In descriptive analytics, historical data is examined using summarizations, patterns, trends, and relationships to determine what previously occurred. It uses methods like data aggregation, basic statistical analysis, and data visualization to answer the question: “What happened?” 

As a people analytics example, let’s imagine that a company wishes to know how much employee attrition occurred this year. All organizations gather and retain data that can be analyzed to answer this question such as, employee headcounts, job details, hire dates, and termination dates, and many also ask exiting employees their reasons for leaving the company. This data can be used to calculate an annual attrition rate which is a simple type of descriptive analysis that would  answer the question. [If you need to first get a quick understanding of the various terms and approaches surrounding employee attrition, here’s a short video where I talk about it: Attrition, Turnover & Retention: What’s the difference? Plus How to Calculate Attrition.] 

The analytics could stop with the calculation of an attrition rate and the question will have been answered. But, as we discussed in step 2, successful people analytics professionals are curious and creative so they usually want to know more. In addition to the rate, descriptive analytics allows you to describe even more of “what happened?” For example, this year’s attrition rate can be compared to prior years to identify changes or trends over time. Or, rates can be compared across different groupings of the organization to identify areas with higher or lower attrition. The length between hire dates and termination dates can be used to describe the typical length of employee tenure. Employee attrition can be categorized by leaving reason to identify potential areas for improving retention. And, these are just some of many possible examples for how a curious people analytics professional might use descriptive analytics to gain insights about what is happening regarding attrition in the organization. 

Descriptive Analytics Skills

To understand what happened using descriptive analytics, focus on building the following skills:

Start with understanding key statistical concepts like measures of central tendency (mean, median, mode), frequencies, percentiles, measures of dispersion (minimum, maximum, variance, standard deviation), and correlation. Many online resources offer free courses and practice problems. Seek out how-to articles and tutorials until you can complete each of these statistical techniques easily in the tool(s) you are most comfortable using. Note, while you can learn the math behind each of these statistics and calculate them by hand, I definitely do not recommend that! In people analytics, you will usually be working with digital files so it can be more efficient to use existing functions that calculate these for you in platforms like Microsoft Excel, Tableau, R, Python, or other available data tools and focus your time on learning which statistics to run and how to interpret them – we’ll talk more about tools and technology later in this chapter.

Basic summary statistics provide a wealth of information that can help decision makers understand and answer key questions about the workforce. Consider a manager who needs to hire a new, additional team member for position x but they are unsure what the salary should be for the position. Calculating the median salary among all current employees in position x gives the manager a reference point for a typical ‘middle’ salary for that position. The minimum and maximum salaries can be used to identify a range of salaries for that position and can highlight any current outliers. Correlation can show if employees with more work experience tend to have higher salaries, helping the manager decide if requiring more experience is justified for this role. And, once the manager has a potential salary they are considering, they could use percentiles to explain how the new salary might compare to the distribution of current employee salaries. If the manager decided on a salary that is at the 50th percentile, they would know that the salary they had been considering is greater than 50% of the salaries of all current employees, which may be an important consideration before finalizing the job posting. A manager informed with all these descriptive analytics will be better situated to make a well-informed decision in this scenario. This salary example is just one of many ways understanding what happened can provide insight to support decisions.

Explore & Engage: 

Even with data and statistics, the insights won’t just find themselves. For that, you need to pair the following techniques with your descriptive analytics skills:

• Identify key metrics: Understand what questions you want to answer with your data. Before you jump into running different statistics, start by asking yourself, what do I want to know? What are the best metrics to use? A great approach is to first research common analytics used for your chosen topic. Things like headcount, attrition, well-being, collaboration, innovation, hiring, etc. have all been analyzed before. So look for examples from others and try to replicate them. Start with a goal for what you hope to describe, then apply analytic techniques until you feel you have a clear answer.
• Learn to interpret: Simply calculating a statistic or producing a chart won’t lead to action. With each analytic skill you learn, go beyond simply learning how to calculate or create it, learn to interpret and explain it effectively – analytics require context-relevant translation to be meaningful.
• Practice with real data: If you have access to people data files, do all your learning and practice on them! If not, you can find open datasets related to human resources and business topics that include mock or anonymized people data from online platforms like Kaggle and many university’s public data repositories.