The world is becoming more and more data driven every moment, brought on by advances in technology, cloud computing, and more demand for data-informed insights.
This demand is not restricted to only understanding, markets, customers, or systems. In fact, demand for data informed insights about an organizations most critical asset, its workforce has been growing steadily.
In the Deloitte 2018 Global Human Capital Trends survey, 85% of respondents rated “people data” as “important” or “very important”, tied for #1 with the need for cross-functional collaboration from the C-suite.
However, only 42% indicated that they were ready or very ready to meet people data expectations. Between 40 to 80% of a company’s revenue is spent on the workforce. Yet it remains one of the least understood resources.
Amid the current Coronavirus pandemic, we are seeing this demand and urgency to analyze information about people and work skyrocket. Analytics on people and work data enable organizations to measure the impacts of the new ways of working such as mass telecommuting, work from home policies and effects on individuals in terms of social interactions, collaboration, isolation and burnout.
There is an increased demand for data informed planning to ensure appropriate staffing in the current environment and planning for a “new normal” of work trends. Organizations are asking questions around productivity, employee well-being and sentiment that, when working from a distance, are reliant on data driven approaches to gathering information.
While consensus for the power and need to analyze data about people and work is clear now more than ever, there is not so much clarity when it comes to terms and definition.
When HR organizations set out to unleash the power of analytics to address issues and create opportunity, they are met with a barrage of terms and titles. Some used interchangeably, others argued as being totally different and unique.
A google search or a scroll through books on Amazon will yield a mix of terms for the process of analyzing people and work-related data. Terms and titles like “People Analytics” “HR Analytics” “Workforce Analytics” and “Human Capital Analytics” come flooding in. But, what do these mean? Are they saying the same thing? Are they different? Which one should I use?
The truth is that this whole approach to analyzing people and work data for insights is still relatively new and every individual and organization approaches it in different ways.
While people and work have been studied for thousands of years, the type of analytics, access to data and technology has enabled a way of looking at this topic in a way that is still relatively new.
Unsurprisingly, many of the terms and definitions are still being formed and defined in this new field of study. So, a little bit of confusion is natural at this point. That being said, there are many individuals like myself who have been in this field for a long time and believe each of these terms convey a slightly different concept.
Here is how I have come to understand and define these multiple terms:
Some might want to say that HR analytics is just any set of analysis conducted by an HR person. However, if being a bit more strict about definitions, HR analytics would be defined as analytics for the understanding of HR related processes and techniques.
Typically conducted for the sake of improving or enhancing HR practices. A great example is the use of analytics to better understand the recruiting process and help increase processes like the time it takes to hire an employee.
Human Capital Analytics
Human capital analytics emerged out of the more academic study of human capital. Human capital is defined as the stock of competencies, knowledge, social and personal attributes, including creativity, effort and cognitive abilities that contribute value to an organization or society.
Analytics with this specific focus tend to home in on the concept that people have value, often translated into monetary terms, that they bring to an organization (or to society) through their contributions. While it is specific to value that is brought about by people, it is not necessarily limited to only direct work outputs nor does it technically include other aspects about the person or the work itself.
Workforce analytics, unsurprisingly, starts with a focus on the work itself. Workforce analytics focuses on all aspects of work and may, but does not necessarily, involve the study of people.
While work is often done by people, workforce analytics extends beyond a person and also includes the understanding work processes, planning for future work, transformations in ways of working and may include work not done by people at all, such as automation or robotic processing of work.
It follows then that people analytics starts with a focus on people. While work outputs such as productivity would be included, it extends beyond work alone to measure things about the individual.
This can include analyses of topics like work life balance, well-being and bias. For example, in the current coronavirus environment, HR leaders are asking questions about whether employees may have additional stressors or support in their home-lives to help them through difficult times.
These types of analyses do not always necessarily focus on work, value or HR processes but are undertaken to enable better decisions about people.
While I have defined each term distinctly, it is important to note that these terms do overlap. Some of the confusion may arise when a project overlaps between many or all these concepts.
For example, a study to measure the efficacy of a training which was designed to increase work output of employees would meet all the above definitions. It would analyze the HR process of the training program, measure people in terms of productivity and learning, identify the impact on increased work output and the value to be gained through increased human capital.
Many of the analytics activities you may undertake when working with data about people and work will be interrelated and could be classified under more than one of the definitions.
So, which term should you use? Now that you have clarity on all these definitions, I aim to leave you with two pieces of advice:
1) Ignore these definitions; use whatever term you want!
Whether you are focused on HR, value, work or people specifically in the type of analytics you undertake, I am sure that your end goal is the same. You are looking to find insights that will help you be better informed, make decisions and drive value.
If you are searching for a term to use, select the one that feels best suited to you and your organizational needs and do not feel that you have to restrict yourself to one single area of analytical focus.
You also should not feel that you have to use the ‘correct’ term to explain your type of analysis; there is not “right” or “wrong” when it comes to how you choose to label your use of people and work data to drive insights. The more important thing is that you are leveraging the power of people and work data to drive those insights.
2) Embrace a common definition for all these terms.
I would like to leave with you with one final definition. A definition that you can use with any of these terms.
Here is my definition for HR/Workforce/Human Capital/People Analytics: The application of data and insights to improve outcomes through better decision-making regarding people, work and business objectives.
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