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Chapter 14: HR Metrics and Workforce Analytics

Introduction

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

Satisfies Learning Objective 14.1: Discuss why the objective of analytics efforts needs to be improving decisions and why doing so is critical to generating return on investment.

Increasing interest in human resources (HR) metrics and analytics:

HR metrics and workforce analytics have been a topic of interest in almost every organization.

However, today, more factors are driving organizations to use these tools to enhance organizational effectiveness.

The growing power of Human Resource Information Systems (HRIS):

Integrated HRIS is one of the main reasons for the increasing interest in HR metrics and analytics.

Modern HRIS is powered by more capable computers, high-speed Internet, enhanced connectivity, and access to user-friendly analytics software.

As a result, the dynamics of human capital assessment in organizations have changed drastically.

Other factors driving the interest:

Higher potential for near real-time analysis and distribution of information.

Growth in evidence-based management.

Greater availability of information from third-party sources.

Increasing interest in human resources (HR) metrics and analytics.

The growing power of Human Resource Information Systems (HRIS).

Factors driving the interest.

A Brief History of HR Metrics and Analytics

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

Satisfies Learning Objective 14.2: Discuss how a decision-based view of HR can be used to identify important workforce analyses that can drive improved value in almost any organization.

The evolution of HR metrics and analytics:

Measures to capture the effectiveness of employees have been in place since days of scientific management and industrial and organizational psychology.

More study in the area happened during the great post-war industrial expansion in the United States.

However, assessment of HR metrics became popular with the pioneering work of Dr. Jac Fitz-enz and the early benchmarking work he conducted through the Saratoga Institute. (1984)

Measures developed with the Saratoga Institute:

Through the joint efforts of the Saratoga Institute and the American Society for Personnel Administration (ASPA), 30 metrics were developed. (Table 14.1)

Some of them include revenue per employee, expense per employee, cost of hire, and so on.

Metrics were initially used to measure aspects of HR programs and activities and were later employed to measure HR effectiveness.

Following this, Society for Human Resource Management has identified several metrics (HR Metrics Toolbox Table 14.2) that helped to build more detailed approaches for the measuring and benchmarking of employees’ behaviors. (Show PDF)

Kaplan’s and Norton’s balanced scorecard:

With Kaplan’s and Norton’s balanced scorecard came more refined thoughts about metrics.

Balanced scorecards: Focused on creating leading indicators of performance from several important perspectives (such as customer satisfaction, process effectiveness, and employee development) as well as financial performance.

The work of Huselid: Around 1995, Huselid showed that the systematic management of human resources was associated with significant differences in organizational effectiveness thus indicating the strategic potential of human resource management.

During this period where we start seeing HR with a focus on managing people to get results, instead of just a cost to control

The evolution of HR metrics and analytics.

Measures developed with the Saratoga Institute.

Kaplan’s and Norton’s balanced scorecard.

The work of Huselid.

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

Limitations of Historical Metrics

Satisfies Learning Objective 14.1: Discuss why the objective of analytics efforts needs to be improving decisions and why doing so is critical to generating return on investment.

Question 2 – What are some of the limitations of early attempts at HR Metrics

HR metrics: unchanged:

Computing and communication tools that support HR metrics have evolved drastically in the past years.

However, HR metrics have remained unchanged since the time when computerization of HR transactions were absent.

As a result, most metrics tend to focus on the cost involved and do not account for the benefits derived.

With cost as a standalone factor for influencing managerial decisions, the perception of HR as a “cost center” is only perpetuated.

An effective HR metric focuses on both costs and benefits of a decision, thus allowing a fair estimation of ROI.

Use of organization level data:

When metrics use data aggregated at the organization level, it is difficult to identify and diagnose within-organization differences.

To solve this issue, organizations are leaning towards interactive reporting portals that allow managers to drill down to lower-level details in aggregate data.

Lack of real-time feedback metrics:

When feedback metrics are delayed, responses to opportunities and problems are slowed down too.

Metrics that support real-time remedial action to minimize any negative effects are considered most effective.

Focus on costs--limited value to managers. If managers are only provided information about costs, with little or no information about benefits, costs are likely to become the primary driver of managerial decisions. This perpetuates the still common perception of HR as a “cost center.” Tended to aggregate data to the level of the organization. As such, they offer limited information that could be used to identify and diagnose within-organization differences—produced “after the fact,” resulting in slow responses to problems or opportunities. Because they provide data “after the fact,” these are described as “feedback” metrics.

HR metrics: unchanged.

Use of organization-level data.

Lack of real-time feedback metrics.

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

Contemporary HR Metrics and Workforce Analytics (1 of 8)

Satisfies Learning Objective 14.2: Discuss how a decision-based view of HR can be used to identify important workforce analyses that can drive improved value in almost any organization.

HR metrics: Attributes of an organization’s HR programs and activities, or its related outcomes.

Some examples of HR metrics include, turnover rate, head count, cost of conducting a training program etc.

HR metrics that must be tracked:

Tracking common organizational metrics can aid in benchmarking efforts.

However, an organization can derive the best benefits only when specific metrics with specific calculations are customized to the specific needs of organizational decision makers.

HR Metrics

Attributes of HR programs and activities.

HR metrics that must be tracked.

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

Contemporary HR Metrics and Workforce Analytics (2 of 8)

Satisfies Learning Objective 14.2: Discuss how a decision-based view of HR can be used to identify important workforce analyses that can drive improved value in almost any organization.

Workforce analytics: Strategies for combining data elements into metrics and for examining changes in metrics or the magnitude of relationships among them

Purpose of workforce analytics:

Helps to inform managers about the state of human capital in an organization and its impact on decision-making.

Helps to determine what metrics the organization needs, what data elements are relevant and need to be captured, and how these data elements should be combined.

Workforce Analytics

Examination of changes in metrics.

Purpose of workforce analytics.

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

Contemporary HR Metrics and Workforce Analytics (3 of 8)

Satisfies Learning Objective 14.2: Discuss how a decision-based view of HR can be used to identify important workforce analyses that can drive improved value in almost any organization.

Benchmarking: It is a method of creating useful comparisons between or within organizations.

The Saratoga Institute premiered in developing information on standard HR metrics and informed managers about differences in HR outcomes among major organizations.

The purpose of benchmarking:

Benchmarking helps to draw comparisons between HR practices and human capital in terms of cost and outcomes.

It helps an organization to understand where it is currently placed in the HR landscape and what level can it reach in terms of HR outcomes.

Challenge in benchmarking HR metrics:

The challenges faced by an organization and its corresponding HR practices differ from other organizations.

Therefore, direct comparison of external benchmarks of HR metric data to one’s own organization may not reflect a realistic picture and may also not provide the right guidelines for goal setting or for planning remedial actions.

Workforce Analytics: Benchmarking

Comparison between organizations.

The purpose benchmarking.

Challenge in benchmarking HR metrics.

Contemporary HR Metrics and Workforce Analytics (4 of 8)

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

Satisfies Learning Objective 14.3: Discuss the roles that activities such as data mining, predictive analytics, and operational experiments play in increasing organizational effectiveness

Data mining: It is the effort used to identify patterns that exist within data and which in turn may identify unrecognized causal mechanisms that can be used to enhance decision-making.

To identify causal mechanisms, data mining uses correlation and multiple regression methods and identifies patterns of relationships.

Value of Big Data:

Big Data: It is used to extract insights when multiple transaction systems generate large datasets (of many terabytes).

Consider the case of Google or any social media website that can generate very large datasets.

The data can be mined to draw inferences about customer preferences and can thus help managers to achieve better sales, customer satisfaction, and reduced costs.

Big Data is considered valuable because of three important characteristics: volume, variety, and velocity.

Volume: It offers very large amount of data that is sufficient to draw useful patterns and, in some cases, additional insights too.

Variety: Offers access to wide range of data elements, thus making new data available to the organization.

Velocity: Data are generated at a fast pace thus making decision cycles shorter.

Caveats of data mining in human capital:

In any domain, data mining can expose spurious or nonsensical relationships.

In other words, relationships between variables can be identified but data mining plays no role in determining if these relationships are meaningful, casual, or of any significance to the organization. Example: taller employees have higher leadership scores.

Another drawback of data mining is that it can capture relationships that existed in previous patterns of relationship too.

Workforce Analytics: Data mining and “Big” Data

Identification of unrecognized causal mechanisms.

Value of Big Data.

Caveats of data mining in human capital.

Contemporary HR Metrics and Workforce Analytics (5 of 8)

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

Satisfies Learning Objective 14.3: Discuss the roles that activities such as data mining, predictive analytics, and operational experiments play in increasing organizational effectiveness

Predictive analysis: The creation of models of organizational systems to predict their future outcomes at some point.

It can also help in predicting how changes in the environment and planned organizational interventions can impact key outcomes.

Benefits of predictive analysis:

Predictive analysis makes organizational planning more proactive.

By knowing what to expect, managers can act to enhance positive effects or mitigate negative ones from any internal or external action.

Tools used in predictive analysis:.

From the simple trend analysis technique to highly sophisticated models, predictive analysis uses a whole range of tools.

Also, efforts to develop balanced scorecards are examples of elementary predictive systems.

Enhancing the quality of models:

The quality of predictive analyses models can be enhanced through regular testing of assumptions in these models.

Testing and revision of assumptions lead to identification of additional leading indicators and better specifications about the nature of the relationships between predictors and outcomes.

Workforce Analytics: Predictive Analyses

Models to predict the future.

Benefits of predictive analysis.

Tools used in predictive analysis.

Enhancing the quality of models.

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

Contemporary HR Metrics and Workforce Analytics (6 of 8)

Satisfies Learning Objective 14.3: Discuss the roles that activities such as data mining, predictive analytics, and operational experiments play in increasing organizational effectiveness

Artificial intelligence enhances human decision-making:

Artificial intelligence: The approach used to automate or enhance aspects of human decision-making.

Decision-making models are fed with data about correct and incorrect decisions and factors related to decisions made thus far.

These models use multiple regression and related technologies to identify data elements and decision rules that lead to the greatest number of correct decisions.

Artificial systems are more effective than human decision-making because they are faster and always apply the optimum decision rule.

By using artificial intelligence in less ambiguous circumstances, decision-making is made more effective, and humans are allowed more time in decision contexts that are more ambiguous and uncertain.

Machine learning and unstructured data:

Machine learning: Refers to the use of algorithms that work with large volumes of data (moderately structured or unstructured) to learn relationships among data elements that can be useful in improving decision-making.

Machine learning tools are used along with human decision-makers and are specifically useful to those who may not have a deep systematic understanding of certain fields or problem domains.

Workforce Analytics: Artificial Intelligence and Machine Learning

Artificial intelligence enhances human decision-making.

Machine learning and unstructured data.

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

Contemporary HR Metrics and Workforce Analytics (7 of 8)

SKIP

Satisfies Learning Objective 14.3: Discuss the roles that activities such as data mining, predictive analytics, and operational experiments play in increasing organizational effectiveness

Difference between data mining and modeling:

Modeling and optimization involve the creation of highly accurate models of key organizational systems.

Modeling varies from data mining in that the former has a more accurate understanding of the system of relationships and interrelationships (between variables affecting specific outcomes) than the latter.

Uses of modeling:

Accurate models can be used to assess the required input for a given level of output.

They can estimate the joint effects of environmental change or organizational action.

Using modeling, refined theories about the effects of new or untested interventions can be generated.

Workforce modeling: This application tries to draw a relationship between an organization’s human capital needs and expected changes in the organization’s environment.

Workforce Analytics: Modeling and Optimization

Difference between data mining and modeling.

Uses of modeling.

Workforce modeling.

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

Contemporary HR Metrics and Workforce Analytics (8 of 8)

SKIP

Satisfies Learning Objective 14.3: Discuss the roles that activities such as data mining, predictive analytics, and operational experiments play in increasing organizational effectiveness

Operational experiments: An effective method of developing the evidence on which to base decisions that can be conducted with the organization.

Decisions based on actual evidence:

Within an organization, a lot of assumptions about “how things work” exist and managers have their own philosophies.

However, when it comes to decision making, it is the actual evidence from the functioning of the system that must drive decisions.

Google uses operational experiments:

Google does not rely on assumptions or any experts about which ad wording is more effective.

Instead, it creates an experiment, by configuring its site to alternate the presentation of competing ad text to visitors to its site and then tracks the number of “click-throughs” on the ad during a given time period.

With the help of the results, Google adopts the most effective ad wording.

Workforce Analytics: Operational Experiments

Effective method to develop the evidence.

Decisions based on actual evidence.

Google uses operational experiments.

HR Metrics, Workforce Analytics, and Organizational Effectiveness (1 of 4)

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

Satisfies Learning Objective 14.5: Discuss why the information from HR metrics and workforce analytics may fail to generate value for an organization

The impact of workforce analytics efforts:

Multiple metrics are reported with increased frequency to a broader range of managers in order to improve human capital management in an organization.

However, these efforts taken by HR professionals do not always have significant impact on organizational effectiveness.

Common problem areas:

Often HR professionals complain that managers do not tell them what information they need or use HR metrics from existing reports or even acknowledge receipt of the HR reports.

Clearly, this indicates the existence of some fundamental challenges that need to be dealt with.

The impact of workforce analytics efforts.

Common problem areas.

HR Metrics, Workforce Analytics, and Organizational Effectiveness (2 of 4)

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

Satisfies Learning Objective 14.5: Discuss why the information from HR metrics and workforce analytics may fail to generate value for an organization

Common misconceptions about HR metrics:

Assessing and reporting HR metrics is perceived to yield better organizational performance.

But the links between reporting more HR metrics and better performance of individuals and units and ultimately the organization are not well-established.

Further, the misperception about workforce analytics is that it must simply derive value from HR data, workforce analytics starts with HR data rather than from the organizational problems that need to be resolved.

Result of the misconceptions:

As a result of this approach, more time and struggle goes into determination of metrics that may not be relevant to the organization and their ways of calculation.

Also, most metrics end up being of very little use and relevance in making key decisions.

Alternative way to drive decision-making:

The right approach is however to start with the problems or opportunities faced by the organization.

This allows a deeper understanding of the information needed, helps identifying the relevant data metrics and the corresponding data elements.

“Data first” versus “problem first”:

In the “problem first” approach, the main focus is on specific managerial decisions.

Since the targets are specific, fewer metrics are calculated and reported thus reducing the cost simultaneously.

In the “data first,” data remains the main focus.

A Common and Troublesome View

Common misconceptions about HR metrics.

Result of the misconceptions.

Alternative way to drive decision making.

Data first versus problem first.

HR Metrics, Workforce Analytics, and Organizational Effectiveness (3 of 4)

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

Satisfies Learning Objective 14.5: Discuss why the information from HR metrics and workforce analytics may fail to generate value for an organization

Fundamental problem with HR metrics:

The most fundamental problem for many HR metrics is that a clear impact of the metrics on important managerial decisions remains unestablished.

Workforce analytics efforts must allow managers to access information that can help them to make better decisions regarding the acquisition and deployment of an organization’s human capital.

In other words, it is not sufficient to simply to “do” metrics and analytics, but the focus must be on deriving the potential return on investment from the efforts.

Ways to make better decisions:

It is not sufficient for analyses to simply confirm the decisions that were already about to be made.

2. With workforce analysis, managers can make better decisions in three ways:

A different and better decision is made after receiving the results of the analysis (when compared to the decision that was about to be made before the results).

The same decision is made but it is done sooner.

The manager can avoid making a decision at certain instance as the results show no requirement for intervention.

Maximizing the Impact of Workforce Analytics Efforts

Fundamental problem with HR metrics.

Ways to make better decisions.

HR Metrics, Workforce Analytics, and Organizational Effectiveness (4 of 4)

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

Satisfies Learning Objective 14.5: Discuss why the information from HR metrics and workforce analytics may fail to generate value for an organization

Varying opportunities for workforce analysis:

Most workforce analyses demand the same amount of effort and time and yet do not yield the same level of ROI.

For instance, a large organization that is just introducing workforce analysis, may find multiple opportunities to generate thousands of dollars in return.

However, with the same analyst effort, opportunities that yield millions of dollars can also be found.

Taking advantage of the opportunities: To enjoy optimal ROI, organizations must develop the capacity and discipline to recognize large analytics opportunities and focus their analysis there.

Triage in Evaluating Workforce Analysis Opportunities

Varying opportunities for workforce analysis.

Taking advantage of the opportunities.

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

So Where Are the Best Workforce Analytics Opportunities Likely to Be Found? (1 of 4)

Satisfies Learning Objective 14.4: Discuss the differences between analytics used to assess efficiency, operational effectiveness, and organizational realignment, and offer examples of each

Administrative process efficiency:

Most organizations invest time and effort in workforce analysis to report HR administrative process efficiency to accomplish how the critical human resource management (HRM) processes are in support of organizational effectiveness.

Some common metrics used to indicate the administrative efficiency of HR department are cost per hire, HR department costs, days taken to fill positions and so on.

Significance of HR process efficiency analytics:

HR process efficiency analytics is considered necessary to create credibility for HRM managers in some cases, while sometimes, they offer only limited potential in influencing organizational effectiveness.

In short, the efficiency of HR processes is important, but it is often less critical than assuring that the organization has the right processes in place.

HR Process Efficiency

Administrative process efficiency.

Significance of HR process efficiency analytics.

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

So Where Are the Best Workforce Analytics Opportunities Likely to Be Found? (2 of 4)

Satisfies Learning Objective 14.4: Discuss the differences between analytics used to assess efficiency, operational effectiveness, and organizational realignment, and offer examples of each

Question 1 – What is Operational Effectiveness?

Operational effectiveness: Analyses focus on identifying opportunities to enhance operational outcomes in an organization.

In this process, the analysts use the technical competence of HR professionals.

For instance, the analysis can draw links between HR functions like recruiting, training, and job design and operational outcomes like on-time deliveries, units sold, and so on.

Maximizing human capital intervention:

To achieve operational effectiveness in different units of an organization, workforce analysts play a consultative role and help to identify opportunities where human capital interventions can contribute.

When organizations shift from HR process efficiency to operational effectiveness, the impact of workforce analysis recommendations on organizational outcomes is significantly increased.

Operational Effectiveness

Opportunities to improve operational outcomes.

Maximising human capital interventions.

So Where Are the Best Workforce Analytics Opportunities Likely to Be Found? (3 of 4)

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

Satisfies Learning Objective 14.4: Discuss the differences between analytics used to assess efficiency, operational effectiveness, and organizational realignment, and offer examples of each

Strategic alignment: Refers to the human resource planning efforts that focus on the long-term goal to ensure replacement of the labor power for smooth operation of an organization as well as on planning for needed strategic changes in the organization.

New situations and circumstances: When an organization encounters a new situation such as a merger, entry into a new market or so on, HRM analytics plays an important role in estimating the future demand for and supply of needed human capital.

Highest potential effect: While workforce analytics to improve strategic realignment is yet to develop in most organizations, these analyses have the greatest potential to influence an organization’s bottom line.

Strategic realignment

Human resource planning.

New situations and circumstances.

Highest potential effect.

So Where Are the Best Workforce Analytics Opportunities Likely to Be Found? (4 of 4)

Starting with the end in mind

Identifying the problem or opportunity.

Triaging analytical opportunities.

Identifying influences and interventions.

Intermediate and ultimate outcomes.

Johnson, HR Metrics and Workforce Analytics, Fifth Edition. © SAGE Publications, 2021

Satisfies Learning Objective 14.6: Describe what factors managers should consider when building workforce analytics capability in an organization

Identifying the problem or opportunity:

In order to build an effective workforce analysis, one must start with identifying the biggest challenge or opportunity within the organization.

The next step would be to understand what outcome variables might change if the opportunity was captured or the challenge was solved.

The outcome variables are then represented using numbers with the help of available metrics.

A dollar value is attached to them and the difference in the values of outcomes are determined.

Triaging analytical opportunities:

Triaging refers to the determination of the highest priority analytical opportunity amongst all the available ones.

The gain achieved from each analytical opportunity and the cost of conducting the analysis is estimated.

Triage decisions are influenced by the potential benefits, rather than the cost of conducting the analysis because while cost remains more or less the same across opportunities, the difference in benefits is vast.

Identifying influences and interventions:

When the most ideal outcomes are identified, the factors influencing the outcomes and available intervention options (and the cost involved) are determined.

Most organizations do not thoroughly understand the system of factors that influence outcomes of interest and the types of available interventions and their effects.

Intermediate and ultimate outcomes:

To understand how interventions and influences impact the outcomes, it is important to determine if the chosen outcome is an intermediate or an ultimate one.

If an outcome directly leads to increases in revenue, reductions in cost, or some combination thereof, then it is an ultimate outcome, and all other outcomes are intermediate.

In human resources, most often, interventions never impact an ultimate outcome directly.

They act on an intermediate outcome which may sequentially affect one or more additional intermediate outcomes before impacting the ultimate outcome.

The challenge here is to understand the exact sequence and determine the expected effects of an HR intervention.

Limits in this understanding can lead to incorrect decisions by analysts and therefore it is recommended that they work with managers to surface the assumptions associated with the causal sequences expected from interventions so that the validity of these hypotheses can be tested.

An Example Analysis: The Case of Staffing (3 of 3)

Satisfies Learning Objective 14.6: Describe what factors managers should consider when building workforce analytics capability in an organization

Intermediate outcomes: cannot be interpreted in dollars:

Most staffing analyses focus on intermediate outcomes.

While these outcomes are important, they cannot be directly interpreted in dollars and cannot be compared to changes in costs directly.

Boudreau’s utility analysis:

Boudreau’s utility analysis lays the initial step to estimate the value of the greater contributions of better employees to organization effectiveness.

Results derived using the analysis:

When done right, the analysis can yield consistent results for low autonomy positions.

The same cannot be said about high autonomy jobs, as high responsibility increases the potential impact of each decision and involves more dollars or impacts more people.

Assessing the Financial Impact of Staffing Decisions: Utility Analysis

Intermediate outcomes: cannot be interpreted in dollars.

Boudreau’s utility analysis.

Results derived using the analysis.

Building a Workforce Analytics Function (1 of 3)

Satisfies Learning Objective 14.6: Describe what factors managers should consider when building workforce analytics capability in an organization

Getting started:

Since most organizations are yet to find their feet in establishing HR metrics and analytics, it is recommended that they focus on a limited number of potentially high-payback opportunities to build their workforce analytics upon.

A good way to get started is by determining the most critical problems in the organization that are worth solving or opportunities that best enhance organizational effectiveness.

Understanding why: Choice of outcome:

Most often, organizations use their own personal theories about organizational functioning to choose the outcome measures.

However, the chosen outcome may only be an intermediate one and may not have the expected impact on the ultimate or distal outcomes.

The why test: The why test makes organizations to ask why it is interested in a specific outcome and ensures that changes to an intermediate outcome also positively impacts the distal outcome.

The example of employee turnover:

Consider the case of employee turnover. High employee turnover costs much to the organization and can be disruptive to operations too.

By asking why turnover is important, we understand that high employee turnover can lead to loss of knowledge and important skill sets. However, this does not imply a direct loss in company profits.

By asking why, the organization can highlight the potential causal sequence through which employee turnover can influence the distal outcome.

Building this ability to understand the causal sequences and how interventions can affect through them is an important capability for an organization’s workforce analysts.

Getting started.

Understanding why.

Choice of outcome.

The why test.

The example of employee turnover.

Building a Workforce Analytics Function (2 of 3)

Satisfies Learning Objective 14.6: Describe what factors managers should consider when building workforce analytics capability in an organization.

Question 4 – Why is it important to put HR Metrics and Analytics in Context?

Reporting trend information:

In order to make HR metrics data more meaningful and consequently enhance managerial decision-making, data must be placed in context.

Reporting trend information is one way to do that.

Benchmarking:

Benchmarking also allows organizations to place data in context.

By collecting data from other organizations within the same industry, a company’s performance relative to peers can be assessed.

Internal and external benchmarking:

Since, not all organizations structure the HRM function in the same way and this can impact the value of HR efficiency metrics, external benchmarking must be done with care.

For this reason, internal benchmarking can provide more appropriate data for establishing operational objectives for the HR efficiency benchmarks.

Putting HR Metrics and Analytics Data in Context

Reporting trend information.

Benchmarking.

Internal and external benchmarking.

Building a Workforce Analytics Function (3 of 3)

Satisfies Learning Objective 14.6: Describe what factors managers should consider when building workforce analytics capability in an organization.

Reporting what we find: Questions to be addressed:

Reporting metrics involves addressing some important questions.

What metrics must be reported? (must be dealt with after considering an organization’s problems and opportunities)

How metrics must be reported? (channels for distributing the metrics such as email, website, extraction when needed)

Whom should the metrics be reported? (Senior executives alone or managers too)

Push and pull systems:

While there are many way to communicate workforce analytics information, a mix of push and pull systems is considered more effective.

Push systems : Uses push communication channels such as email, to send relevant information to decision makers. Best suited for information that is time-critical, and that the manager is unaware of.

Pull systems: Makes information available to managers at a time when it is most needed or relevant for making useful decisions. Example: posting HR metrics on internal websites.

Pull systems avoid information clutter but can be ineffective if managers do not know what information is available and where to look for it.

Frequency of reporting:

Frequency of reporting combined with proper packaging of data play a critical role in supporting effective decision making.

Long reporting cycles and aggregating too much data can pose sufficient risks.

Question 5 – What is the value of an HR Dashboard?

HR Dashboards:

Dashboards showcase the efforts taken to align real-time analysis of organizational processes (including HR) as well as the increased capacity to aggregate organizational data.

More than often, analysts wonder whether anyone pays any attention to the reports they produce.

The solution to this problem is to ensure that data is reported in context and with a meaning attached to it.

Also, workforce analysts must be consultative to understand the needs of the recipients and fit the data to the information needs of the decision maker.

Reporting what we find.

Questions to be addressed.

Push and pull systems.

Frequency of reporting.

HR dashboards.

Useful Things to Remember About HR Metrics and Analytics (1 of 3)

Satisfies Learning Objective 14.6: Describe what factors managers should consider when building workforce analytics capability in an organization.

Question 3 – What is the Primary determinant of the success of a metric and analytics project?

Don’t “do metrics”: Primary objective of HR metrics: The main aim behind developing workforce analytics and HR metrics is not to simply maintain a menu of metrics and increase organization cost, but to increase organizational effectiveness through better decision making.

Real test to assess the value of HR metrics:

Workforce analytics deliver information that supports managers in making informed and better decisions.

Therefore, the real test to assess value of the analytics project, organizations must determine the improvement in managerial decision making

Bigger: not always better: Measuring the success of analytics projects:

The success of a analytics project is gauged through the impact that the project’s results have on managerial decisions and not on the number of metrics or people involved.

Advantage of small analytics projects:

Small analytics projects require a lesser amount of time and resources.

They are less visible during the initial stages, allowing the team to learn better through trial and error.

Ultimately, small projects provide greater flexibility and allow the team to focus on critical HR metrics.

Don’t “do metrics.”

Primary objective of HR metrics.

Real test to assess the value of HR metrics.

Bigger: not always better.

Measuring the success of analytics projects.

Advantages of small analytics projects.

Useful Things to Remember About HR Metrics and Analytics (2 of 3)

Satisfies Learning Objective 14.6: Describe what factors managers should consider when building workforce analytics capability in an organization.

Avoid the temptation to measure everything aggressively:

It is not important for every HR metric to be measured.

While trying to resolve a problem or capture an opportunity, the metric outcomes that are most likely to have the greatest impact on managerial decision-making alone must be considered.

HR metrics and analytics is a journey – Not a destination:

HR metrics and workforce analytics change with time, and the corresponding problems or opportunities are unlikely to require the same analytics the next time when they are dealt with.

Therefore, organizations are required to identify new metrics and consider adjustments to the existing projects.

Be willing to learn:

Just like any other operational function, HR metrics and analytics efforts can be examined regularly for improvement.

Organizations should have a metrics and analytics “laboratory” where new analyses can be experimented with and existing assumptions about organizational requirements can be tested.

This action fosters new approaches and allows new metrics and analytics to be created.

Avoid the temptation to measure everything aggressively.

HR metrics and analytics is a journey - Not a destination.

Be willing to learn.

Useful Things to Remember About HR Metrics and Analytics (3 of 3)

Satisfies Learning Objective 14.6: Describe what factors managers should consider when building workforce analytics capability in an organization.

Source of competitive advantage:

Organizations that develop useful and effective workforce analytics are likely to have a significant source of competitive advantage over their peers.

With the available tools and computing infrastructure, organizations can effectively manage and improve HR programs and HRIS use.

Managerial benefits of metrics for organizations:

Since data from various individual applications is integrated, operational reporting will become easier and cost-efficient.

Graphically rich information will be easily available to decision makers.

HR investments and practices can be optimized and aligned to enterprise performance goals.

Benchmarking: Organizations will begin to understand the competitive value of HR metrics and workforce analysis and therefore access to benchmarking will become more difficult.

Workforce Analytics and the Future

Source of competitive advantage.

Managerial benefits of metrics for organizations.

Benchmarking.

chap 14

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