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