Innovating HR Analytics From Within – Lyndon Sundmark
Lyndon Sundmark has been very active in the HR analytics space for the last few years. He has shared his passion for analytics in multiple articles and participated as an instructor in one of our online courses.
Earlier this month, he has written a book titled ‘Doing HR Analytics – A Practitioner’s Handbook With R Examples’. This is the first book of its kind that shows how to do HR analytics using R.
We sat down with Lyndon to talk to him about his book.
Hi Lyndon, some of the readers already know you as the R-guru. You wrote one of our most popular blogs, titled employee churn analytics. For those who don’t, can you briefly introduce yourself?
First of all, I don’t consider myself as an R guru. Perhaps it’s the ‘application’ of R and statistics being applied to HR information that might seem ‘guruish’.
I guess the best way to describe myself is as an HR professional who was given an opportunity decades ago to apply statistical analysis to HR information to solve research questions.
I had picked up 3 separate interests in the MBA program – HR, Information Technology, and statistical analysis – and I thought I would have to choose between them for a career. A research assistant role with a few profs during the summer changed that. I began to realize that HR could be my main career, but that Information Technology and Stats could be indispensable tools and skills sets to support that.
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All of my career roles used some mixture of the above 3 knowledge areas.
You once mentioned to me that you’ve been working in HR analytics before it was called HR analytics. Can you tell us more about your work experience?
The building blocks for HR analytics have been around for decades.
HR analytics is, in essence, being ‘data driven’ in your HR management and decision-making. That happens when you apply statistics and data science to the field of HR and to HR information.
HR has been around for decades, statistics have been around for decades, and information technology has been around for decades. The ability to integrate these together have been around for that same period of time. Each of these has evolved significantly over that period of time.
I think what’s different is:
- The degree to which the integration of these has been recognized recently
- The degree to which the capacity to generate information and store it has increased
- The degree to which CPU processing power and memory have increased
- The degree to which statistical methods have evolved hugely, particularly predictive modeling
- The degree to which costs for all the above have been driven downward
All these factors contribute to the democratization of the ability these days for any individual or organization to engage in this field and activity.
In that light, I have been integrating the use of these fields together since 1980 – even as the technologies and methodologies have evolved and unfolded.
We often talk about descriptive, diagnostic, predictive, and prescriptive analytics when it comes to different types of HR Analytics activity. Historically, descriptive and diagnostic have been most in evidence since the 1980’s and even before. The predictive and prescriptive have exploded in terms of options and choices more recently.
So, the building blocks have been evolving for quite some time. It’s the differences mentioned above reaching a critical mass stage, that I think account for its current popularity and visibility. The ‘applicability’ has always been there as the fields evolved.
What drew you into the field of data-driven HR?
Throughout the years as the building blocks were evolving, what attracted me to these, was understanding that data and analysis were key to measurement, monitoring, and understanding in real time what is happening to an organization’s human resources.
A key thought which I had heard early in my career was ‘You cannot manage what you cannot measure’. This thought has been a stumbling block for a lot of people.
Personally, I took the truth of it at face value. As a result, I committed myself to learning HR not only in the traditional way but also to look at all of HR from a business process and informational mindset. I wanted to know what each HR function looked like as a business process, what its inputs – steps – and outputs were, what information was generated at each of these stages, what was done with the information, and how you could measure them.
Seeing the field of HR from a perspective that many others weren’t (and possibly in still cases still don’t) is what drew me to the field. When you are seeing things in data for the first time, and perhaps the only person currently seeing them (because you might be the only one looking) becomes quite exciting and motivating.
We do this interview because you recently wrote your book Doing HR Analytics – A Practitioner’s Handbook With R Examples. What prompted you to write this book?
About 3 to 4 years ago as stuff was being written and starting to gel around HR Analytics, I started paying attention. I was finding that the kinds of things being written were describing bits and pieces of building blocks that I had been using since the 80s.
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I also became increasingly aware particularly that the advances going on in the technology world and data science world was in many ways leapfrogging ahead of HR’s capacity to understand and to use these advances. Other areas such as sales, marketing, and finance were embracing these advances but not HR.
Because HR Analytics is just the application of data science and statistics to the HR context to help solve HR problems and address HR issues, I became motivated to be at least one voice in this area.
I’m probably not the first and definitely won’t be the last. The intent of the book is to show some examples of how HR Analytics can be applied in organizations, as well as to help the reader structure their thinking around this subject.
Is this what makes this book so unique?
At present, many books written on HR Analytics talk around it, about it, and its significance and importance (which is important when the field is emerging).
But if books don’t show how analytics can be practically operationalized to address real life organizational HR issues and problems then it may end up as the latest HR and management fad. And this, even before it has the opportunity to bear fruit for an organization.
This book tries to give practical examples. It goes beyond talking the talk, to walking the walk.
Obviously as time goes by and others write ‘how to’ books, this book will be less unique. If this book gets an organization living and breathing their own People / HR Analytics journey, going beyond just the talk – it will have achieved its purpose.
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