What are Metrics and how to Avoid Common Mistakes When Working with Them

Seventh Entry in the Metrics and Analytics Series

Now that we’ve spent several weeks in the Metrics and Analytics Series defining what analytics are and discussing several real world applications, I want to spend this week reviewing and defining metrics and some common mistakes when using them.

Let’s start out by defining what metrics are according to Jac Fitz-Enz in his book The New HR Analytics. According to Fitz-Enz, metrics “are numbers that indicate how well a unit or an organization is performing in a specific function.” Rather than relying on anecdotal evidence, metrics provide context to where we can analyze performance much more accurately.

These metrics come in the form of percentages, ratios, complex formulas, or incremental differences and can be either individual or aggregated. We can also track them over time in order to show trends.

The data for the metrics are found in both internal and external sources. Internally, the data are payroll, employee surveys, Enterprise Resource Planning (ERP) systems, HR functions, marketing data, sales data, and financial statements. Externally, the data are common benchmarks in your industry, national and local labor market trends, salary surveys, actions from competitors, workforce demographics, and government reports.

In addition, the data collected is both quantitative and qualitative.

Quantitative data are the numbers. Examples of quantitative data are retention rate, overtime, training & development hours per employee which can be found in most HRIS systems. Some other examples are tracking the number of daily employee-client interactions, the number of units an employee produces, etc. These data can be used to rank employees to award bonuses, raises, and promotions for those who excel as well as offer additional training and coaching or discipline employees who are falling short.

Qualitative data are actions and behaviors that are observed. There are no numbers involved which means the data are subjective. Often these data are collected via employee surveys, interviews, and observations. Examples of qualitative data are why employees stay or leave an organization, the value of teamwork in an organization, the effectiveness of how a supervisor manages her direct reports, etc.

It’s also very important to avoid making certain common mistakes when working with metrics. HR Professionals need to show that we understand what we are talking about and how we analyze and report them as they contribute to the improving the business. I have summarized the following common mistakes from the The New HR Analytics.

  • Confusing Data with Information – Don’t bury yourself in data thinking you will find some valuable information simply from gathering it. You will need to know what you will do with it once you have it.
  • Valuing Inside Versus Outside Data – As Fitz-Enz says “…no one in the organization cares what is happening with the human resource function. All they want to know is what value HR is generating for the company.” Don’t get hung up on the HR activities, instead focus on the employee activity and how it impacts the organization.
  • Generating Irrelevant Data – Metrics must be able to effectively answer relevant business questions so the focus must be to collect and report only data that is important to the business. Learn and understand what metrics are important to your organization.
  • Measuring Activity Versus Impact – Don’t collect and report data that doesn’t show some positive or negative effect. Just reporting costs, quantities, or time cycles without describing their effects on the business is ineffective and a waste of everybody’s time.
  • Relying on Gross Numbers – Try to avoid averages as they mask the effects on the business. Analyze the mean, median, mode, and the percentile in order to determine if the data points are spread out in a wide range or are bunched up around the middle.
  • Not Telling the Story – After collecting and analyzing the data, make sure to tell the story of what happened, why it happened, when it happened, where it happened, how it happened, and to whom it happened.  Never report something if it doesn’t tell a story.
  • Analysis Stagnation – What are the implications of the data and what are you going to do with it? If the data and the story are compelling enough, determine how to get management to take action in order to solve the identified problem or exploit the identified opportunity.

I encourage you to take another look at these common mistakes and do what you can to avoid them. As I mentioned, HR Professionals must, in order to be take seriously, be able to understand what we are talking about and how to effectively analyze and report our data in order to positively impact the business of our organization.

I invite you to take the survey located at the top of the sidebar on this week’s post about metrics and some common mistakes about them.

Linking Human Capital Measurements to ROI – Part 2

Sixth Entry in the Metrics and Analytics Series

Last week, I published Part 1 of Linking Human Capital Measurements to ROI, setting the stage for the four human capital metrics that track the return on investment in human capital. My source is Kirk Hallowell’s essay in The New HR Analytics book by Jac Fitz-Enz.

As a brief summary,  Hallowell’s four metrics are designed to be event driven (how and when the measurement takes place), clear and easy to understand, and focus on the highest points of leverage for gain or loss of ROI (fewer strategic measures).

So, with that, let’s dig in!

Performance metric 1 – Time to Full Productivity (TFP)

An employee’s time to full productivity (TFP) is simply a learning-value curve that will increase over time as that employee improves their knowledge, skills, and productivity. TFP is a very important metric because it can focus and direct the organization’s investment strategies. In order for this metric to be effective, however, full productivity needs to be clearly defined and quantified in order to properly measure it.

An employee’s competencies and professional relationships will predict the employee’s future performance and contribution to the organizations overall performance.

The primary goal here is for the organization to shorten the employee’s TFP as much as they possibly can. Some of the ways they can do this are listed here:

  • Use an integrated talent development system
  • Hire employees for their competencies and adaptive learning skills
  • Provide competency-based training
  • Deliver a robust and effective onboarding process
  • Identify and address development needs early
  • Deliver timely and effective feedback
  • Provide incentive-based pay
  • Optimize environmental issues (work flow, equipment, support resources, etc.)

Performance Metric 2 – Quality of Hire

The quality of hire is how the employee fits within the organization’s culture and their ability to accomplish their job responsibilities. Different employees will reach full productivity at different rates depending on their skills and experience.  Each employee will have a different starting point, shape, and trajectory on the learning-value curve.

Several variables that can determine the quality of hire are listed here:

  • The employee’s key experiences as they relate to the job responsibilities
  • Their past work and performance
  • A competency assessment
  • Their adaptive learning skills
  • Personality variables as measured by an assessment

Of course, the actual quality of a hire will vary greatly depending on how the candidates are sourced and recruited. Employees with stronger and more developed competencies will achieve their TFP much quicker.

We can also use the quality of hire to justify pay differences between new hires by paying more to those who can show that they are capable of reaching TFP sooner than their peers.  An organization’s decision to invest more in a higher quality of hire will be justified when that new hire reaches their TFP quicker than her peers.

Performance Metric 3 – Quality of Promotion

The quality of a promotion depends on how well the newly promoted manager adjusts and learns their new position. There will be a dip in their learning-value curve after their promotion but if the employee was properly vetted for the promotion and the proper training is administered, the learning-value curve will recover and the employee’s performance will start to provide a solid return on investment.

Most of us can agree that one of the most difficult transitions an employee makes is when they are promoted from a line employee to a supervisor. The employee must shift from being a technical/administrative/functional expert to a management expert.

In addition, when the newly promoted employee becomes the supervisor of people with whom they were peers, they will often fail or struggle for a long period of time. This is where the organization must invest in management training, coaching and effective feedback in order to realize a good ROI.

And this is where the learning-value curve takes a dip.

If the employee’s promotion is successful, their learning-value curve will recover from the dip and begin an upward increase positively impacting their direct reports, systems, and processes.

Performance Metric 4 – Quality of Separation

The loss of good employees can have a tremendous negative impact on an organization’s economic return. Typically, an organization does not measure this impact leaving it a costly and unknown mystery to how serious the impact actually is.

When a good employee leaves an organization, the ROI in human capital is potentially reduced in the following ways:

  • The potential for the employee to add economic value from their performance immediately stops.
  • The organization’s investment in training, experience, and internal networking of the employee is immediately lost.
  • New investments to replace the employee must be made in order to maintain and grow productivity.
  • Loss of potential revenue streams and broken customer relationships may hurt the organization’s profitability.
  • The employee may move to a competitor and take their intellectual capital and customer relationships with them.
  • The remaining employee’s morale and productivity is typically negatively impacted.

The separation costs of a top-performing employee has been estimated to be 75 to 125% of that employee’s annual salary when including lost opportunity costs and adding the direct and indirect costs of hiring, training, onboarding a new employee.

 

The four metrics I just briefly discussed here give organizations the opportunity to apply a dollar amount on the cost or return on investment as they relate to human capital investment as was done above in the Quality of Separation metric above.

I highly recommend you read Hallowell’s essay in Fitz-Enz’s book where he does a great job of explaining the four metrics as they apply to his case study.

Please take this week’s survey, located at the top of the sidebar, about this week’s subject of linking human capital measurements to ROI!

Linking Human Capital Measurements to ROI – Part 1

Fifth Entry in the Metrics and Analytics Series

While looking for ideas for this week’s post and podcast, I came across a very interesting essay in The New HR Analytics book by Jac Fitz-Enz that I feel is very important in understanding how human capital measurements should be made in terms of linking them to an organization’s return on investment (ROI).

The essay is by Kirk Hallowell titled “Roberta Versus the Inventory Control System: A Case Study in Human Capital Return on Investment”.  I’m not going to discuss the actual case study presented in the essay but I want to review the key concepts in the essay as I think they make a great deal of sense in how we should change the way we think about human capital metrics and ROI.

The concepts he discusses identify ways to link an organization’s investments in human capital to their financial returns in the same manner they apply to depreciating or appreciating their tangible assets. Hallowell suggests, and I agree, that accounting metrics and rules need to be modified in order to change the way we think about how we invest in people.

Human Resource costs – recruiting, payroll, benefits, training & development, etc. can typically be a full 70% of an organization’s budget. The fact that most companies don’t have a reliable and consistent method of measuring this much of a company’s budget is concerning to say the least. In addition, human capital costs are always expensed rather than depreciated. This prevents the organization’s leadership from effectively managing and maximizing their human capital return on investment the same way they do with their other tangible asset investments.

When a company invests in their tangible and people assets, they do so with the goal of achieving the same business results but the way the company processes the accounting for each investment is completely different.  Human capital costs are expensed and immediately impact the company’s balance sheet while investments in tangible assets (physical plants, equipment, etc.) are listed as assets and depreciated for up to 30 years.

The result? The tangible assets, which are typically a much greater investment, are recognized as a significantly lower expense on the company’s balance sheet than an initial investment in human capital expenses.

As tangible assets age, they decrease in value and within a certain period of time, they lose all of their value and will need to be replaced. These tangible assets will require maintenance and utility costs to keep them from deteriorating too quickly.

Unlike tangible assets, human capital assets actually increases in value over time. Employees gain experience, expertise, and knowledge becoming more efficient through their work and training. Effective training and management will make the company’s operations more efficient and employees will increase their ability to add value to the organization’s operations. This will happen while the company’s investment in salary, benefits, administration, and training remains more or less consistent.

The return on investment for organizations is always driven by their people but the investment is listed as a nondepreciated expense. Crazy. We have our most valuable asset, our people. We invest in finding the best people we can, train and develop them, and as a result the value of that asset actually increases!  But we treat it simply as an expense rather than an actual appreciating asset!

Hallowell’s solution is to introduce four human capital metrics that will track the return on investment in both the tangible and human capital assets. The four metrics are designed to be event driven, clear and easy to understand, and focus on the highest points of leverage for gain or loss of ROI.

I will explore these four human capital metrics in next week’s post and podcast. This post is a little shorter than usual but if I include the four metrics, it becomes too large of a post so I decided to split this topic into two parts.

Please take this week’s survey, located at the top of the sidebar, about this week’s subject of linking human capital measurements to ROI!

Some Predictive HR Analytics to Start Using

Fourth Entry in the Metrics and Analytics Series

Today I’m going to review and explore a number of actual predictive HR analytic measurements that Jac Fitz-Enz discusses in his book, The New HR Analytics.  The first eight are ones that Fitz-Enz considers the most effective  based on his actual experience working with many different organizations since the 1980s. The additional three are from other experts and are equally useful as leading indicators.

Fitz-Enz uses ratios but I like to use percentages instead, so I tweaked his definitions a bit.

1. Professional/Managerial Percentage: This is the number of professionals and managers compared as a percentage to the total number of employees in the organization’s workforce.  (e.g.  Let’s take an organization with 2500 employees and 1352 professionals and managers. They would have a Professional/Managerial Percentage of 54.08% (1352/2500=.5408)). Typically, a organization with a higher percentage would be considered as having a greater chance for future growth and profitability. In this example, the organization may or may not have a potential problem because it would depend on the nature of the business and/or industry.

2. Readiness Percentage (Succession): This is the percentage of  key jobs with at least one qualified person ready to take over.  (e.g. An organization with 82 key jobs has determined through their succession plan review and analysis that they currently have 36 employees who can effectively step in and take over if those key jobs are vacated. This would be a Readiness Percentage of 43.9% (36/82=.4390)). The closer this number is to 100%, the better so in this example, the organization has a fairly serious gap in their succession planning strategy for their key positions. This will likely result in slower growth while these positions remain vacant during the talent acquisition process and increased costs as they recruit for outside and often expensive talent.

3. Commitment Percentage: This is the percentage of the organization’s staff that is committed to the organization’s overall mission and vision. This percentage is measured by an employee survey.  (e.g. An employee survey was conducted and it was discovered that only 739 of the organization’s 2500 employees knew and believed in the organization’s mission and vision. This would be a Commitment Percentage of 29.56% (739/2500=.2956)).  The higher this percentage, the better. In the example, the organization shows signs of a serious lack of commitment and employee buy-in of the organization’s values and mission. A lack of commitment shows a lack of engagement which leads to lower productivity and increased turnover.

4. Leadership Rating: This is the performance rating of the organization’s current leadership as measured by the organization’s staff.  This is also measured by an employee survey. (e.g. In a survey, using a scale of 1 to 5 with 1 being Unsatisfactory, 3 being Meets Expectations, and 5 being Outstanding, the organization’s staff rated their leadership at an average score of 2.43). According to the scale, the organization’s leaders are below expectations. This rating is predictive of employee retention and turnover rates as it is well known that the most common reason people quit their jobs is because of poor managers and leadership.

5. Climate-Culture Rating: This is the rating the organization’s staff gives as to whether the organization is a good place to work and is also measured by an employee survey. (e.g.  In a survey and using the same 1 to 5 rating scale, the employees give the organization a Climate-Culture Rating of 2.12 which would be below expectations). This rating is also predictive of retention and turnover rates because the second most common reason people quit their jobs is based on the poor working climate and culture of the organization.

6. Training Rating: These are the scores from the organization’s current training programs that develop skills that help employees get their jobs done now.  Interestingly, this rating is not concerned in training for skills needed for the future because having skills you don’t use or need now does not add positively to corporate value.

7. Accession Percentage (Turnover): This is the number of new and replacement hires as compared to  the total number of employees in the workforce.  (e.g. An organization with 2500 employees had 1750 new and replacement hires during the previous year for an Accession (turnover) Percentage of 70% (1750/2500=.700)). Of course, this is a negative indicator and the lower the number the better. There are both hard costs, conservatively estimated to be six to nine month’s of the employee’s salary to hire and train that employee, and the soft costs, lower engagement and morale from the remaining staff and lower productivity from the new hire.

8. Depletion Percentage (Turnover): This is the percentage of the top talent the organization lost in a year. (e.g. An organization with 2500 employees lost 126 of their top employees in the previous year for a Depletion Percentage of  5.04% (126/2500=.0504)). This is also a negative indicator and a lower number is better. The higher the number, the worse the organization’s future ability to maximize profitability as they are losing their best innovators, producers, and leaders not to mention the hard and soft costs we discussed earlier associated with turnover.

As I mentioned at the beginning, in addition to these eight measurements, there are three more predictive HR analytic measurements mentioned in the book that also serve as very good leading indicators. There isn’t a lot of detail in the book on these measurements but they seem interesting and ones that I will explore further in the future.

1. Executive Stability Ratio and Separation Rate: This rate shows that executives with more than three years of executive experience lowers voluntary employee turnover in the organization.

2. Management Ratio and Promotion Rate: The number of employees that each manager in an organization supervises impacts the number of promotions that those employees have available to them. Managers with a smaller span of control, supervising fewer employees, have fewer promotion opportunities for  them. This affects employee engagement, morale, and retention.

3. Training Investment Factor and Promotion Rate: This measurement shows that the more an organization spends on training programs, the more employee professional development will occur which should increase employee engagement, morale, and productivity.

These 11 predictive HR analytic measurements are all excellent and a great place to start.

Most of the data you need can be found on your current HRIS and you can run an employee survey to collect the data you need for the remainder. I suggest using SuveyGizmo or SurveyMonkey to create and administer an employee survey. They both have free accounts and trial periods where you can test run some surveys with no financial risk.

I prefer using SurveyGizmo and have decided to add a new feature, using SurveyGizmo, called the HHHR Weekly Survey based on my latest post and podcast, if appropriate. So take a look at the top of the sidebar on the right or click on this link and take the survey.

The Three Types of Analytics

Third Entry in the Metrics and Analytics Series

We still have a little bit of work to do before we get into the real nuts and bolts of metrics and analytics.

Last week we discussed the five steps of  analytics and this week we are going to cover the three different types of analytics, Descriptive Analytics, Predictive Analytics, and Causation. Again, I am taking all of this information and summarizing it from the Jac Fitz-Enz book titled The New HR Analytics.

The first type of analytic is the most basic, Descriptive Analytics. This type of analytics takes a look at data and analyzes events that happened in the past to help us understand how to approach the future. With this data, we can look for reasons behind past success and failure. Most management reporting uses this type of data.

As HR professionals, we all know that there are many different groups and subgroups within our workforce. With Descriptive Analytics we can really take a deep dive into our workforce and really analyze each of the different groups. It allows us to discover and describe many types of relationships and differences between the groups and show past and current behavior among the groups. It’s important to remember that this type of analytic is not a prescription or solution, but rather just the exploration of past and current data.

Descriptive analytic models quantify relationships in data into group classifications or characteristic. We can take almost any group characteristic and use it to build descriptive models to understand our workforce. The characteristics can be things such as performance appraisal ratings, skills, education, rank, title, age, etc. Fitz-Enz equates this to what the marketing department does with customer segmentation and calls it workforce segmentation with the purpose of improving the return on investment, or ROI, of our HR services.

The second type is Predictive Analytics. This is where we take data and turn it into valuable and actionable information. It uses data to determine the possible outcome of a future event. It gives us meaning to the current patterns that we see in Descriptive Analytics to identify risks and opportunities in helping us see what might happen in the future.

It will give us a good probability of what might happen in the future. We won’t be able to predict the future with 100% accuracy but we can increase the probability that we will be right by reducing as many variabilities as possible.

A great example in the Fitz-Enz book is building a model for selecting employees to hire, train, and promote for a particular position. The model is based on traits, skills, and experience of your very best employees in that particular position. You will increase the probability of making a successful selection by applying the model to all of the candidates for the job.

The third and final type of analytic is called Causation. This is a combination of Descriptive and Predictive Analytics and is considered the most sophisticated level of human capital analysis.

Causal modeling is used to find the hidden root cause of a problem or to make a business proposition for a human capital project investment. Fitz-Enz cites Dr. Nick Bontis as the most prominent expert of causal modeling and uses an example of the perceived value of a training program by the trainees.

In his model, he wants to determine the trainee’s perceived value of the training program so he adds the perceived value of training by the trainees to their rating of the course materials and delivery.

By surveying the trainees, we can determine the business value of the training program by asking them to rate their perceived value of the training on a scale of 1 to 100, the course materials from 1 to 5, and the delivery from 1 to 5. Throw these into an equation and you get something like this:

Perceived Value Course Materials Delivery Total
.80 X 4 X 5 = 16

And you get a total score of 16 out of a possible maximum of 25. We can now track the training’s effectiveness over time, the value of the training to the organization, etc.

When discussing HR Analytics, we can’t simply rely on the past to predict the future. We have to have knowledge as our base in order to do so. Fitz-Enz puts it this way:

Being able to foretell what is likely to happen with a high degree of probability depends on four things:

  1. Comprehension of past and current events.
  2. Understanding not only trends but also the drivers behind them.
  3. Being able to see patterns of consistency as well as change.
  4. Having tools to describe the probability of something in the future.

HR Analytics takes HR metrics and looks to the future by taking past and current strategic and operational data and adding leading indicators.

We now have a basic understanding of the three types of analytics which will help us understand many of the topics in this series I will cover in future posts and podcasts.

The Five Steps of Analytics

Second Entry in the Metrics and Analytics Series

Next in my series of metrics and analytics, I feel its important to discuss some more of the foundational elements, or the “first steps” as Jac Fitz-Enz calls it in Chapter 2 of his book, The New HR Analytics, in order to better understand the topic.

One of the first things to remember is that it doesn’t make a lot of sense to spend time on metrics that are of very little value to a business. Value comes from the knowledge of things that actually matter and what matters most is a business question, not an HR question. Those of us in Human Resources have to decide what actually does matter to the business and for what purpose.

To help decide what matters, Fitz-Enz introduces five steps of analytics which I will review here:

Step 1 – Recording the work (hiring, paying, training, supporting, and retaining). This is the most basic of HR metrics and were we measure how efficient our organization’s processes are and how we can improve them. This step indirectly creates value for the organization by saving money and/or time, improving production capacity, or improving customer service by coming up with better procedures.

Step 2 – Relating to the organization’s goals (quality, innovation, productivity, service). These four elements, known as QIPS, cover all of the basic goals of most organizations. Goals related to these elements are set by the senior leaders who regularly review the organization’s results as compared to the organization’s goals.

It is important to align the results of our employee’s work to these goals which are related to QIPS. It shows the value of each employee’s work and how it aligns to the organization’s goals.

Step 3 – Comparing results to other organizations (benchmarking). This step compares the organization’s results to those of other comparable organizations. Some examples are comparing the turnover rate between branch stores in a large department store chain, or comparing sales results with organizations within your organization’s industry.

Of course, the more detailed data available from that comparable organization or group, the better the value of the benchmarking as there can be a great deal of variance between the different branch stores or other companies within your industry.

Step 4 – Understanding past behavior and outcome (descriptive analytics). This step is where the actual analysis begins to happen. This is where we start to look for and describe relationships among the data. It doesn’t, however, give meaning to any patterns. We start to see trends from the past but it’s important to remember that its very risky to accurately make predictions about the future from these trends as the marketplace is always volatile and rapidly changing.

Step 5 – Predicting future likelihoods (prescriptive analytics) This step compares what happened in the past to what will probably happen in the future. This is predictive analytics. This is were we start to see meaning to the patterns we see in the descriptive analytics described above. Some examples are when banks predict credit worthiness and insurers predict patterns of accident rates. HR can apply prescriptive analytics to decisions on things like the expected return on hiring, training, and planning of human capital.

As you probably already guessed, these five steps increase in value going up from Step 1 to Step 5. Step 1 is where organizations typically start by collecting basic data like cost, time, and quantity. Step 2 is an easy next step where we simply relate that basic data collected in Step 1 to the organization’s goals. Step 3 is where we compare the data from Step 1 to a comparative organization or group to see how we stack up.

Steps 1 through 3 deal with what are known as metrics as I defined here last week:

…metrics are informational and focus on tracking and counting past data. Metrics look at tangible data that are easy to measure and usually of lower value. Metrics tell us what happened.

Steps 4 and 5 are where the actual analytics begins to occur. I defined analytics here:

Analytics, on the other hand, are strategic and look at both past and present data using mostly intangible data that are difficult to measure and of higher value. Analytics are very helpful with gaining important insights and predictions. Analytics tell us why it happened.

In order to be able to negotiate resources for your HR department’s programs and projects, you need to know and be able to explain why, what, and how your department contributes value to your organization. You need to be able to defend and explain the value that you produce to the organization in order for them to justify the funding you want and need. If you can explain the value by using the language of the business, metrics and analytics, you will have a much better chance of earning the funding and/or keeping your programs and projects.

That’s smart business and HR must learn to think this way. That’s why I love Jac Fitz-Enz’ books and that’s why I’m working on this Metrics and Analytics Series. HR needs to fully embrace metrics and analytics and learn how to comfortably speak the language of business. That’s the only way we will be taken seriously by senior leadership and have a positive impact on the organization’s financial and business objectives.

A simple and common example would be to look at the quality of a hire measurement once we fully understand the cost per hire and time to fill data. The question is, however, how do we measure the quality of a hire?

Another great example is with training programs and how relevant is training to an organization? Are the trainees doing a better job because of the training they received? How do we measure this?

We have to be able to figure out how measure these things because putting value on work without any supporting data is ineffective and dangerous. Training programs are often the first programs to be cut when there is an economic downturn because there was no data supporting their value to the organization.

That concludes this week’s entry in the series. As I continue this series I will explore the methods measuring things such as quality of hire, quality of training, and many more that are important and relevant to HR.

Introducing the HR Metrics and Analytics Series

First Entry in the Metrics and Analytics Series

Notepad with hr analytics on a wooden backgroundI’m a big proponent of the importance of HR metrics and analytics. In order for HR to be taken seriously in the business world, we have to be able to speak the language of the business. We need to translate what we do in HR into metrics and analytics that can be presented to and understood by the senior leadership of our organizations.

Business uses numbers to explain itself and we need to use numbers to have leadership understand how HR can positively impact the business. By analyzing the data we gather and putting it to use by helping senior leadership make important strategic decisions based on that information makes HR a critical business function.

We want to be taken seriously by the leadership in our organizations and the only way to do so is to speak the language of business whenever we are communicating professionally with them. To be a true strategic partner, we need to provide them with information and data that helps them see the strategic value in our role as an HR leader.

So with that, I’m starting a series of blog posts and podcasts focusing on the importance of HR metrics and analytics. I will focus on one or two topics per post/podcast and explain how they are calculated, why they are important, and how they can be used in analyzing those numbers to benefit the business of the organization.

The goal in this series is to help us better understand the different metrics and analytics and how to apply them in the real world.

I will explore the metrics and analytics that I have effectively used and some that I see value in and would like to employ in the future.

Jac Fitz-Enz has been writing books about HR metrics and analytics for many years and I strongly recommend them for anybody who wants to dig deep into this important subject. Much of the content for series will be taken from the Fitz-Enz books as I’ve gained most my knowledge from them. I’m re-reading and studying them more closely for the purpose of this series.

My goal is to explore the many facets of HR metrics and analytics and share my knowledge, experience, and opinions. Today’s post is to set the stage for future posts.

First let’s define the types of data that will be used. There are three types of data – structural, relational, and human. Structural data tells us what assets we own. Relational data tells us what our customers and other stakeholders need or want from us, and human data shows us what our only active assets are doing to drive the organization towards its objectives.

Understanding how these three types of data relate to each other and how they support and drive each other will help us make better strategic business decisions about the future. I will cover this as we move though the series.

Second, I want to define metrics and analytics. Many in HR are confused by the terms and often use them interchangeably. They shouldn’t because there is a distinct difference.

As Fitz-Enz says, metrics are the language of organizational management that HR needs to be able to speak in order to make an impression on senior management. Analytics are the communication tool that brings together data from many different sources to establish a cohesive, actionable picture of current conditions and likely futures.

To put it another way, metrics are informational and focus on tracking and counting past data. Metrics look at tangible data that are easy to measure and usually of lower value. Metrics tell us what happened.

Analytics, on the other hand, are strategic and look at both past and present data using mostly intangible data that are difficult to measure and of higher value. Analytics are very helpful with gaining important insights and predictions. Analytics tell us why it happened.

Here are some examples of the difference from the Workforce Dynamics blog:

Talent Metrics (HR): How many top sales reps left last quarter?
Talent Analytics (Business): Why do my top performing employees keep leaving?

Talent Metrics (HR): What is the average compensation for engineers across the organization?
Talent Analytics (Business): Why are our top software engineers dissatisfied even after we’ve given everyone a department-wide raise?

Talent Metrics (HR): Who is next in line to become our CEO?
Talent Analytics (Business): Will the CEO candidate align or conflict with the rest of the executive team?

So now that we understand the three different types of data and the difference between metrics and analytics, we can focus on how to apply this information to strategic business decisions as I progress through this series.

I’m very excited to start this series and I will publish posts & podcasts often as it is one of my favorite HR topics. If you need me to clarify something or want me to discuss a particular topic related to metrics or analytics please comment below.