The Viability of Data Analytics in Human Resources

Jonathan Gunter

SCS 2943 – 010

December 14th, 2015

Introduction – The Evolution of Human Resources

Over the last century, the model for business operations was predicated almost entirely

on sales and production. Workers were seen as expendable components of the overall

production process, carrying out mechanical tasks and having little or no autonomy in

performing their duties. Due to these working conditions the human resources agenda was

really an afterthought, being more commonly referred to as the ‘Personnel Department’ as

opposed to ‘Human Resources’. This division would function primarily as the administrative arm

of the business where responsibilities included payroll, benefits, and policy writing.

In the last thirty years however, technology has radically shifted the nature of work. The

emergence of the Internet in the 1990s led to faster and easier access to information, greater

global connectivity, automation of many administrative or tedious tasks, and self-service

capabilities. Economic drivers shifted from production-based to knowledge-based, and

companies placed a greater emphasis on the intangible skills that prospective candidates

possessed. This resulted in the “war for talent” as competition for top talent increased

exponentially. This struggle still carries through even today as the largest age demographic, the

“Baby Boomers”, progresses towards retirement. This demographic age group comprises over a

third of the current working population1, and as they phase out of the workforce the incoming

population of post-baby-boom workers will not be large enough to compensate for their

departure. As a result, there will be a dearth of critical talents and skills that companies will be

fighting for tooth-and-nail.

1 Population by sex and age group, Statistics Canada (Sept 2015). www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/demo10a-eng.htm

It is due to the contributing factors mentioned above that progressive companies are

beginning to embrace Human Resources as a strategic business partner. Pressures have begun

to mount on the HR function, to pivot away from the traditional activities of facilitating new

employee orientations and writing handbooks and policies to instead be more active

contributors to business strategy; finding efficiencies through people in order to push the

bottom line. In facing new pressures, HR leaders need to adopt new ways of thinking. As

mentioned by Deloitte HCM Principal Josh Bersin, some executives will often make critical

business decisions based solely on experience or intuition2. Instead of relying on personal

judgment, a greater emphasis needs to be placed on tapping into the ever-expanding volumes

of available data. However, the issue with this approach is that few companies know how to

properly implement an analytics initiative.

Potential Barriers to the Viability of HR Analytics

Insufficient Understanding of Data & Analytics

One of the primary barriers with HR analytics is that many HR departments perceive

themselves to already be analytical simply by producing dashboards or scorecards that depict

headcount totals or turnover rates. While metrics on the efficiency of HR activities (i.e. time to

hire) can be useful in getting a sense of the current state of affairs, it provides marginal value or

insight for business leaders and doesn’t mean much to operations outside HR3. In a study by

Bersin by Deloitte, 86% of organizations focus their efforts on ‘reporting’, choosing to provide a

description of past activities and not perform any analytical modeling to predict future positive 2 Developing Advanced Talent Analytics: Why It Matters to CFOs, Bersin by Deloitte, Wall Street Journal (Sept 2015)3 Maximizing the Impact and Effectiveness of HR Analytics to Drive Business Outcomes, Scott Mondore, Shane Douthitt and Marisa Carson, Strategic Management Decisions, Vol. 34, Iss. 2 (2011)

or negative outcomes4. Of the 14% of respondents that were deemed to be “mature” analytic

organizations, 80% were found to have improved recruitment practices due to their effort in

measurement and analytics5. Further to that, Deloitte discovered through their studies that

analytic organizations were twice as likely to believe they were consistently selecting the right

candidates and delivering a strong leadership pipeline, and three times as likely to believe they

were efficiently operating HR6. Lastly, data-driven HR organizations were found to generate 30%

higher stock returns than the S&P 500 over the last three years7.

For other HR departments that understand the need to transition from ‘reporting’ to

‘analytics’, most either don’t know where to start or apply analytics resources to the wrong HR

activities. To gain some momentum, HR can obtain guidance from their colleagues in Finance to

get a sense of how to progress their analytic capability. As mentioned by Human Capital

strategist Jac Fitz-Enz, finance professionals have moved up in recent years from the perception

of simply being accountants to supplying valid and valued strategic analytics to business

executives8. Finance became a more valued function to the business as financial capital grew to

be more important than physical capital in a service-based economy. As a result, the Finance

function evolved to develop generally accepted accounting principles (GAAP) that were

accepted across industries. These principles allowed senior Finance professionals and leaders

to develop valued analytics specific to their own businesses9. The Human Resources function is

4 High-Impact Talent Analytics: Building a World-Class HR Measurement and Analytics Function, Bersin by Deloitte, (Sept 2013).5 Developing Advanced Talent Analytics: Why It Matters to CFOs, Bersin by Deloitte, Wall Street Journal (Sept 2015)6 HR Technology 2015 – Ten Disruptions: Ignore Them At Your Own Peril, Josh Bersin (Dec 2014), Slide: “Analytics Drives Huge ROI”.7 Ibid.8 The ROI of Human Capital, Jac Fitz-Enz (2011), Ch. 1 (Human Leverage), pg. 13.9 Ibid.

now in a similar situation, where human capital is coming to the forefront of business strategy.

Since most CFOs have an extensive view across the organization, they can provide valuable

perspective on deficient areas of the business and where HR should focus their analytic

efforts10.

Insufficient Statistical Skills & Tools in the HR Department

A misconception is that even if HR leaders are able to pinpoint areas of opportunity for

applying analytics, they suffer setbacks due to gaps in statistical talents and tools available

within the HR department. A study by the Aberdeen Group revealed that the biggest problem

with workforce analytics is having people who know how to properly analyze numbers. The

study reported that 44% of companies reported a lack of people resources that understand how

to interpret and analyze data, and transform the data into actionable insights11. However this

should not become an obstacle when attempting to implement an analytics initiative, since

statistical skills can be captured from different sources. Companies can search the external

market for statisticians or actuaries who specialize in data modeling, and couple their skill set

with the domain knowledge possessed by HR professionals. The two sides can work in tandem

in order to build a data map to understand key performance indicators (KPIs) in a human capital

context, what sources of data are available and what data attributes should be pulled in to the

analytic study. If conducting an analytical search externally proves too costly, companies can

leverage analytical talent within the company, such as financial analysts, to provide guidance on

how to properly manipulate and analyze sets of data. Finance will often possess the sharpest 10 Developing Advanced Talent Analytics: Why It Matters to CFOs, Bersin by Deloitte, Wall Street Journal (Sept 2015).11 Four Most Significant Barriers to Producing Workforce Analytics, Michael Moon, HCM Essentials – Aberdeen Group (Oct 2015)

analytical skills, and these professionals can assist HR in understanding how to link talent data

with revenue, profitability, and other operational business data12.

Another commonly perceived barrier is a lack of statistical tools and technology available

to properly analyze HR data sets. In the aforementioned study by the Aberdeen Group, nearly

35% of companies insist they don’t have tools or software to assist with analytics13. The truth of

the matter is that HR can perform data modeling at little to no cost, using a combination of

simple analytic techniques and open source statistical software. For example, correlational

analysis can be a useful tool for uncovering relationships between talent data attributes. The

downside however, is that uncovered relationships can merely be coincidental and lead to

invalid conclusions drawn from the data. Alternatively, a more advanced modeling technique

like multiple linear regressions can be used to measure multiple predictors simultaneously, and

then select the variables that have the strongest relationship with the outcome variable. This

technique can be more effective than correlational analysis, since it quantifies and prioritizes

the best individual predictors to an outcome14. The most complex but effective way to measure

multiple talent variables at once is through Structural Equations Modeling (SEM), which takes

into account multiple independent and dependent variables all at once to determine cause and

effect relationships. As opposed to correlational analysis, the cause and effect relationships that

can be uncovered from SEM results in much more conclusive evidence that is less likely to be

12 Developing Advanced Talent Analytics: Why It Matters to CFOs, Bersin by Deloitte, Wall Street Journal (Sept 2015).13 Four Most Significant Barriers to Producing Workforce Analytics, Michael Moon, HCM Essentials – Aberdeen Group (Oct 2015).14 Predictive Analytics for Human Resources, Jac Fitz-Enz & John R. Mattox II (2014), Ch. 6 (Predictive Analytics in Action), pg. 101.

dismissed by senior leadership when sharing findings15. Furthermore, talent data collection

brings a portion of measurement error with it. While correlation assumes that everything is

measured without error, which can be problematic, SEM can correct for measurement error

using reliability assessments16. The downfall of structural equations modeling is that it can be

very complex, and therefore requires specialized software to perform. Fortunately there are

modules available in SAS statistical software which can be used, or even the lavaan and “sem”

packages available in “R”, which is freely available statistical software17. The models that HR

builds however, will only be as effective as the data they possess.

Sourcing Data & Data Quality

Data quality is a critical component to analytics, especially for a function like HR that

already has tenuous ties to executive leadership. If analytical findings are produced from

inaccurate or messy data, senior leadership will dismiss the findings and HR will lose credibility

as a result. In any analytics study, a significant investment in time and effort should be

dedicated to data acquisition and cleansing. While it isn’t glamorous work, it is nonetheless

pivotal to achieving successful analytic results. Since a considerable amount of effort needs to

go into data cleansing efforts, it’s important to distinguish which data attributes are most critical

to the analytics exercise and start improving data quality on these particular variables18.

15 Maximizing the Impact and Effectiveness of HR Analytics to Drive Business Outcomes, Scott Mondore, Shane Douthitt and Marisa Carson, Strategic Management Decisions, Vol. 34, Iss. 2 (2011)16 Ibid.17 About SEM Software. http://www.structuralequations.com/software.html18 Developing Advanced Talent Analytics: Why It Matters to CFOs, Bersin by Deloitte, Wall Street Journal (Sept 2015).

Another important barrier to consider in relation to data quality is the number of

dissimilar data sources that need to be gleaned from for useful information. These sources will

often comprise different data dictionaries and will belong to different data owners or stewards

across different functions. Michael Moon of the Aberdeen Group would refer to these different

sources of data as “information silos”, where the data is static and housed in different parts of

the company19. Even for analytic initiatives that exclusively reside within HR, this can still prove

to be a cumbersome exercise due to different systems used for applicant tracking, payroll,

compensation and benefits, employee administration, and learning and development, just to

name a few. According to a Bersin by Deloitte study, about 1 and 3 organizations have ten or

more HR systems20. Data that does not flow freely though different channels of the company is

not being utilized to its full value, so it’s critical to have the appropriate systems development

resources available to install the necessary integration programming to open up the channels

between these silos21.

HR departments that can successfully navigate through the potential barriers mentioned

above offer themselves an opportunity to deliver true value to their clients in the business. The

next section will cover some specific case studies within HR, where data analytics has proven to

be an effective tool and has delivered compelling insights that could trigger transformative

action to business operations.

19 Four Most Significant Barriers to Producing Workforce Analytics, Michael Moon, HCM Essentials – Aberdeen Group (Oct 2015).20HR Technology 2015 – Ten Disruptions: Ignore Them At Your Own Peril, Josh Bersin (December 2014)21 Four Most Significant Barriers to Producing Workforce Analytics, Michael Moon, HCM Essentials – Aberdeen Group (Oct 2015).

Applications of HR Analytics

Recruitment & Selection Analytics

In a meta-analysis study written in the Harvard Business Review, researchers discovered

that human judgment can often have its pitfalls during the recruitment and selection process.

The authors argue that while experienced managers and executives are very effective at

identifying the core skills and competencies required for a specific role, their intuition fails them

in the actual selection decision since they place too much emphasis on inconsequential

factors22. The study discovered that algorithms were 25% more effective than human selection

decisions, outperforming humans in selecting above-average performers through variables such

as supervisor ratings (algorithms were 7% more effective), number of promotions (4% more

effective) and ability to learn from training (11% more effective)23. Another study by the

National Bureau of Economic Research reviewed over 300,000 hires from 15 different

companies, and compared the tenure of employees who had been hired based on the

algorithmic recommendations of a job test with that of candidates who had been selected by

humans24. The job test included a wide array of questions concerning technical and cognitive

abilities, personality traits, and cultural fit25. After running the answers through an algorithm,

the predictive model classified the candidates into three groupings: green for high potential

employees, yellow for medium potential and red for the lowest rated potential. The conclusion

was that the algorithm worked, and that green hires stayed at the job 12 days longer than

22 In Hiring, Algorithms Beat Instinct, David Klieger, Nathan Kuncel, & Deniz Ones, Harvard Business Review (May 2014).23 Ibid.24 Machines Are Better Than Humans at Hiring the Best Employees, Rebecca Greenfield, Bloomberg Business (Nov 2015).25 Ibid.

yellow hires, which stayed 17 days longer than red-classified hires26. The sentiment reached

based on the findings of these studies is that humans that make the selection decisions are

often overcome by a number of different biases, which leads them astray when attempting to

select the best candidate. The objective is not to replace recruiters with machines as there are

still important cues that humans can pick up on that machines cannot. However, HR

professionals and business line managers need to overcome their distrust of algorithms and

instead embrace it as a useful tool to supplement the hiring decision.

Retention Analytics

Employee retention is an area that presents one of the greatest cost-savings

opportunities within HR. When factoring in recruitment costs, training costs, and productivity

losses, it can cost companies and estimated average of six to nine months’ worth of salary to

replace an employee, and this cost increases the longer an employee stays27. However when

looking at a combination of employee attributes from internal HR systems and external data

that can be found on social networking sites, organizations have the potential to collect

extensive retention risk data on their employee base. This includes aggregated retention risk

profiles by employee, as well as identifying the underlying drivers of retention and attrition28.

In an example from Deloitte, their human capital consultants worked with a global

company in China to improve employee retention with their sales workforce. The initial belief

from middle management was that increasing compensation would improve retention rates.

However after conducting an analytics study, it was discovered that there was minimal 26 Ibid.27 Can an Algorithm Prove You won’t Quit Your Next Job? Rebecca Greenfield, Bloomberg Business (Nov 2015).28 HR Technology 2015 – Ten Disruptions: Ignore Them at Your Own Peril, Josh Bersin (December 2014).

correlation between compensation and turnover29. Instead Deloitte identified that time in role

and supervisor tenure were better indicators of turnover, and as a result recommended that

development opportunities, job rotations, and career discussions be held with employees that

were identified in the model as “high risk”30. The result was a significant drop in turnover over

the next six months, which allowed the company to reach their sales growth targets in the

region31.

Another company on the leading-edge of people analytics is Google, led by VP of HR

Laszlo Bock. Mr. Bock implemented what has been dubbed a “Three Thirds” structure to his HR

team, where one third of the team comes from a traditional HR background, one third has

backgrounds in business strategy consulting, and the last third comes from an analytical

background and possess Masters Degrees and PhDs in data and analytical science32. The

philosophy adopted by Bock’s HR team is a data-driven one, where every decision revolving

around people management is supported by algorithmic or data-based evidence33. Included in

their decision-making practices is a retention algorithm that allows Google to proactively and

successfully predict which employees are at greatest risk of leaving. This not only provides early

alert warnings to management on risks of attrition, but additionally permits the use of

personalized retention solutions by identifying which employee attributes carry the most weight

in the reason for leaving34.

29 Developing Advanced Talent Analytics: Why It Matters to CFOs, Bersin by Deloitte, Wall Street Journal (Sept 2015).30 Ibid.31 Ibid.32 Building a New Breed, Michael O’Brien, Human Resource Executive Online (Oct 2010).33 How Google Is Using People Analytics To Completely Reinvent HR, Dr. John Sullivan, ERE Media (Feb 2013).34 Ibid.

Workforce Management & Planning

In addition to their advanced retention analytics, Google has done an effective job of

using analytics to understand the core competencies and behaviours of great managers and

leaders. When Google initially started out, there was a common belief among leadership that

middle management was not critical to business operations. As a result of this belief, they

disposed of this line of management entirely. When they quickly realized that this belief was

false, they turned to data as a means of understanding manager effectiveness35. After analyzing

a substantial amount of internal performance data, they were able to prove that great managers

had a statistically significant influence on critical measures such as attrition, productivity, and

team performance36. Based on this analysis, Google took the next step of classifying “good

managers” and “struggling managers”, and conducted qualitative interviews of both groups.

Using text analysis they were able to identify common traits of good and struggling managers,

and use this information to build a semi-annual performance evaluation for their managers.

Included in this evaluation framework was an alert system that allowed Google to proactively

identify “good” and “struggling” managers, and provide remedies for struggling managers

through training and support37.

Another great example of using analytics to optimize workforce planning came from a

long-standing energy company managing its largest growth in headcount in its 130-year history.

Black Hills Corp. doubled its workforce to over two thousand employees after a significant

acquisition. In evaluating their workforce, Black Hills ascertained that a substantial portion of

35 Is HR Going to the Geeks?, Bernard Marr, LinkedIn (March 2015)36 Ibid.37 Ibid.

their workforce was aging and close to retirement. This created a significant risk for a talent

deficiency, as the company forecasted that they could lose over 8,000 years of experience in the

next five years38. To prevent the massive dearth in talent, Black Hills used workforce analytics to

calculate the number of expected retirements per year, the types of workers required to replace

them, and where to source those workers from in their recruitment activities. The result from

this aggregated analysis was a workforce planning summit that devised and prioritized nearly 90

action plans to address the shortage in talent39.

Conclusions and Analysis

There are countless other business case studies that can be drawn from to depict the

effective use of HR analytics. However the common link between these examples is one of the

core problems that continues to plague HR activities, which is that too much of people-based

decision-making is carried out with little or no data and excessive reliance on personal

experience and intuition. Other enabling or supporting functions in the business such as

Finance or Marketing actively use data and analytics in nearly all of their consulting activities

with the business. Employees are often a company’s greatest asset, typically representing

around 60 percent of corporate variable costs40. If this is the case, why is there so little evidence

based decision-making used when casting critical HR decisions? An article from Mondore,

Douthitt, and Carson best articulated the approach that HR leaders need to take to not only be

more analytical, but better positioned to be an effective partner to the business41:

38 Change Your Company with Better HR Analytics, Mick Collins, Harvard Business Review (Dec 2013).39 Ibid.40 How Google Is Using People Analytics To Completely Reinvent HR, Dr. John Sullivan, ERE Media (Feb 2013).41 Maximizing the Impact and Effectiveness of HR Analytics to Drive Business Outcomes, Scott Mondore, Shane Douthitt and Marisa Carson, Strategic Management Decisions, Vol. 34, Iss. 2 (2011)

1. Calculate the Return-on-Investment (ROI) in everything that they do.

2. Give evidence-based advice on how to drive the business from a people perspective.

3. Take accountability for a portion of the organization’s financial health.

4. Show results (HR effectiveness) and not just HR activity completion (HR efficiency).

5. Create an HR strategy that has direct impact on the bottom line.

In summary, the cliché of “HR earning its seat at the table” is overused and still dubious in many

cases. Effective business leaders in the C-suite make decisions based on good data and insights.

It’s time for HR to start doing the same, and this starts with asking the right questions. However

the right question does not start with HR, it starts with the business. The most basic questions

that should serve as the starting point are “what problems does our business face today?” and

“what people-based priorities can support the correction of that problem?” This is not to

suggest that algorithms completely replace HR professionals, but instead for HR employees to

become more adept at understanding and analyzing data. By having effective data analysis and

HR domain knowledge working in tandem, it can lead to more effective and evidence-based

decision-making that will assist HR in taking that next step to becoming an effective business

partner.

References

1. Population by sex and age group, Statistics Canada (Sept 2015). www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/demo10a-eng.htm

2. Developing Advanced Talent Analytics: Why It Matters to CFOs, Bersin by Deloitte, Wall Street Journal (Sept 2015)

3. Maximizing the Impact and Effectiveness of HR Analytics to Drive Business Outcomes, Scott Mondore, Shane Douthitt and Marisa Carson, Strategic Management Decisions, Vol. 34, Iss. 2 (2011)

4. High-Impact Talent Analytics: Building a World-Class HR Measurement and Analytics Function, Bersin by Deloitte, (Sept 2013).

5. HR Technology 2015 – Ten Disruptions: Ignore Them At Your Own Peril, Josh Bersin (Dec 2014), Slide: “Analytics Drives Huge ROI”.

6. The ROI of Human Capital, Jac Fitz-Enz (2011), Ch. 1 (Human Leverage), pg. 13.7. Four Most Significant Barriers to Producing Workforce Analytics, Michael Moon, HCM

Essentials – Aberdeen Group (Oct 2015)8. Predictive Analytics for Human Resources, Jac Fitz-Enz & John R. Mattox II (2014), Ch. 6

(Predictive Analytics in Action), pg. 101.9. About SEM Software. http://www.structuralequations.com/software.html10. In Hiring, Algorithms Beat Instinct, David Klieger, Nathan Kuncel, & Deniz Ones, Harvard

Business Review (May 2014).11. Machines Are Better Than Humans at Hiring the Best Employees, Rebecca Greenfield,

Bloomberg Business (Nov 2015).12. Can an Algorithm Prove You won’t Quit Your Next Job? Rebecca Greenfield, Bloomberg

Business (Nov 2015).13. Building a New Breed, Michael O’Brien, Human Resource Executive Online (Oct 2010).14. How Google Is Using People Analytics To Completely Reinvent HR, Dr. John Sullivan, ERE

Media (Feb 2013).15. Is HR Going to the Geeks?, Bernard Marr, LinkedIn (March 2015)16. Change Your Company with Better HR Analytics, Mick Collins, Harvard Business Review

(Dec 2013).