The Big Data Opportunity for HR and Finance

Sponsored by Workday

THE BIG DATA REVOLUTION—creating new ways to gather and analyze information of varying types, size, and volume—is no longer the sole prerogative of marketing, sales, and product development. Finance and human resources (HR) departments, often working together, are finding that they too can find new insights and improve strategic decision making.

Many companies are just beginning to build capabilities in this area. To recognize the full benefits, clearly HR and finance will need to invest in big data technology—just as market- ing does—to collect and analyze their information. And that means that HR and finance will need to win the approval and support of the C-suite for funding, as well as demon- strate they can create a culture that emphasizes scientific methods and data-driven deci- sion making to fuel inquiry and innovation.

of CEOs view human capital
as the top factor contributing to
sustainable economic value.

“Big data” is a term used to describe the new volume, variety, and velocity of data that now confront companies—information that can be difficult to store, search, and analyze, especially with legacy systems and tools. But more important, big data also represents a new set of opportunities for data-driven decisions tied to business strategy across the organization.

Those opportunities have been identified by leaders at companies like Spectrum Brands, which has taken its first steps toward blending HR and financial data in big data projects. “Big data in HR will allow us to move beyond simple headcount and be more predictive than reactionary,” says Adria McCarthy, director of HR systems and technology at Spec- trum Brands, a diversified consumer products company with 13,000 employees globally. “Predictive modeling is the Holy Grail of reporting and analytics.”

of CEOs indicated that investing
in HR is a high priority.

At Spectrum, the human resources and the financial planning and analysis teams are strong allies, in part because they both experience the same demands to combine HR data and financial data into actionable analytics that are meaningful to the business. “Our financial analysis and planning leader, who reports to the CFO, is extremely supportive of our efforts to provide full visibility of workforce data and trends to our executive leader- ship team,” McCarthy says. “HR cannot move fast enough for him in this effort.”

Spectrum chose early big data projects to address the challenges both teams face and to relieve some of the burden to produce reports that combine HR and financial data analysis.

For its first project, Spectrum focused on a problem that, when solved, alleviated a large headache for human resources, finance, and business unit leaders. As in many enterprises, human capital is one of the areas that Spectrum business unit leaders use to monitor and control costs. But there was no quick and simple way they could know whether they were on budget and what their projected trend would be for the remainder of the year.

Sharing information across silos.

Big data in HR will allow us to move beyond simple headcount and be more predictive than reactionary.

“At times, perceptions were not always consistent with reality,” McCarthy says. “If our leadership does not have full and immediate visibility into our current workforce data, it is very easy to jump to the conclu- sion that our divisions are just hiring, hiring, hiring and therefore the headcount must have grown expo- nentially. But in reality, year over year, headcount and total compensation might actually have stayed flat or even decreased.”

In the past, multiple company resources, including financial analysts in the CFO’s office were routinely asked by business unit leaders to run these numbers, a task that could regularly consume many hours for each request.

Taking advantage of a newly installed system that blends HR and finance data, McCarthy led a project to create a manager self-service feature on the dashboard that will provide Spectrum’s financial and execu- tive leadership the ability to get a metric for current headcount and salaries against budget in real time. The data comes from different functional silos, including salaries, headcount, and open position requisitions.

“Now, front and center on our dashboard is our global headcount,” she says. “And we will be able to proj- ect whether we are going to be over or under by end of year.”

“Coming next to the dashboard,” McCarthy says, “is EBITDA per employee, an important measure of productivity and a ratio of employee compensation.” EBITDA stands for earnings before the deduction of interest expenses, taxes, depreciation, and amortization, an approximate measure of operating cash flow. “The new metrics will assist executive leadership in tracking company and business unit health and performance and easily monitoring a key metric reported to our executive committee and board of directors,” she says.

The View from HR

As Spectrum Brands shows, the big data journey is just beginning for many companies. In most HR organizations, data-driven methods are still limited to collecting workforce metrics, using comparison benchmarks, reporting to management, and automating the delivery of those reports. This, however, is important groundwork for the more rigorous analytical use of big data, the predictive modeling it makes possible, and more data-driven human capital decisions.

At present, few HR organizations use predictive modeling to understand the workforce the way marketing teams use big data to predict customer behavior and patterns. However, there is a desire among HR execu- tives for predictive models, the most commonly identified cases being hiring, retention, and performance.

“We’re still in the early stages of understanding how to align our efforts with data to solve real busi- ness problems,” says Steven Rice, executive vice president for HR at Juniper Networks, Inc., a developer of software, silicon, and systems that power advanced Internet-based networks, with more than 9,000 employees. “We began with the data in our HR information system (HRIS). We’ve now expanded into cross-functional information and combine those elements with HR data to create broader context. That means we’re starting to leverage more company wide data, including sales, finance, legal, and more.”

Early HR adopters of big data throughout organizations are gleaning insights that replace intuition as the basis for human capital decisions and win plaudits from the C-suite. Here are some recent examples, many of which involve financial data:

■ Using performance data, sales data, and employee survey data, retailers determine which employees are most successful and why, then develop pre-hire screening surveys that predict which applicants are most likely to succeed and produce higher sales.

■ A restaurant chain conducted a multivariate analysis of financial data and found that futures contracts for all the agricultural commodities it uses have a surprisingly big impact on profitability. It redefined the profile for its buyers and now targets job candidates from top commodity trading houses.

■ An electronics manufacturing company built a model that predicts the impact of attrition, wage increases, and profit on each other, to help each factory use site-specific data to set optimal pay rates and better manage thin margins.

■ Several companies use embedded sensors in office furniture to better understand employee behavior patterns that in turn lead to optimized office design.

■ Some HR organizations are figuring out how to analyze unstructured data from career-oriented social networking sites not only for recruiting purposes, but to better understand career progressions so they can create more effective learning and development activities.

In many early HR examples, big data has been used to either confirm or refute conventional wisdoms held within the enterprise. At Juniper, for instance, one analysis confirmed a widely held assumption but refuted another. It confirmed the assumption that a particular class of engineers was not paid enough and refuted the assumption that the pay structure was at fault for not being market-competitive.

In early 2013, Rice and his team saw that the turnover rate for one of Juniper’s most advanced engineering teams was higher than what other engineering groups were experiencing. “Everyone assumed the engi- neers were not paid enough and an analysis proved that assumption was correct, but it was only half the story,” says Rice. The other assumption was that the pay structure must be out of whack with the market. “The pay structure for the position was market-competitive, but the manager was paying them only at the lower end of the pay grade,” Rice says. Presented with the facts, the manager took the obvious steps to take advantage of the full range available in the pay structure, resulting in reduced attrition rates.

The View from Finance

Traditional finance organizations continue to competently do what they’ve always done—manage, ana- lyze, and report financial data each quarter. And more advanced departments provide strategic guid- ance to the C-suite and the operating units. Traditional business intelligence (BI) tools have been widely adopted by finance organizations to orchestrate performance management across the enterprise, but big data is not traditional and offers opportunity only some finance organizations have begun to realize.

If a challenge for HR is to find staff with the quantitative skills for big data, then a challenge for finance is to find talent that can see beyond the traditional statistical methods of the finance function as well as have in-depth knowledge of the business issues. For example, creative new approaches to produc- ing actionable insights in risk management, revenue forecasting, indirect cost controls, and compliance require more than just traditional number crunching skills.

“Big data analytics requires people who are knowledgeable about the business, who think creatively around insights and patterns, and who have the methodologies and tools that provide insights to help inform decisions,” says David Hom, a principal at Deloitte Consulting.

Early finance adopters of big data are gleaning insights that go beyond the traditional income statement analyses.

“At present, big data is having a bigger impact on problems related to products, compliance, and customer engagement,” says Hom. “In HR, many of our clients are still figuring out how to get an accurate head- count; however most want to better leverage their data to support decision making.” Hom’s experience with clients confirms what many surveys find: HR and finance lag others in big data funding and adoption.

However, early finance adopters of big data are gleaning insights that go beyond the traditional income statement analyses and are winning favorable notice from the C-suite. Here are some recent examples.

■ A transportation and real estate management company mitigated risk when the 2008 economic downturn hit, because a year earlier it had built a predictive model based on data about its industry, property inventories, and other pertinent information. Based on the model, which was at odds with the board’s intuition, the company redirected more assets into operations and away from what was a still-booming commercial real estate business. When the market crashed, it was in a better position than competitors.

■ To better understand its risk, a pharmaceutical company analyzed comments on social media and in international news reports to supplement its financial analysis. This included using sentiment analy- sis to validate a result predicted by a financial model or to identify a new drug regulation that could restrict the company’s market potential.

■ Several insurance companies use big data techniques to identify and move faster against fraud cases.

■ A bank uses transaction and propensity models, with internal and external sources of data, to pre- emptively identify which of its primary relationship customers may have a credit card or a mortgage loan that could benefit from refinancing.

In addition to the examples of actual use of big data by finance noted above, here are some finance scenarios that could be undertaken with big data:

■ Glean insight into what moves the revenue needle, by pulling in competitive, regional, and economic data sets.

■ Keep tabs on how suppliers are perceived in the market, using a supplier sentiment analysis and scorecard.

■ Reduce risk exposure by identifying risky customers and supplies as well as fraudulent transactions through social media and other Internet-based unstructured data.

More often than not, data from finance supports efforts by other departments, including HR. As keeper of the enterprise’s central truth about money, the finance organization can and should find ways to assist marketing, customer service, and HR to access the financial data often needed for their big data efforts.

HR and Finance—Big Data Allies

CFOs and chief human resource officers (CHROs) manage two of the most strategic enterprise resources— financial capital and human capital. Any CEO might acknowledge as much, but when it comes to big data investments, CEOs appear focused elsewhere—for now. This suggests the CFO and the CHRO ought to join forces in the pursuit of big data agendas, and in many cases they do.

In a 2012 IBM global survey of 1,709 CEOs worldwide, respondents identified “human capital” as the “key source of sustained economic value,” while “data access and data-driven insights” ranked third. figure 1 This might indicate that CEOs are ready to use big data in human capital decisions. But the survey also suggests a blind spot. It found 73 percent of CEOs making “significant investments” in “new insights” about operations. Yet “investing for new insights” about the workforce was a lower priority—and devel- oping new insights about financials even lower. figure 2

A recent global survey of executives at more than 1,200 companies by Tata Consulting Services confirmed that finance and HR are among the lowest priorities for big data investment. figure 3 As the poor bedfellows for analytics budgets, CFOs and HR executives could be natural partners in the quest to secure tools and talent to leverage big data.

Figure 1

Human Capital—the Key Source of Sustainable Economic Value
Percentage of CEOs indicating that a factor contributed to sustainable economic value.
Source: IBM Fifth Biennial Global CEO Study, 2012
Capture d’écran 2015-10-29 à 07.05.40

Figure 2

CEOs Place a Lower Priority on HR Insights
Percentage of respondents indicating the investment priority of various functional areas.
Source: IBM Fifth Biennial Global CEO Study, 2012
Capture d’écran 2015-10-29 à 07.06.02

Figure 3

Low Big Data Investments for HR and Finance
Percentage of total big data investments, by department.
Source: The Emerging Big Returns on Big Data, Tata Consulting Services, 2013
Capture d’écran 2015-10-29 à 07.06.21

IBM’s research suggests a trend that could help CFOs and CHROs argue their case, says Randy Hendricks, a leading expert on big data and managing partner, GBS North America, Public Sector at IBM. CEOs have recently been telling IBM researchers they want “globally integrated enterprises” and they understand the role technology plays. “Not long ago, regionalized models of organization were the way to go,” he says. “Now it is more about interconnectivity and global integration. The top two areas for achieving the globally integrated enterprise are the financial back-office and HR systems.”

Hendricks contends that technologies for financial management and human capital can help move an enterprise toward integration, with an ROI case around efficiencies and total costs. These newer tech- nologies typically offer more analytic capabilities. In turn, “analytics, and especially predictive modeling, can provide insights that help leaders optimize their organizations, reduce costs, and improve capabilities and outcomes,” he explains.

Here are some big data scenarios that blend finance and HR, and that could grow out of a technologically and globally integrated enterprise.

■ Analyze global payroll costs by addressing the geographic investment and expansion strategy by merging payroll-related costs such as base pay, benefits, employer taxes, and overtime with financial data such as facilities and overhead costs.

■ Determine the profitability of individual customers by using operational data about costs combined with financial data such as customer payments; then analyze the personnel factors that drive profit- ability. For example, the training and traits of the sales representatives that bring in the most profit- able customers over time.

■ Gain a complete picture of an organization’s staffing to determine how to revise recruitment budgets and pinpoint risks in product or service delivery.

■ Use advanced visualization techniques on geospatial data overlaid with demographic data to help determine market entry strategies and distribution strategies as well as the staffing requirements.

■ Analyze the impact of wellness programs on employees, gauging factors such as morale, productiv- ity, and overall savings on health care.

Overcoming Data Silos

In the Tata survey, respondents rated “getting business units to share information across organizational silos” as “the biggest challenge to getting business value from big data efforts” across the enterprise. figure 4 Cross-silo problems are often cited as issues for finance and HR, but clearly are not unique to them. And creative thinking can lead to workarounds while HR and finance await more investment in data marts.

As one HR executive explains, some business units in the company keep their data very close and no one has actually asked to use it before. HR would like to use sales and other financial metrics from the units in some of its analyses but has no access. Finance managers and professionals, however, almost always have access to sales and purchasing metrics but rarely have an in-depth view of HR data—typically finance has aggregate data only on payroll and benefit costs. In a big data world, though, HR executives and CFOs each need to have robust access to each other’s data sets so they can optimize working together.

Within HR there is also a silo issue that finance generally does not face and that business executives might not fully appreciate. HR organizations have a proliferation of systems—payroll, HR information system (HRIS), recruiting, performance management, learning management, and other functions. Rare is the situation where these systems all interconnect.

Figure 4

Top Ten Challenges in Getting Business Value from Big Data
Rating based on a scale of 1–5, with the higher number indicating bigger challenge.
Source: The Emerging Big Returns on Big Data, Tata Consulting Services, 2013
Capture d’écran 2015-10-29 à 07.06.40

Many initial big data HR projects focus on extracting desired information from these systems into a data mart. Finance departments found themselves in a similar position many years ago when they began to adopt BI and data mart solutions.

Sales databases, customer relationship management systems, and call center data outside HR are among the many rich sources for advanced workforce analysis. Gaining access to the data in these silos is also a hurdle, especially in companies that are decentralized or grew through acquisition.

Predicting Sales Associates Success

The value of interconnecting silos so HR and finance can take advantage of the big data opportunities is especially high for retailers. At Bon-Ton Stores, Inc., with more than 270 department stores in 23 states and about 27,000 employees, sales associates’ performance is deemed strategic, according to Denise M. Domian, senior vice president for HR. As a result, she says, “although other departments are very cau- tious and sensitive to how we use data, HR has never had opposition to anything we wanted to do with data to improve associates’ performance.” For example: An early predictive project with data from HR and the individual stores got the attention of business leaders.

Cosmetics sales associates had one of the highest turnover rates in the company. Intuitively, it made no sense: The company and cosmetics vendors provide these 3,600 associates with more training, knowl- edge, and tools to do their jobs than any other positions receive. As one of the few sales positions to earn commissions, the cosmetics associates can make more money than most. “You’d think the turnover would be lower,” Domian says.

Getting top management to approve investments in big data and its related investments (training, etc.)

Putting analysis of big data in a presentable form for making decisions (e.g., visualization/visual models)

Finding the optimal way to organize big data activities in our company

Determining what to do with the insights that are created from big data

Understanding where in the company we should focus our big data investments

With the help of a consulting firm and data provided by the CFO and other business leaders, the HR staff studied a sample of their own cosmetics associates to determine the skills, knowledge, and attitudes predictive of longer tenure and betters sales performance. Key data came from Bon-Ton’s productivity system. Developed by the HRIS staff, it calculates commissions, using sales data from the point of sales system and hours worked by employee from payroll. A key metric used in the study was sales per hour.

The project team mined the data, conducted a survey of a statistically valid sample of their own cosmetics associates, and correlated the findings. Based on that analysis, they rolled out a custom pre-employment assessment.

The candidate test focuses on situational judgment, problem solving, and cognitive ability, including math and logic. That’s because cognitive ability proved to be the trait that correlates most closely with lower turnover and better sales.

This finding surprised Domian. “Before, I would have said friendly, good customer service, and someone really into the beauty environment. Those were traits we targeted in hires. Now, she still must be like that, but we also found the most successful and those with the most tenure were problem solvers. They have to take information from the customer about what she wants and needs and solve that problem. This was our ‘aha’ moment.”

The bottom line on the big data project for Bon-Ton: Turnover rate improved and—unexpectedly—so did the sales per hour.


Marketing, customer service, and product development have received the lion’s share of attention and investment for big data because they are, in fact, doing more than others, and the results appear to busi- ness leaders as more tangible. With less fanfare HR and finance, often in collaboration, are also starting to use big data.

In HR, many are still laying the foundation for the future: standardizing data and definitions, improving historical reporting, automating metrics, adopting new tools, and creating data marts. Some are captur- ing and storing unstructured information they can’t easily analyze today in anticipation of using it later.

About half of the HR organizations adopting quantitative methods are still in this first phase, according to Ranjan Dutta, director of the metrics and predictive analytics practice at PwC Saratoga. “Most HR organi- zations had not gotten very far with the basics when the big data deluge began.”

Even if an HR organization is not doing analytics, Dutta applauds any effort to collect data. When an orga- nization is ready for predictive modeling it “will need enough history to build the most suitable model.”

It will take more to win C-suite support. It is imperative to focus on solving business problems. “When you look at the different pieces of big data there are so many components,” says Bon-Ton’s Domian. “It is a matter of breaking down those that will drive your business most.”

At Juniper, Rice has the full support of his CEO and a strong relationship with his CFO. Even so, he con- stantly educates business unit leaders in careful language. “I only talk about the business challenges that I see and how HR can help move the company forward. I never say ‘big data’ or ‘analytics.’ My objective is to integrate disparate insights from data within the context of our business challenges to support the lead- ership team in making informed and thoughtful decisions. It’s all about solving specific business issues, with my role being the broker in charge of bridging data and insights.”

At Spectrum, now that she and the finance department have waded into the data deluge, McCarthy is clearly making progress with big data: “Predictive modeling is our end goal. It will be a long process to get there.” The hurdles are lack of time, resources, and knowledge, but they can be overcome with effort. “There will also be a large change management effort required to move from reactionary metrics to pre- dictive and transformational big data,” she adds.


The findings in this report on big data are based on a combination of qualitative primary sources and both qualitative and quantitative secondary sources.

The primary sources included interviews with five HR practitioners and five third-party analysts and consultants.


• Denise M. Domian, Senior Vice President for HR, Bon-Ton Stores, Inc.
• Mindy Geisser, Vice President for Global HR, Colliers International Property Consultants, Inc. • Adria McCarthy, Director of HR Systems and Technology, Spectrum Brands, Inc.
• Steven Rice, Executive Vice President for HR, Juniper Networks, Inc.
• A senior HR manager at a global software company who requested anonymity


  • Dr. John W. Boudreau, Professor at the University of Southern California and Research Director of the USC Center for Effective Organizations
  • Ranjan Dutta, Director of the Metrics and Predictive Analytics Practice at PwC Saratoga
  • Randy Hendricks, Managing Partner, GBS North America, Public Sector, IBM
  • David Hom, a Principal at Deloitte Consulting
  • Ian Ziskin, Former Chief HR Officer (CHRO) at Northrop Grumman Corp. and President of Excel Group, a talent management consulting firmSecondary sources included various trade and business press articles, web sites, and reports from consulting firms in HR and in information technology management.

Sponsor’s Perspective


As the big data revolution continues to gain momentum, organizations face troublesome questions: Why haven’t more HR and finance teams tapped into its value before now? And why—even though research strongly suggests CEOs are ready to embrace big data in human capital decisions—do HR and finance still only receive an anemic 12.7% of the big data investment budget?

In no small part, the hype surrounding big data is to blame. While those in marketing, sales, and product development have more immediately recognizable uses for big data, many in HR and finance are still sorting through the jargon to determine how big data can work for their organizations. Pair this with the difficulty business leaders have in explaining the dollars and cents value to their C-suite and it’s no wonder they are playing catch up.

But, as progressive business leaders are discovering, big data will be a game changer in HR and finance. It will allow these organizations to reach beyond their data to more value-focused insights that will drive the business. As Adria McCarthy, director of HR systems and technology at Spectrum Brands, highlights, “Big data in HR will allow us to move beyond simple headcount and be more predictive than reactionary. Predictive modeling is the Holy Grail of reporting and analytics.”

To be effective, however, big data analytics must continue to answer real business problems.

At Workday, we believe businesses work more effectively when real-time insights are made available alongside the transactions being conducted. We’ve always approached our applications this way. However, we don’t believe insight should stop at the boundaries of the application. Big data must be treated as an equal citizen of the application, enabling users to gain broader, deeper insights regardless of the source, size, or type of data. That’s the power of big data analytics.

As Dan Beck, vice president of product management, financials and analytics at Workday, explains, “Big data is the promise of answering your business questions more effectively, more expeditiously with data.”

In finance, that means access to predictive insights that go far beyond the traditional income statement, including the ability to do competitive benchmarking, gain important insights into sales revenue attainment and revenue performance, and get a complete view of the factors impacting customer profitability by measuring critical financial metrics.

In human resources, it means diving deeper than standard metrics and benchmarks to tap into future-looking modeling to understand such key business drivers as workforce performance, hiring, development, and retention.

Big data is revolutionizing the way organizations answer their most pressing business questions. As Spectrum Brands and our other customer design partners have already discovered, if CEOs, CFOs, CIOs, and CHROs want to drive their businesses forward, they will need to quickly recognize the enormous value of big data for HR and finance and start investing in it accordingly.

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