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.

Please note: I reserve the right to delete comments that are offensive or off-topic.

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