8. • Availability of organizational
and financial information1
• Expectations of Boards of
Directors, Executives, and
Analysts
2
• Limited financial and people
resource to apply towards
the HR function
3
The Challenge
9. Human Capital Analytics is….
Human Capital or Human Resources Analytics or People Analytics or
Talent Analytics, is:
• the use of data mining and collection of organizational and
financial information that undergoes business analytics (BA)
techniques to human resources (HR) data.
• to provide an organization with insights for effectively managing
employees so that business goals can be reached quickly and
efficiently.
The challenge of human resources analytics is to identify what data
should be captured and how to use the data to model and predict
capabilities so the organization gets an optimal return on
investment (ROI) on its human capital.
10. Then again there is reality….
Although most organizations have enough data to make analytics useful:
• The data is often created and stored in multiple places in multiple formats.
• There exists gatekeepers of the data who are reluctant to share fearing
scrutiny.
• Business intelligence systems (BI) is complicated and requires specialized
skills or establishing specialized functions within the organization or hiring
business consultants.
But for every HR function, the ability to review organizational and
financial data against HR efforts will result in better validation of
HR’s efforts and justification for people investment.
11. Learning Objectives
• To gather a baseline
understanding of what
Human Capital Analytics
is, the significance of it,
and a roadmap of
implementing such an
effort for any
organization.
13. • Introduction
• What are Human Capital Analytics?
• Why are Human Capital Analytics important?
• What are the Benefits of Human Capital
Analytics?
• What HR Analytics Is Not
• How HR Analytics Should be Executed
• Statistical Tools
• Key approaches to analytics
• Steps in implementing a Data Analytics Plan
• Seven Guiding Principles of Human Capital
Analytics
• Examples of New Human Capital Metrics
• Case Studies
• Closing
• Q&A
14. Why is Human Capital Analytics
important?
– HR’s traditional use of “Best Practice” or “Me
Too” program approaches are not effective
– Expanding access to data due to information
systems
– Organizational Leaders conditioning towards
data analysis and reliance as compared to
instinctive reaction
– Continued scrutiny of HR programs by
executives, finance, and board of directors
15. What are the Benefits of Human
Capital Analytics?
– They can redirect the amount they spend today
from the wrong employee initiatives to more
beneficial employee initiatives.
– The investments decided will result in tangible
outcomes that benefit shareholders, customers and
employees themselves.
– The returns on such investments can be quantified.
– HR departments can be held accountable to
impacting the bottom line
– HR executives will be included in the conversation
because they can now quantify their impact upon
business outcomes
16. What HR Analytics Is Not
– Metrics or Scorecards such as time to hire and the quality
of people hired
– Alignment: demonstrating how the HR function supports
the business
– Gap Analysis: Demonstrating improvements from year-to-
year or employee survey improvements
– Benchmarking comparable data sets between
organizations
– Cost-based, lagging metrics that either measure
employee-related expenditures such as headcount costs
or efficiencies in their function itself such as position fill
rates.
Most HR executives lack forward-looking
data that help drive business strategy
17. How HR Analytics Should be
Implemented
– To calculate return on investment
– To provide evidence-based information related to
HR efforts
– Have a role in the organization’s financial health
– Show results of efforts not just HR activity
– Create an HR strategy that has direct impact on
the financial results of the organization
Three in four surveyed companies (75 percent)
believe that using people analytics is “important,”
but just 8 percent believe their organization is
“strong” in this area—almost exactly the same
percentage as in 2014. – Deloitte Survey
19. Cause and Effect Analysis
– Considers multiple independent & dependent
measures concurrently
– Suggests cause-effect relationships
– Creates a more significant ROI
21. Regression Analysis
– Enables HR leaders to look a multiple pieces of
data simultaneously to help prioritize the impact
of people data on business outcomes
– Regression is used to show the connection from
attitudes to attitudes on an employee opinion
survey or connecting survey items to turnover
intentions, etc.
– Can be used to tie sales or financial performance
data to hiring, restructuring, training,
compensation changes, etc.
Regression Analysis can be
completed within MS Excel by using
their “Data Analysis” add on.
22. Other Opportunities
– Pareto Charts – a chart that contains both bars
and line graphs and represents in descending
order the amounts by bars and the cumulative
total by line.
– Histogram or “bar chart” - represents groupings
of data by a series of bars.
Data can be extracted from multiple databases through
their “export feature” and introduced to MS Excel to
conduct the analysis.
24. Examples of Process Analytics
• On-boarding
• Selection
• Performance Management
• Employee Opinion surveys
• Competencies adherence to performance
• Leadership Development
• 360 Assessments
• Work-life Balance Initiatives
25. Integrated Analytics
• Succession Planning
• HR Strategy Development
Companies that build capabilities in people
analytics outperform their peers in quality of hire,
retention, and leadership capabilities, and are
generally higher ranked in their employment brand
– Deloitte Report
27. Identify the Desired Outcomes
• Determine the top two to three most critical
outcomes on which to focus.
• For example, outcomes such as productivity, turnover
and customer satisfaction
• Financial indicators
• Safety-related data
Similar to developing a theory, the gathering of preliminary
data through surveys, focus groups, or readily available
information will help determine where focus should exist.
28. Create a Cross-Functional Data Team
• Identify the various owners of the outcome data
• Team should include measurement experts, line of
business leaders or metric owners and HR Leadership.
• Members should be represented from multiple
disciplines to ensure coverage of all available data.
Different perspectives will also aid in assuring that all
available data sets are considered.
There may be issues related to silo mentality and
turf issues when assembling such a team. Effective
communication in establishing this effort is
necessary.
HC Data Analytics is not necessarily about
measuring HR’s performance, but measuring HR
programs and initiatives that impact org
performance
29. Evaluate Outcome Measures
• Determine how the data is currently captured and opportunities
for gathering it.
• Decide on the frequency of the measurement
• Determine the level of measurement (by line of business, by
work unit, by manager, etc.)
• Identify who are the organizational owners of each of the
outcomes measures (the department or leader of the particular
measurement
• Understanding each of these measurement characteristics is
important before any linkages to employee data can be made.
• The goal is to have apples-to-apples comparisons of the data-
which means that if you want to look at productivity numbers,
you need to have productivity data that is measures at the same
interval over an extended period of time.
It’s natural to want to change measures or intervals, but
important to stick to a data set for a period of time
before deviating in order to properly test the data for
validity.
30. Analyzing the Data
• This step requires advanced statistical knowledge to
link the data.
• If such resources don’t exist within your organization,
then hiring a consultant, graduate student, professor
or full-time statistician for this role might be
necessary.
• Using structural equations modeling affords us the
ability to determine whether there exists a viable
cause and effective driver of the key outcome desired
such as customer satisfaction, financial performance,
or reduction in costs.
31. Analyze Data Conti.
• The statistical component of this step should include:
– Understanding the relationship between employee
initiatives, skills, behaviors, attitudes as it relates to the
desired business outcomes
– Prioritizing types of interventions such as where to
spend time or money
– Calculating expected ROI to determine levels of
investments and returns
– This work is designed to allow you to determine your
HR priories and how much to invest in them.
– The final result generated from the data analysis step is
a list of priorities that have data and analysis behind
them to ensure an impact on the business behind them
to ensure an impact on the business
32. Build Program & Execute
• Create interventions that will have the desired effect
• Action-planning stage in which to focus activities at
the systematic, organization wide, line of business or
work-unit level.
• An expected return will now be used to guide the HR
strategy, and initiatives must be customized and place
in the context of each unique organization
• Align the strategy, programs, or initiatives to the data
and then use later data sets to validate the success of
the efforts.
33. Measure & Adjust
• Re-measure to assess progress and calculate actual
return-on-investment.
• Leaders should make slight adjustments to initiates
along the way based on regular measurement results.
• Frequent, wholesale changes to the strategic focus is
not advised
• Pick two to three priorities and build action plans
around those priorities
• Measure progress against those plans two to three
more times, and then re-calculate the dataset linkages
and re-prioritize
35. Guiding Principles of applying Human
Capital Analytics
– Measure the organization’s entire investment in human
capital including payroll, benefits, training, etc.
– Used standardized, auditable data sources from the
organization’s financial system or system or record
– Define and measure data consistently over time
– Use only a small number of measures and validate often
– Address important strategic questions about what drives
business results not an HR self-validating presentation
– Create a credible connection between human capital
performance and business performance
– Apply documented, recurring methods that are resistant
to being modified to make results appear beneficial
36. Examples of New Human Capital Metrics
– Human Capital Return on Investment (HC ROI)
• HC ROI =(Profit-Financial Capital Costs)/Human Capital
Costs
– Productivity
• Productivity = (Revenue – Material Costs)/(Human Capital
Costs + Financial Capital Costs)
– Profit Sensitivity
• Profit Sensitivity = Incentive Compensation / Profit Goal
– Financial Capital Costs = Interest, Depreciation,
Amortization and Cost of Equity
– Human Capital Costs = Employee Costs, Costs in
Support of Employees and Costs in Lieu of Employees
38. Con Agra
• ConAgra Foods Inc. looked to measure the impact of a new
leadership training initiative the company had launched.
• They studied the performance of 600 trained and 1,600 untrained
supervisors across 65 U.S.-based plants to determine the effect of
training on leader retention.
• By measuring the performance of the two groups, it was
determined that the overall 12-month turnover of trained
supervisors was just 5.3%, compared with 11.4% for untrained
supervisors, translating to a savings of $2.3 million in the first year
of the leadership initiative.
• This helped them gather raw numbers to justify management
training going forward.
ConAgra—maker of American treats such
as Reddi-wip, Orville Redenbacher
popcorn, and Hebrew National hot dogs
39. Google
• People management decisions at Google are guided
by the powerful “people analytics team.
• Two key quotes from the team highlight their goals:
“All people decisions at Google are based on data
and analytics.”
The goal is to … “bring the same level of rigor to
people-decisions that we do to engineering
decisions.”
• They use “data-based decision-making,” “algorithm
based decision-making,” or “fact or evidence-based
decision-making.”
40. Top reasons for Google’s people analytics approach
• The people analytics team reports directly to the VP and it has a
representative in each major HR function.
• Leadership characteristics and the role of managers – Research
analyzed reams of internal data and determined that great managers
are essential for top performance and retention. It further identified
the eight characteristics of great leaders.
• The PiLab — Google’s PiLab is a unique subgroup. It conducts applied
experiments within Google to determine the most effective
approaches for managing people and maintaining a productive
environment.
• Retention algorithm — A mathematical algorithm to proactively and
successfully predict which employees are most likely to become a
retention problem.
• Predictive modeling – People management is forward looking at
Google. Predictive models and use “what if” analysis to continually
improve their forecasts of upcoming people management problems
and opportunities.
41. • Improving diversity – Analytics are used to
solve diversity problems.
• Hiring algorithm – Developed an algorithm
for predicting which candidates had the
highest probability of succeeding after they
are hired. Google is also unique in its
strategic approach to hiring because its hiring
decisions are made by a group in order to
prevent individual hiring managers from
hiring people for their own short-term needs.
42. • Calculating the value of top performers – Google executives have
calculated the performance differential between an exceptional
technologist and an average one (as much as 300 times higher).
• Workplace design drives collaboration – Google has an
extraordinary focus on increasing collaboration between
employees from different functions.
• Increasing discovery and learning – Rather than focusing on
traditional classroom learning, the emphasis is on hands-on
learning (the vast majority of people learn through on the job
learning). Google has increased discovery and learning through
project rotations, learning from failures, and even through inviting
people like Al Gore and Lady Gaga to speak to their employees.
• It doesn’t dictate; it convinces with data — The final key to
Google’s people analytics team’s success occurs not during the
analysis phase, but instead when it present its final proposals to
executives and managers.
44. Closing
– Begin with what is your most pressing HR issue
– Identify what data is available
– Seek stakeholders in which to partner
– Focus on correlation and cause and effect coming
from data sources throughout the organization
– Measure consistently over an appropriate range of
time
– Do not interfere until you have your base data
– Make moderate changes and interventions
– Measure again
– Adjust and reconfirm your findings
