Human Capital Analytics 2.2016


Transcript of Human Capital Analytics 2.2016

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Rick Baron, MS, SPHR, SHRM-SCPPrincipal

February 25, 2016

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How does HR measure its impact?


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Where should HR focus its money and efforts?

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How can I apply data to people impact?

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How do I know HR’s efforts are impactful?

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The answer is:Human Capital Analytics

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• 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


The Challenge

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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.

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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.

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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.

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• 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

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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

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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

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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

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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

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Cause and Effect Analysis– Considers multiple independent & dependent

measures concurrently– Suggests cause-effect relationships– Creates a more significant ROI

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Cause and Effect Diagram

*Created in MS Visio

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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.

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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.

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Examples of Process Analytics• On-boarding• Selection• Performance Management• Employee Opinion surveys• Competencies adherence to performance• Leadership Development• 360 Assessments• Work-life Balance Initiatives

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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

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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.

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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

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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.

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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.

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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

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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.

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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

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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

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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

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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

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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.”

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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.

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• 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.

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• 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.

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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

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