January is the best time to look back at the previous year. That’s what we do in this article. Read about 5 top people analytics stories of 2016 – and some more.
In December 2015 David Green predicted that people analytics would (finally) go into orbit in 2016. The conversations David had with HR leaders suggested that people analytics was on the brink of exponential growth. And although people analytics hasn’t reached a tipping point yet, it is definitely growing exponentially.
Around the same time David wrote his prediction, my co-founder Nando and I were exploring business interest in a specific people analytics proposition. We noticed that almost any HR professional was happy to talk to us whenever we mentioned HR analytics in our emails.
Despite the interest in HR analytics people didn’t really know what it was. Every time Nando and I visited a company, we explained what HR analytics was and how it could help them.
These company visits taught us that interest in the topic was astronomical. This was one of the reasons why we decided to found Analytics in HR.
Our aim was to create a knowledge sharing platform and community for HR analytics professionals. We posted our first blog in June 2016.
It has been a great ride ever since. In this article, I will share our top 5 people analytics stories of 2016. I will also include a few of my personal favorites that didn’t make the list but that you definitely should check out.
Top 5 People Analytics Stories
Employee engagement is a hot topic amongst HR professionals. Commonly asked questions on the topic are: What is the added value of engagement? Can we show the impact of engagement on business performance? If so, how do we do this?
This article answers all these questions.
It also contains a conceptual model that shows how customer engagement can lead to better service performance, happier customers, lower customer churn and higher revenue.
The thing that makes this article unique is the level of detail on how one should define variables. For example, there are many ways to measure performance. Before you start with analytics you need to define how to measure it.
After making this decision, it is important to start small and to relate every outcome to the bigger picture. This helps to measure the impact of engagement on business performance.
This tutorial is one of the articles I am most proud. It describes how to analyze employee churn using R and is written by the brilliant Lyndon Sundmark.
The article offers a step by step guide on how to analyze churn using R and links to a unique and detailed HR data set. All commands and R libraries are provided. By copying the steps you can recreate the analysis – and even apply it to your own data.
The tutorial validates a set of hypotheses on the data and shows even how to predict employee churn before it will happen.
All the color coding of the programming lines was manually done by Nando based on Lyndon’s input. It took him a long, longtime but it sure was worth it!
Full article: A Tutorial on People Analytics Using R – Employee Churn.
This article was originally titled 5 HR Analytics Courses Online but we recently added two newly released courses.
A lot of people who subscribe to our weekly newsletter ask us how they can learn more about people analytics. This article is a great place to start.
The list includes the excellent Wharton People Analytics course, but also an introduction to R and an extensive course for data analytics using R from MIT. For those not schooled in statistics there’s one in basic statistics. For those of us who don’t have a background in HR, there’s a course on the key principles of HRM. In other words: there’s something in there for everybody.
Full article: 7 HR Analytics Courses.
People analytics professionals use a people analytics lingo that contains a lot of words that not everyone is too familiar with. Examples are machine learning, structured data, and overfitting. What do these terms actually mean?
This list both explains the terms and dives deeper in how they are applied in the people analytics’ space. It is easy to talk about predictive algorithms, but how are they constructed? And do algorithms that predict the future with a 100% accuracy exist?
They do, but they probably don’t work in real life due to overfitting.
This is because sometimes algorithms are tested on the data that was used to create the algorithm in the first place – a common but critical flaw.
These and more examples are used in this article. It even includes a brief description on how to build a decision tree!
Full article: 9 HR Analytics Terms you Should Know.
The inspiration for this article came from the book Predictive Analytics by Eric Siegel. I bought this book because it had a section on predictive analytics in HR. The section offered a great overview of the possibilities of predictive analytics and how it could be applied in HR.
Examples include deducing someone’s personality traits using his/her Facebook profile, how the U.S. special forces select the best job candidates and how Wikipedia predicts their greatest company risk: when its contributors will churn.
Predictive analytics is a game changer for HR, and – if you haven’t already – you should definitely read the full article: Predictive Analytics in HR.
If you want to know more about HR case studies, check out one of our article about 15 HR analytics case studies. These will show you what is possible using analytics.
I selected these articles based on total number of readers and quality. Some blogs got even more readers. For example, this article on 14 HR metrics examples. Because of its somewhat concise nature, I did not include it in this overview.
One article that hasn’t gotten that many views yet is the 5 reasons why HR analytics projects fail
In this article, I mention five common reasons for HR analytics project failure. These mistakes are often made and hinder People Analytics adoption.
The five reasons are:
- Biting off more than you can chew
- Lack of relevance to the business
- Not looping in compliance
- Bad data
- No translation to actionable insights
By creating awareness about these potential pitfalls they can be prevented. A side effect is that avoiding them creates a better chance of HR analytics adoption. Read the 5 Reasons why HR analytics projects fail when you are starting with HR analytics.
Memory Nguwi and his team’s series of three case studies deserves another honorable mention.
I especially like these because of their evidence-driven style. Memory’s team analyzes a business problem by checking the key people drivers to those problems – and then analyze them.
An example is his article on reducing workplace accidents. In this case study, he describes how he and his team predicted road traffic accidents for a transport company. Traffic accidents can cause human injury, cause delays, and involve material and transport damage.
His team looked at historic records of road traffic accidents and assessed the company’s drivers. By doing this, the authors came up with a number of factors that lowered the chance that a driver would be involved in an accident.
These factors were then introduced in the company’s selection procedure. This helped the company create a competitive advantage by delivering superior service and lowering accident costs.
The two other case studies focused on HR drivers of retail sales performance and on determining optimal staffing levels for a blue-collar firm. Both case studies include interesting theoretical frameworks, data-driven outcomes and rigorous hypothesis testing that provide valuable lessons for people analytics practitioners.
2016 was a great year for people analytics and for our website. It was not only our founding year but also a year of rapid growth. We are grateful to see an ever-increasing number of readers returning every week. Cheers to a great year and best wishes for the next!