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.

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

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