridge the talent gap. Often underrated as a vast pool of highly educated, cheap number crunchers, these emerging markets are becoming an excellent source of talent as they gain the capabilities and experience to carry out some of the most complex analytical tasks. Leading this trend, India is the fastest-growing market for offshore analytics delivery. By 2011, we estimate that the majority of the offshore market for analytics delivery will be based in India. ¹⁸

Some resourceful organizations have uncovered creative new sources of analysts, proving that your analytics expert does not have to be on your payroll—or even in your home country. For example, 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 their trickiest analytical problems. And Netflix had a competition that offered a top prize of $1 million to whoever can improve by at least 10 percent the accuracy of Cinematch, its movie recommendation algorithm. ¹⁹ Even MIT has taken to leaving advanced mathematical equations on its chalkboards after hours, in hopes that one of their janitors happens to be an underachieving quantitative genius. Oh, wait—that may have just been a movie. ²⁰

Developing Analysts

Analytical techniques and tools are constantly changing, and the skills demanded of analysts at all levels are perpetually being redefined. To respond to this constant flux—not to mention keep analysts engaged—analytical organizations must invest in updating their analysts’ skills, particularly those that accelerate strategic business benefits.

One approach is to train analysts internally. Analytical amateurs especially benefit from improving their skills in analytical opportunities, methods, and tools. At Procter & Gamble, for example, the central product supply analytics group offers a course called Analytics with Spreadsheets—often the preferred tool of analytical amateurs. The trucking firm Schneider National’s central analytics group offers the courses Introduction to Data Analysis and Statistical Process Control in Services.

Another avenue to increase analytical skills is by rotating developmental assignments. Analysts who routinely analyze the same data and look at the same business problems tend to fall into a rut. Rotational assignments keep them on their toes and help them bring a fresh perspective to different parts of the business. Analytical amateurs also learn well when they have the opportunity to work alongside different pros and semipros on analytics projects. These assignments can be part of a formal job rotation program; when managed well, they can turn their departments into hotbeds of valuable analytical activity. For example, GE Money’s offshore analytics centers in Shanghai and Bangalore loan their staff for temporary assignments in business operations through a formal job rotation program. This has aided retention (in very competitive markets) and improved employee engagement by offering analysts new learning opportunities, task variety, and a sense that they are making meaningful contributions to the business. The local businesses see the benefits, too, and demand for these analysts is growing. ²¹

Deploying Analysts

Matching a person’s skills and aspirations with the jobs the business needs is critical; however, it’s no simple trick to find people who can not only deliver the goods today but also develop the skills to deliver different goods tomorrow. Most analytical amateur work and some semipro work is predictable and routine, so if people demonstrate the skills and the company has established performance measures, then matching talent to work is relatively easy. Analytical pros, like PhD statisticians, for example, are another story. Highly educated pros, with rare combinations of knowledge and experience, are in scarce supply. So put your best analysts where they’ll do the most good. Make sure that the pros are focused on the company’s biggest problems and be prepared to move them around as needed.

Deploying analysts appropriately is a win/win. Analytical people have skills that are in high demand, so organizations should recognize analysts as a special segment of high-value workers whose preferences and motivators are distinct from those of the average employee. Organizations must also connect analysts with one another, especially when they are few or dispersed; creating communities reduces ineffective “pockets of analytics” and facilitates learning.

Managing Analysts Through the Stages

Analytical people may be the last element of the DELTA model, but they are by no means the least important one. An investment in understanding what makes analytical people tick and how to take full advantage of their skills and capabilities has a huge payoff. In the early stages of business analytics, just attending to the care and feeding of your analysts is enough. But as demand builds, the organizational challenges increase.

At each stage, consider how to acquire, develop, engage, organize, develop, and deploy analysts. Recommendations by stage are summarized in table 6-1.

From Stage 1 to Stage 2.Start to identify and assess your existing analysts: who they are, where they reside within the organization, and how deep their analytical skills range. It probably won’t take you long! Offer training opportunities aimed at developing analytical expertise—in statistical methods, software tools, and applications. Encourage communication and informal links between analysts and the rest of the organization. Enlist senior business leaders to get to know, appreciate, and engage analytical workers.

From Stage 2 to Stage 3.This transition should mark a more sustained effort to obtain, manage, and develop the best analytical talent. Seek out and nurture specialized recruitment sources (Web sites, associations, academic contacts) that target analysts. Customize job descriptions to attract analytical skills and know-how. Encourage networking among analysts of all levels—champions, professionals, semiprofessionals, and amateurs—to share knowledge and skills. Move people who work on similar topics closer together to encourage collaboration and drive efficiency. Support and coach analysts in data-heavy roles—this will help you see how to improve training and development. Finally, foster general business and industry expertise in analytical talent.

TABLE 6-1

Moving to the next stage: Analysts

From stage 1 Analytically Impaired to stage 2 Localized Analytics	From stage 2 Localized Analytics to stage 3 Analytical Aspirations	From stage 3 Analytical Aspirations to stage 4 Analytical Companies	From stage 4 Analytical Companies to stage 5 Analytical Competitors
Identify pockets of analysts and skills. Offer analytical skills training. Encourage analytical components of systems projects. Enlist managers to appreciate and engage analytical employees.	Define analytical positions and use specialty recruiting sources to fill them. Encourage knowledge sharing among analysts of all types. Promote rotational deployment of analysts. Provide coaching and support, especially for analytical professionals.	Evaluate analytical expertise of all information workers, develop relationships with universities and associations, and provide advanced training for analysts. Focus on developing business acumen in analysts and analytical expertise in business executives. Integrate the development and deployment process. Form communities of analysts.	Hire analytically minded employees in all business roles. Formalize an analyst-role/business -role rotation program. Organize and deploy analysts centrally. Regularly recognize analytical employees in all roles, and ensure that analysts are constantly challenged in their work.

 

From Stage 3 to Stage 4. Build links with academia and industry associations in order to gain access to future recruits and specialized skills. Provide training and development opportunities for professionals and semiprofessionals to keep them up-to-date with the newest processes, software, and technologies. Champion the use of analytics within your organization by developing business acumen in your analytical professionals and analytical knowledge in your business executives. Create wider communities for your analysts, spanning corporate functions, business units, and geographies. These communities allow analysts to keep up-to-date with the latest developments in analytics, solve common problems, and discover opportunities. If feasible, centralize your key analysts.

From Stage 4 to Stage 5. Tailor your HR strategy and processes around analytically minded employees—even incoming amateurs should display a basic level of analytical expertise. Provide interesting and challenging work to attract, engage, and retain outstanding analytical talent. Implement a rotation program to give your analysts greater exposure to the challenges facing different functions, business units, and geographies. Adapt performance management processes to reflect your expectations of analysts, thereby ensuring that your analytical talent will receive recognition for their work.

Keep in Mind …

• Top quants are scarce resources, not human calculators. Analysts can feel isolated from their business colleagues and overlooked when it comes to their contributions. They are likely to depart for greener pastures when they feel unappreciated.

• Analytical workers are motivated by interesting and challenging work, not by large paychecks alone.

• Offshore resources, professors and their graduate students, and open competitions are innovative sources of analytic talent. Seek a variety of talented analysts by cultivating sources like universities, social networking sites, and conferences.

• Hire employees with analytical aptitude and give them the training they need to use analytics well.

• Organize and deploy your analysts strategically to leverage their skills across the enterprise, whether in a centralized group or on rotation across the organization.

• Analytics is a broad field of knowledge: analytical amateurs, semipros, and professionals require quantitative and technical skills, industry expertise, and business acumen; but they also need the skills to communicate effectively, build relationships, and coach others.