2015 global human capital trends

HR and people analytics

Stuck in neutral

February 27, 2015

Still too few organizations are actively implementing HR analytics capabilities to address complex business and talent needs.

Among all the challenges we studied this year, people analytics presented the second-biggest overall capability gap for organizations, trailing only the need to build better leadership. (See figure 1 for capability gaps across regions and selected countries).


Why is this issue so prominent? Today, as many companies prove the power of analytics, a new race is under way to gain a competitive advantage by understanding all elements of the workforce.

DUP_1136_FeaturedGoogle uses analytics to gain insights into the impact of every interview and source of hire.3 Many companies, including Pfizer, AOL, and Facebook, now analyze the factors that correlate with high-performer retention. BP uses analytics to evaluate its training. SAB Miller uses analytics to drive high quality standards across a variety of programs worldwide.

Despite these and other high-profile uses of analytics, our survey confirms that most organizations have been slow to get started. Respondents showed little change in their ratings of their analytics capabilities since last year, and more than half of our respondents rate their organizations weak at scorecarding (figure 2).


Organizations are still new to this discipline, and many suffer from poor data quality, lack of skills, and a weak business case for change.4 While people analytics programs can deliver a high ROI, HR leaders have difficulty building an integrated plan.5 And more than 80 percent of HR professionals score themselves low in their ability to analyze—a troubling fact in an increasingly data-driven field.

As HR analytics teams struggle to build this capability, vendors are starting to fill the gap. Today, nearly every HR software vendor is eager to sell packaged predictive analytics tools, often built right into their talent and HR management software.6

But buying more data-driven HR and talent management software is just the first step—it will take several years for businesses to fully absorb this technology. Companies with leading capabilities in HR and people analytics have been building these capabilities for three years or more.

Where can an organization best apply analytics to improve talent management? Some possible areas include:

  • Understanding and predicting retention: With retention and engagement now becoming a CEO-level issue, understanding why people leave a company has become a top priority. One vendor we know of has become so sophisticated at this analysis that it can predict retention within weeks, simply based on data available from an individual’s behavior on social media. This type of data-driven insight has become a hot commodity in Silicon Valley’s new race to attract and retain top software engineers.
  • Boosting employee engagement: While changing behavior among managers often proves harder than simply uncovering facts, many companies are using analytics to identify ways to increase engagement and/or boost retention. One company, for instance, found that its compensation was too evenly distributed, pleasing mid-level performers but leading high achievers to depart for greener pastures.
  • Expanding the sources of talent and improving the quality of hires: After years of forcing job candidates to endure endless rounds of interviews and tests, Google used data to discover that, after the fourth interview, every following interview is largely a waste of time.7 Not only did this discovery streamline recruiting, it also helped the company understand what management factors led to the best job performance. Based on insights from its “people science” work, Google wrote its manifesto on leadership.8
  • Profiling high performers in sales and customer service: Companies such as Oracle and ADP analyze sales performance based on talent characteristics.9 They can now better decide who to hire, how to set quotas, and who should become a sales leader.

Beyond those more common applications, people analytics are beginning to be used in more advanced ways. Many financial services firms, for instance, have turned to analytics to understand and predict ethics and compliance problems. As new government regulations place greater burdens on financial institutions to prevent misconduct, a tool that accurately forecasts which employees are most at risk of committing ethical transgressions offers a critical insight.

Analytics reaches into other exciting areas as well, such as how people learn and progress in their career. Learning management systems vendors now offer new tools that use data to “recommend learning” in the same way as Amazon and Netflix recommend books and movies.

As people analytics takes hold, data-driven decisions will become a common theme across all parts of HR.

The common theme connecting all these applications is simple: They address business issues, not merely HR issues. Connecting these tools to business needs helps build the case for investment in and deployment of analytics.

Companies can move faster on analytics by considering a cross-disciplinary approach. One company created a cross-functional team called “HR Intralytics” to model ways in which the efficiency and effectiveness of its people services could be improved. This team worked with finance and business operations to visualize data across processes, defining the business benefits of improvements to various parts of HR. The output was so compelling to the board of directors that it approved funding for a major transformation—including a dedicated people analytics center of excellence.

As people analytics takes hold, data-driven decisions will become a common theme across all parts of HR. Organizations should invest aggressively in this new discipline, link it to the rest of the business, and reskill their teams to bring data to work in every major people-related decision.

Lessons from the front lines

HR leaders at ConAgra Foods are using analytics to calculate and report the total cost of its workforce rather than leaving this important task solely to the finance function. Until recently, the company has struggled to collect accurate data about its workforce. Information was spread across the organization, making it difficult to reconcile. Analytics solutions allow the company to gain better insights into employee data, providing current and projected headcount as well as total workforce costs.

Following a major acquisition in 2012, business leadership gave HR a mandate both to acquire the best talent in the business and to understand the true cost of its talent. HR’s analytics team began searching for a solution that would deliver extensive self-service analytics capabilities to stakeholders as well as provide accurate workforce costs and support future planning scenarios.

Partnering with finance, the team mapped all available data and processes. The HR system held employee-specific data on salary and benefits, while an ERP system from finance provided aggregate cost data. Using a cloud-based system, the team aggregated all workforce cost data. To calculate the total cost of the workforce, the team developed a taxonomy of the different elements going into this figure, including direct compensation, benefits, employee costs for labor, and workforce overhead, as well as the subcategories under each.

After collecting and aggregating the data, ConAgra can now visualize these different elements in a single, interactive application displaying a wide range of metrics, including actual and planned headcount and actual versus planned workforce costs. HR and finance professionals are now able to analyze and optimize investments across a wider range of workforce costs. The company can now see the impact of spending on a minute level and understand how workforce costs impact its financial plan. For instance, the company can model workforce costs at two different locations, or better understand the cost of entering new markets. In the past, these calculations would have been highly time-consuming and error-prone to compute by hand.10

Where companies can start

  • Build the right team and show the return on investment: An analytics team should be multi-disciplinary, combining employees with business knowledge and those with technical skills. Since it is hard to find people with a combination of all the necessary skills, the most effective approach may be to build a highly diverse team.11 Employees with physics and engineering backgrounds and industrial-organizational psychologists are often good candidates for the team. Pair them with a talent expert who understands the people dimension. Add team members with skills in communication, visualization, and consulting to help drive value, and remember to quantify the value that better decision making is bringing to the organization.12
  • Start with the tools you have: Organizations do not need to purchase new software to start the transformation. Using the analytics tools built into spreadsheets is a good place to start, allowing organizations to put existing capabilities to work to analyze data that are too often underused. Do not let the perfect be the enemy of the good; it is better to do analytics based on less-than-perfect data than to do no analytics whatsoever.
  • Partner with IT: Data quality is often a problem when it comes to the people side of the business. HR teams must enlist the support of IT early to help build a program to clean up, rationalize, and continuously monitor data quality.
  • Use analytics on the HR organization to show analytics’ potential: Assimilate data on the demand and supply profile of HR services, and apply the principles of modeling, forecasting, and visualization to illustrate the dynamics of the function itself. Look for areas in the HR operating model that can be improved, quantify the potential impact, and then design embedded analytics as part of the new landscape.
  • Focus on immediate business needs: Analytics is a business priority, not merely an HR tool. When analytics connects directly to business issues, the argument for investment becomes more powerful to the organization as a whole. Start with a well-known problem—be it turnover, sales productivity, or customer service quality—and start studying the people factors that drive outcomes. Sophistication comes with time and investment, and showing early results will help sell the program to business leaders. More integrated tools are now available, and if early results drive value, companies can justify major investments.
  • Leverage embedded analytics by upgrading technology platforms: More than 70 percent of our respondents are upgrading or have recently upgraded their core HR systems with new cloud platforms. The business case for these systems should include a hard look at the potential benefits from robust people analytics. Because reducing turnover, improving sales productivity, and increasing the quality of hires all have a tremendously high ROI, analytics often represents a strong business case to justify modernizing the HR infrastructure.

Originally published at Deloitte Insights

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