Organization Horsepower

We all understand from a business perspective the concept of getting returns. We spend dollars in order to make more dollars. However, as an employee of a business sometimes the concept of “returns” gets a little cloudier. Sure, we all earn a paycheck but sometimes it’s hard to see in the “here and now” how helping our organization makes more, benefits us and it rarely shows immediate returns in our paycheck. So when we talk about “Return On People”, it’s really easy for employees to relate that concept with just another way for employers to make more money off harder work, more hours, or fewer benefits.

As much as adopting a Return On People mindset is about making more back from your investments in people, it doesn’t work if those people can’t see or don’t understand where they fit in, or what they will get out of it.

A huge component of getting better Returns on People is increasing the amount of time spent on activities that generate profit, as well as decreasing or eliminating activities that have no measurable, or even a negative impact (more cost), on profitability. While the benefits to the employer of better job design are obvious, the net effect for employees is better, more meaningful jobs.

If you asked one of your employees how much time they spent doing actual work in a day (and they answered honestly), what would they say?

It is safe to say the answer would be less than you would like, but the real challenge is identifying what they did with the remaining time and why they didn’t consider it “real” work. Chances are at least some of the time is doing activities that is a requirement of their job that they just don’t see value in.

We all want employees that are engaged with work. It’s much easier to get that level of engagement when the majority of the time spent at work is on activities that have meaningful outcomes and align with that individual’s skill sets. It creates an atmosphere of value. It says to the employee that you value what they are good at far more than the activities that don’t produce returns. You have involved them in something larger than just themselves, and have shown the relationship between work and the success of the company.

Conversely, if we treat employees as costs and draw direct-line relationships between that cost and productivity, that productivity will be limited to the spend associated with it. If they don’t see you trying to make meaningful work for them, your get just what you paid for, labor in exchange for money.

Paying attention to Return On People isn’t just the right thing to do for your business, it’s the right thing to do for your people.

Back in 2011 I wrote series of articles on measurement that focused in performance-based measurement for training professionals. At the time, Media 1 was focused in on the transformation of the training function to a performance mindset. In subsequent years, we’ve reframed that performance mindset for the HR professional. The following is an update on my thoughts from the original post.

Far be it for me to hold back on how I really feel about something. So, here goes:

Measuring HR as a justification for HR is an utter waste of time.

It’s like giving style points to the 50-yard dash. It may be interesting, but the only thing that matters is who crossed the finish line first. In other words, the performance or result mattered; the style in which it was achieved is barely noteworthy. Yet, when you measure HR in and of itself, that’s exactly what is happening.

I think Charles H. Green hits it on the head with this quote from his blog:

“The ubiquity of measurement inexorably leads people to mistake the measures themselves for the things they were intended to measure.”

Why do we keep using measures instead of actual performance as justification to ourselves and our organizations? The answer to that question in many cases is rooted in why we are asked to measure HR in the first place… that is, to prove that it has some kind of meaningful, measurable impact on the organization’s results.

Many of our organizations do not believe that HR as it is currently defined contributes to profitability. Or they do not trust that you or your immediate organization can execute HR in an impactful way. The requirement for measurement comes from a place of distrust—not from a defined need to measure results. Consequently, measurement is demanded to “prove” HR has value. Trust is not impacted or improved through this exercise, but regardless, time and effort is spent generating measurements that don’t really tell us anything about the business.

It is not my intent to write a primer on the effects of trust in business. I think Stephen M.R. Covey has done a good job with that in his book the Speed of Trust and the follow-up Smart Trust. The point is that a lack of trust affects our relationships and results in demands for measurements based on volume that are intended to justify the existence of HR in an organization. It’s a closed loop with no obvious business value. That’s why old-school HR departments are usually viewed as a cost centers, not as a strategic business partners or even a source of predictive intelligence.

So how do we as HR professionals earn trust and show that HR can be a source or profitability within the enterprise?

In short we have to make the paradigm shift into measurements that help the business make better, faster decisions based on the analytics of human performance. When business sees we are measuring things that concern them and aren’t self-serving, then that’s a great first step in assuring the business that we’re all in this together.

It’s easy enough to come up with meaningful HR generated data that should be meaningful to business stakeholders.  After all who wouldn’t want to know revenue and/or profit by FTE, loaded costs by function, or engagement levels? The problem is that those measures while valuable may not align to what is most important right now. The first step in aligning the measurement of HR to the needs of the business is to align to corporate goals and initiatives. From there we should be able to identify specific measurements or Key Performance Indicators (KPIs) when building the case for an initiative.

Resist the temptation to use measures based and volume and time alone. These metrics generally serve as justification indicators, but fall short of a true performance benchmark.  Good KPI’s rely on multiple sources of data that allow for calculating true costs and profitability. At minimum good KPIs support direct correlation to cost and profitability.

Once your KPIs have been identified, you are well on your way to generating a measurement of your current operational state, also known as a baseline.  Now all you need to do is figure where you left all that elusive data…

I recently worked with a client that was very insistent on getting benchmarks from a fairly narrow scope of companies on a specific set of cost measures.  I found out that getting benchmarks from the really high performing companies is often difficult because they believe that they have achieved something with their measurements and that it is a competitive advantage that they don’t necessarily want to let out of the bag. I also found that companies don’t like to disclose cost information unless it’s a pretty nebulous range.

However the good news is that the benchmark isn’t the end-all-be-all. Don’t get me wrong, a benchmark can be important, but don’t get it confused with a goal. Benchmarks are best used as indicators; they can tell you how you compare to other companies, and if the gap is wide enough, it may help you prioritize an initiative. However, while it’s a good start it should never be used as an indicator of the end.

You don’t stop when you hit average, so don’t stop when you hit a benchmark. No one would ever be “best-in-class” if they just shot for a benchmark.  That doesn’t mean chase perfection without payback either, last time I checked best-in-class didn’t come with a guaranteed paycheck. Performance improvement has to always be scaled to match the potential return and the investment needed to get there.

It’s OK to look at a wider range or larger sample size for your benchmarks; they are only one indicator of many you’ll use in evaluating potential opportunities for improvement. Ultimately you’ll need to set achievable goals that meet your internal capability, that’s the measurement that will matter.

The first programming class I ever took, I was introduced to the acronym G.I.G.O. For those of you who have never had the distinct pleasure of such a course, it stands for Garbage In, Garbage out. The premise is that if you input bad data, the only result you can get out is similarly flawed.

Traditional HR does not have a heavy emphasis on data or statistics. We ask finance for numbers, or we run a report and generally trust what is there. Or we like and trust people, so we ask them for numbers. However, as we strive to strategic if not enabled business drivers, we need to pay more attention to where our numbers come from and how those data sources affect the baselines that we use to measure progress against.

I recently had a client provide me with baseline cost data that clearly showed a disproportionate spend on one particular area of operations. Without getting into a lot of detail, it turns out that baseline cost included quite a bit of misplaced expense that significantly bloated the baseline. After removing the added expense, the project couldn’t be justified on a cost basis alone. It was still critical that the client generated a true baseline, but not having an accurate picture up front really took the wind out of everyone’s sails.

The reliability of your baseline data can be directly linked to the maturity of your data analytics culture and the tools you use. There are four primary sources of baseline data listed here from least reliable and mature to most.

Conjecture, anecdote, and correlation – probably the most utilized source of baselines, this method involves making educated guesses about the metrics you use for baseline calculations. This approach is best characterized by using very small sample sizes, and applying logic. If it takes this long to do a known quantity, then this other target quantity is x% of that time. While useful for napkin calculations, this type of data is highly subjective and has credibility problems. At some point someone will question the validity of numbers and it becomes a trust game.

Survey/poll – When you don’t know something you think you should, its human nature to reach out and ask someone that you think should know. In HR terms that means asking a group of employees about the activities they perform. This can be as simple as an email or complex as a survey. When using this type of data to form a baseline you have the benefit of a larger sample size (assuming you get enough responses), but you are essentially either asking an employee how they feel about something or what they imagine something is. There is no way to validate the data and it leads to interventions that are centered on changing the way some one feels instead of changing the way they think and consequently act.

Activity logs – If you aren’t measuring something that you really want to measure, the best way to fix that problem is to start. If you wait to measure until you’ve already changed something you missed out on a lot of good data and it really didn’t help you generate a baseline. In the simplest terms, start asking people to track what you want to measure. Think of it as a naturally occurring experiment. If you want to know the data state of the current state, start logging some data. It will take a while to generate enough of a sample size for it to be reliable data, which means it’s not a great technique to use when you’re already under the gun. However, more mature organizations anticipate and proactively measure.

Enterprise systematic – Of course the best way to generate a baseline is on reliable data that is tracked systematically across your entire sample size over a long period of time. Look for sources of this type of data in your organization first, ERP, timekeeping systems, and payroll are all rich centers of reliable baseline information, but typically require set-up and configuration to get exactly the type of data you need for good baselines.

At the end of the day, having one singular source of truth for your baselines is something to aspire to. If you’ve already got it figured out, my guess is that you haven’t read enough of this post to get to this point. The reality is that as we get started with measurements, metrics, and analytics we have to develop and plan for our baseline data sources. The better the data we get in, there better data out puts we’ll be able to produce, and the more we can help our companies move forward.