Getting Started is Hard
You’ve heard of People Analytics and you’ve an idea, generally, of what it is. So now you’re out there searching for more about it, how to do it, and looking for ideas of where to start. I had the same troubles. Coming from a medium-sized company with a rapidly maturing reporting function, but no advanced analytics function at all, I had plenty of energy but was short on know-how. This serves to document my own learning while aiming to help – and even learn from – others.
Defining People Analytics
Spend enough time looking and you’ll find as many definitions and approaches as sites you’ve visited. My experiences have led me to believe that how you apply data to your people decisions is People Analytics.
Over time, your skills and your function will advance. As the months pass, you’ll evolve your definition. For now, your what feels right to you. It’ll change, be good with that. Here’s one I like to use:
People are hard. Data can help. Data can make things better for people. Let’s make things better.
People Analytics Ain’t New
If we weren’t talking about something in the domain of HR I’d have to exclaim that we’ve been duped with a fresh coat of paint and a new name straight out of Silicon Valley. But really, this has been going on for a long time, just out of the spotlight that’s now been shone this direction.
I love history. Here are some fascinating examples proving People Analytics is something that’s been used for decades, with some new packaging and much fancier tools.
Frederick Taylor
In The Principles of Scientific Management, Taylor writes about his study and formation of what he referred to as “Scientific Management”, largely developed during his time at the Bethlehem Steel Company. Taylor devised scientific methods for selection, training, and development of workers. His analysis concluded that when the right workers were selected and trained, that a wage of up to a 60% percent premium compared to the local market average, would “become more sober, and work more steadily.” Further, he found that when paid more than a 60% premium, the workers output became “irregular”. Principles of Scientific Management
Walter Dill Scott
During World War I, selection and placement of servicemen was critical to the success of the war effort, yet it proved difficult and inefficient. Scott devised an evaluation to rate the potential success of each service member. When this evaluation was rejected, Scott took a challenge to run the test against already successful officers. When his results matched what commanding officers already knew, the tests were quickly implemented throughout the Army. These tests were modified from the tests he wrote about in Increasing Human Efficiency in Business where he applied them first to job applicants. His study extended to assessments to evaluate candidates for promotion and to match skill sets with unique positions that required them. He was awared a Distinguished Service Award for his contributions.
Google rightfully receives and deserves much of the credit for the modern movement to bring data into HR. They just weren’t first.
Way back in 2007 Google was beginning to apply what their business centered around, data, to their people. One of the first projects was to figure out why new mothers left at nearly twice the average rate. Since then, Google has applied People Analytics to problems including diversity, hiring, leadership, workplace design, and retention.
Reading Work Rules! by Laszlo Bock for me was like reading Dan Brown’s The DaVinci Code – a guilty pleasure I (finally admit publicly) just couldn’t put down. I was hooked, inspired, and eager for the weekend to hurry up and end so that I could get to work and get started. (Hey, boss!)
Present Day
Where Google was in 2007 is still where some companies would like to be at 10 years later. Google was a pioneer in the modern age, but they’ve shone a light on what’s possible, and they aren’t shy in sharing. I to this day continue to re-read some of what Google has done. That team has been amazing, and true to their mission, they have made access to this information universal; truly a first considering this is HR.
It’s amazing to think of how the scale of data has grown from where Frederick Taylor started. Think of the data that organizations are sitting on. Mountains! It’s likely messy, and it may not be all the data you’ll ever need (it’s not – you’ll get there), but it’s more than you can handle right now.
Your organization’s HRIS, ATS, LMS and every other acronym/system have years or decades of information. Performance reviews – check. 9 boxes or whatever you’re calling that – check. On and on. You’ve got many datas.
Start including social networks, email, chat, personal trackers, phones… As you start to think of how to reign in, and ultimately process all of this, you’ll feel like I do about it: a bit unsure, but damn excited to try.
Data-driven decisions
Ask yourself:
- Would you hire someone without interviewing them?
- Would you make them an offer without bench-marking compensation for their market and experience?
- Would a Marketing department blindly spend ad dollars without first determining who/where their customer is?
Not likely. Honestly, I hope not. Just don’t do that.
There are reasons HR hasn’t implemented a data-driven approach. Notice I didn’t say good reasons, mostly just reasons (alright – there are a few good reasons, I’ll come to this).
To start, these aren’t skills you find in a HR department. HR professionals are very talented, and necessary – I want to be clear about that. But they rely on soft skills. Data is viewed as hard numbers. In school I always preferred math to English. With math, I knew whether I got the answer; I was right or I wasn’t. English just left me confused. Yet, professionally I found my way into HR. Fortunately, math is coming to my rescue.
For the “old guard” in HR departments, data-driven decisions is scaring them. There’s a fear among some that the machines are coming to take their jobs, and that the algorithm makes the final decision. While I generally leave the definition of People Analytics to you, my recommendation is that it is more than an algorithm applied to people. Algorithms will discriminate, they are biased, and they can be wrong. Well, the math can be wrong, but algorithms don’t get culture. Maybe they will and maybe we’ll be living on Mars. Anything is possible; maybe just not likely.
The truly cool part about what analytics provides is a evidenced-based approach to the intuition that HR has historically provided. They can and do work well together. It’s a marriage. Like any marriage it’ll take some effort. You’ll have to find how to make it work. You’ll have to listen and grow together. Good things are worth the effort. Read Why Use People Analytics for more.
“Most of the world will make decisions by either guessing or using their gut. They will be either lucky or wrong.” Suhail Doshi
People Analytics is a Journey
There are many approaches to People Analytics, and varied potential applications. There is no standardized function, no road map, and no “one size fits all” approach. Getting started can often be the most difficult part of the journey, it’ll feel like your pushing uphill for the first bit. Akin to the launch of the space shuttle, an incredible amount of thrust can be required to put in place an analytics-based approach within HR. This is becoming less the norm, but still an honest assessment of what may lie ahead.
It’s a journey. Every journey begins with the first step. Do not worry about what others say or think. Some will say reporting isn’t analytics. I somewhat agree, but I much more disagree. If it’s the first time you’re seeing some information, and that information provides solid evidence to support a decision, who cares if it took a PhD or Excel? If your people are better off, you’ve delivered.
Your first effort in People Analytics may be cobbling together headcount and attrition across your organization, broken down by department or levels. The more advanced and mature practitioners are well-beyond this, but everyone began with their first step, the first project that led to their first insight. It may be as straightforward as showing at what point in the job level hierarchy do the greatest percent of associates exit. Is it your managers, your 4th-year associates who haven’t been promoted? That’s insight. The journey has begun.
Now is the Time
People Analytics – the name might be new, but the concept is not. You could argue the stakes have never been higher – “the war for talent” is a constant focus and challenge for HR. Despite the stakes being higher than ever, the barrier to entry is far lower than it was when Google booted up their People Operations team over 10 years ago. The tools, techniques, and support for People Analytics has grown tremendously since that time. Whether you’re just getting started, or continuing to mature in your People Analytics journey, the time has never been better. The spotlight in HR is on you.
Oh, and it’s a helluvalot of fun…
Photo by Samuel Zeller on Unsplash
