h report - diversity best practices...best practices in leveraging internal diversity data...

research report

THE PREEMINENT ORGANIZATION FOR DIVERSITY THOUGHT LEADERS

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Published By: Diversity Best Practices 2 Park Avenue, 10th Floor New York, NY 10016 DiversityBestPractices.com Copyright © 2014 by Diversity Best Practices. All rights reserved.

Best Practices in Leveraging Internal Diversity Data

January 2014

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Best Practices in Leveraging Internal Diversity Data

Introduction

There are around 160 million workers in the United States and, for most companies, payroll is

40 percent or more of total revenue. Needless to say, a company’s workforce is one of its most

valuable resources ― if not the most valuable. But what do companies really know about the

people they entrust to keep the business running day to day? How well do organizations truly

understand what drives employee performance? What motivates employees, beside their pay

check, to come to work every day? How can a company know why one employee outperforms

his or her peers? Why do certain leaders thrive and others fail? How can a company know if a

job candidate will adapt to the company’s culture or perform well in its business model?

For a growing number of companies, the answer is a crystal ball called Predictive Analytics, or

“Big Data.” While for most companies, the vast majority of hiring, management, promotion, and

rewards decisions are still made with a combination of “the gut,” personal experience, and

corporate belief systems, some companies are using data to help predict people outcomes in

the same way they have for years used data to predict business outcomes.

Given rapid advances in online technology, data accumulation about everyone and everything

has increased exponentially. An astonishing 90 percent of the data in the world today was

created in only the past two years. Data comes in multiple shapes and sizes — it can be

structured and unstructured — but putting various types of data together can yield new,

powerful insights.1

What types of data is collected?

While fascinating in and of itself, data is most useful when used to predict future behaviors,

patterns, and trends based on previous behaviors. Companies are swimming in all kinds of

employee-related data; for the last 30 years, companies have captured demographic

information, performance information, educational history, job location, and many other factors

about their employees. Most companies are not strategically leveraging this data. But some are.

Companies are collecting data on a diverse range of metrics, including:


• What makes employee feel engaged with their job and with the company?

• What would lead an employee to seek employment elsewhere?

• Who is most likely to retire early and why?

• What personal attributes and attitudes would predict employee success in the

company’s workforce and culture?

• What company attributes better attract the people who are most likely to succeed in

and organization?

• How employees feel about their manager and if that influences their decision to stay

with the company long-term.

• Who are the most successful leaders and why are some being developed and others

are not?

• Where are the talent gaps in the organization and what gaps can be anticipated in

coming years?

For example, Hewlett Packard (HP) compiles large quantities of performance and related data

about HP workers. They looked at salaries, raises, performance ratings, and job rotations,

adding, for each individual, whether the person had quit. The result? A data pool that, if tapped

effectively, can predict the various factors that make someone most likely to quit their job.

How are companies using the data they collect?

There are definitive ways in which using big data can create value internally for a company. Big

data can unlock significant value by making information transparent and usable at much higher

frequency. As organizations create and store more transactional data in digital form, they can

collect more accurate and detailed performance information on such metrics as they number of

sick days employees take, therefore expose variability and boost performance.

Leading companies are using data collection and analysis to conduct controlled experiments to

make better management decisions; others are using data for basic low-frequency forecasting

to high-frequency “nowcasting” to adjust their business levers just in time. Sophisticated

analytics also can substantially improve decision-making.


Used correctly, data can support many of a business’s HR functions, including retention,

development of a leadership pipeline, analysis of leadership and talent gaps, and creating a

general talent pipeline. Finally, data can help with related functions, such as monitoring and

predicting on-the-job injuries, or conducting loss analyses to determine causes of theft by

employees. It can help identify health plan needs.2

Diversity and inclusion practitioners also can use predictive analytics to shape policies that

work, to drive a highly inclusive workforce, and to better illustrate the bottom-line impact of

diversity initiatives.

Teri Morse, vice president of recruitment for Xerox Services oversees hiring for the company’s

150 U.S. call and customer-care centers (about 45,000 workers), used to fill these positions

through interviews and a basic assessments conducted in the office, such as a typing test.

Hiring managers would typically look for work experience in a similar role, but also would use

their gut feeling about whether or not a candidate was right for the role. But, in 2010, Xerox

switched to an online evaluation that incorporates personality testing, cognitive-skill

assessment, and multiple-choice questions about how the applicant would handle different on-

the-job situations.

An algorithm is used to analyze the responses, along with facts from the candidate’s application.

The result is a color-coded rating: red (poor candidate), yellow (middling), or green (hire). The

candidates that score best in Xerox’s analytics tend to be creative, not “overly” inquisitive, and

participate in at least one social media network, among other factors. Interestingly, relevant

previous work experience, a criteria Xerox had screened for in the past, turns out to have no

bearing on employee productivity or retention. Instead, the employee’s distance between home

and work figures strongly in engagement and retention.

When Xerox started using the score in its hiring decisions, the quality of its hires immediately

improved, the attrition rate fell by 20 percent, and the number of promotions rose. Xerox still

interviews all candidates in person before deciding to hire them, but some hiring managers now

don’t want to interview; they just want to hire the people with the highest scores. 3


In the transient world of call center operations involving thousands of people, Big Data is being

used to make predictions about the percentage of workers likely to leave in a month. For

example, Dell has used predictive training to increase the tenure of new call center agents so

they can deliver a better customer experience to the callers.4

Defense Acquisition University, which trains military and civilian professionals in defense

acquisition, logistics and technology, analyzes internal data to determine the least expensive

locations for classroom training. The university looks at variables such as room cost, instructor

salary and travel, expected attendance, and students’ travel costs to the venue.

Juniper Networks, which develops network infrastructure products, uses the career social

networking site LinkedIn to track and analyze the skills, knowledge, experience and career

paths of employees, former employees and potential employees.

Before FedEx Corp. acquires a company, its HR department analyzes employee data from the

company to be acquired, such as employee engagement survey results. That information is

then compared with FedEx data to discern the cultural fit, giving management another data

point before making a decision. 5

ConAgra Foods has recently begun to use some analytics programs in its HR practice with

hopes that the data will help it better plan and improve business outcomes. ConAgra wants to

know its employees as well as it knows its customers and its HR team already has developed a

number of safeguards for what types of employee data it will and will not collect, as well as tools

to assess the impact, both positive and negative, of the project before proceeding. 6

After an acquisition, energy conglomerate Black Hills Corp. doubled its workforce to about 2,000

employees. Like many energy companies, the combination of an aging workforce, the need for

specialized skills, and a lengthy ramp-up time for new employees created a talent risk.

Dire forecasts showed that, within five years, the firm could lose 8,063 years of experience from

its workforce due to attrition and retirement. To prevent this massive turnover, the company

used workforce analytics to calculate how many employees would retire per year, the types of

workers needed to replace them, and where those new hires would come from. The result was


a workforce planning summit that categorized and prioritized 89 action plans designed to

address the potential talent shortage. 7

Who sees the data?

HR practitioners are not sharing these analytics with many people inside or outside of the

organization. At HP, for example, the company’s “Flight Risk” analytics, which predict which

employees are likely to leave and why, are guarded like the Holy Grail.

HP’s analytics team has created a report delivery system that ensures that only a select group

of high-level managers can access individual employees’ scores. This select group has been

trained to understand the data, its ramifications and proper uses, as well as any factors about

the employee that may have contributed to their score. This group can also access only the

scores of employees who report under them, further narrowing the number of eyes on the

information.

Furthermore, the reports do not list employee names or other easily identifiable information

about them. An example of the tight hold on this information: Within HP’s internal Global

Business Services (GBS) division, of the 300-person sales compensation team, only three

managers are authorized to see the reports.8

At Walmart, diversity leaders meet with business leaders quarterly to review diversity and

inclusion goals, as well as to share current workforce metrics that reflect employment practices

in their organization. The data provides demographic analysis regarding new hires, promotion,

retention, and representation at various levels. Insights gleaned from the data highlight both

strengths and opportunities—in an objective fashion. By reviewing this data with the diversity

team, business leaders can make well-informed decisions to help meet their diversity and

inclusion goals.

According to the senior vice president of people operations at Google, “when we look at any

data related to our people, we treat the data with great respect. Typically, we give people an

option to participate in anything either confidentially or anonymously. The lesson for anyone

looking at this space is that you need to construct this really powerful tent of trust in the people

gathering the data and how they use it.”9


Predictive Analytics in Insurance HR

The use of Predictive Analytics is an insurance industry best practice, to target potential clients,

determine accurate pricing, and identify potentially fraudulent claims. Several factors have

increased their use throughout the industry, including technology advances, data availability, the

insurer’s desire to grow in slow markets, and the insurer’s efforts to identify and exploit its

advantages over its competition. 10

An example in a white paper by IDC, a global market intelligence firm, cites an unnamed auto

insurance company that used predictive analytics to reengineer its claims department. The

ability to identify and score initial claims using predictive analytics software allowed the

company to move the initial assessment of loss processes from the call center to first-line claim

adjusters. As a result, 22 percent of initial claims were moved from the costly, labor-intensive

process that required experienced adjusters to evaluate property damage on site. Instead, the

company created a new analytics team that added labor costs, but these analysts now support

the rest of the adjusters. The company also improved its fraud detection and prevention

practices.

But there was another benefit on the HR side that could eventually help the company realize

return on investment: The company’s internal reorganization resulted in more specialized work

groups. The company set up a group of adjusters that focus on organized fraud by criminal

groups, a time- and resource-intensive job. Predictive analytics has helped the company create

new career paths for new and existing employees and increase employee satisfaction.

According to a senior VP of the company, "On the one hand, these new solutions provided us

with more automation. On the other hand, people are now dealing with more exceptions — a

higher value-added and more satisfying work." 11

Conclusion

For companies looking to leverage predictive analytics to enhance the workforce, one strategy

might be to start small: Choose a particular workforce challenge, whether it is recruitment,

promotion, representation or something else. See if data is available to measure current state


and future progress. From there, build the project and leverage the data to build your business

case, securing needed approvals and funding, and prove results.

While the examples here are illustrative of the vast diversity of information that can be gleaned

from predictive analytics, companies should be mindful of the limits of this data. All data comes

with a margin of error. And only humans can work through that error. And, it’s only through trial

and error that accurate models are developed. But for a company willing to take a look into the

“crystal ball” the advantage gained can be significant.

Endnotes

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""1 “What is big data?” IBM.com. Link: http://www-01.ibm.com/software/data/bigdata/ 2 Bersin, Josh, “Big Data in Human Resources: Talent Analytics Comes of Age,” Forbes, February 17, 2013. Link: http://www.forbes.com/sites/joshbersin/2013/02/17/bigdata-in-human-resources-talent-analytics-comes-of-age/2/ 3 Peck, Don, “They’re Watching You at Work,” The Atlantic, December 2013 http://www.theatlantic.com/magazine/archive/2013/12/theyre-watching-you-at-work/354681/ 4 Bhaduri , Abhijit and Basu, Atanu, “Predictive Human Resources: Can Math improve HR mandates in an organization?,” Informs website, October 2010 https://www.informs.org/ORMS-Today/Public-Articles/October-Volume-37-Number-5/Predictive-human-resources 5 Roberts, Bill, “The Benefits of Big Data,” Society for Human Resource Management website, Oct. 1, 2013 http://www.shrm.org/Publications/hrmagazine/EditorialContent/2013/1013/Pages/1013-big-data.aspx 6 “Human Resources Tentatively Tries Predictive Analytics,” InformationWeek website, Nov. 20, 2013 http://www.informationweek.com/strategic-cio/team-building-and-staffing/human-resources-tentatively-tries-predictive-analytics/d/d-id/1112697 7 Collins, Mick, “Change Your Company with Better HR Analytics,” Harvard Business review Blog Network, Dec. 11, 2013 http://blogs.hbr.org/2013/12/change-your-company-with-better-hr-analytics/ 8"Siegel, Eric, Predictive Analytics: The Power To Predict Who Will Click, Buy, Lie, Or Die, John Wiley & Sons, Hoboken, New Jersey, 2013"9 Bryant, Adam, “In Head-Hunting, Big Data May Not Be Such A Big Deal,” New York Times, June 19, 2013. Link: http://mobile.nytimes.com/2013/06/20/business/in-head-hunting-big-data-may-not-be-such-a-big-deal.html 10 Nyce, Charles, “Predictive Analytics White Paper,” American Institute for CPCU/Insurance Institute of America, 2007, http://www.theinstitutes.org/doc/predictivemodelingwhitepaper.pdf 11 Vesset, Dan and Morris, Henry D, The Business Value of Predictive Analytics, June 2011 http://www.spss.com.ar/MKT/Promos/2012/0612_PA/0612_businessvalue_PA.pdf

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