The following is a video perspective on the blog post “Don’t Confuse a Benchmark with a Goal,” released last week:
The following is a video perspective on the blog post “Don’t Confuse a Benchmark with a Goal,” released last week:
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.
Based on existing theory about Human Capital Analytics and levels of organizational maturity, and thousands of hours of our own primary research, these are the levels of maturity that we believe every business leader will need to move his or her organization through in the process of transforming the Human Resources organization into an empowered driver of business strategy.
Let’s take a look at the levels of Return on People Maturity, starting from the lowest level of administrative processes and moving to the highest level, where HR dictates strategic decisions based on data about the organization and the market – a discipline that we refer to as People Analytics:
Traditional employee-focused Human Capital Management (HCM) is occasionally aligned with Line of Business. HR is a tactical, administrative, and human relations cost center that:
Strategic enterprise-focused HCM serves organizational leadership as well as employees. It focuses on a value-driven approach for the “Front office” HR services provided directly to the business. Other characteristics may include:
Enterprise-focused HCM converges around talent integration. It creates a single view of Human Capital value for leadership and the workforce and helps the organization make the best business decisions. This includes:
Enterprise-driven predictive HCM anticipates changing market conditions and the company’s ability to leverage human capital to produce bottom line results. Predictive Drivers promote:
Measured on its ability to help the company anticipate and supply enough skilled, engaged, and motivated employees to meet business needs and drive future business decisions.
Fully integrated continually improving Human Capital Management (HCM) in which HR:
These stages lay the groundwork to transform the HR function into a strategic player in the business. To request an assessment to find out where your organization fits into the maturity model, call us today at 616-935-1155 or email email@example.com.
Ever get a feeling something isn’t what it should be? Do you “know” that that a process related to HR or people is broken or inefficient?
Rarely do we get “go with our gut” in business. We need to start by having data, aka baselines. We need to know by the numbers where we are today before we can drive into the future.
Most people’s first reaction to gathering baselines is to “prove” that there is a problem and to justify an initiative. There is a place for that, but baselines need to be so much more. Imagine you create a baseline that shows a $1 spend every time a person performs an action. Then what? What does that data really tell you? In and of itself, you don’t know if spending a dollar is good or bad for that action.
Typically companies just add up those dollars until they add up to bigger dollars. Then they determine that they can spend a smaller number of dollars, and set off on improvement initiatives. Sometimes our operations are so broken that we get lucky and actually find ways to fix them. The danger as we mature is to keep applying that logic. But eventually we have fixed the process problems, and we find out $1 is just what it costs.
The problem with this approach of the use of baseline is that the need to improve is subjective. There is just a “feeling” that $1 is too much and there must be a cheaper way. Instead of looking at the baseline as an aggregate, you need to determine what makes up that $1, and what other data can tell you what part of that $1 you can change and ultimately if it’s worth it.
This is all a cost-centric approach. In the end, a good baseline tells us if we have moved the bar, and the bar should be measured in profit. Cost is only one factor. The control and service centered HR organization is measured in what it costs the enterprise. The strategic, enabled, predictive, data driven HR organization is measured by the value it helps the enterprise generate. In either case, you can’t quantify your contribution, or better yet, its effect on the business without a baseline.
The largest expense on any enterprise P&L is the money it spends on combined wages, payroll taxes, paid benefits, and unpaid benefits. This is fitting since most companies at least pay lip service to people being the most important and valuable asset that company has. While many will go to exhaustive lengths to calculate return on investment in software, hardware, and real estate, surprisingly few companies measure the return they get as a result of their spend on people.
Return on People is a philosophy and practice of calculating and projecting cost, revenue, and profit that is a result of a company’s spend on people. Return on People is measured using Human Capital Analytics. Human Capital Analytics is designed to measure people on an unemotional and equal business footing with other business measurements such as financial, operations, inventory and facilities. Management and improvement of Human Capital is the mechanism used to maximize Return on People.
For a snapshot of how well your organization is utilizing its most valuable (and costly) asset, a free version of our ROP Maturity Assessment will be available shortly. You can sign up to be notified as soon as it is available. Your ROP score gauges your organization’s readiness to make strategic use of Human Capital information. A high ROP score indicates your ability to earn high returns on your Human Capital Investment – your Return on People.
Ingenuity, innovation, inventiveness, improvisation – it seems we’ve been talking about these words for a decade now. In fact, we’ve talked about them so much that I think sometimes the words have lost their meaning, or at least are diluted to a point where their mention causes a roll of the eyes. Yeah I know, I get it, the innovative thrive and succeed. But what does innovation really look like? What is innovation in practice?
That’s why we go to things like TEDx, to see that it’s possible to make a refrigerator that uses no electricity, a device that prevents amputations, a gene map that can save millions of dollars a year. Ideas so worthy that our business problems seem small. These ideas give us hope that in the light of a new day, we can also do innovative things.
However, I reject that notion that the result of innovation is always a “thing” and that innovation comes in response to something that needs to be improved. The height of innovation comes not from what you want to fix, treat, or respond to, but rather what you predict and work to prevent. The distinct shift in mindset from responsive to proactive is critical. But you have to create space to think about the future. It’s just too hard to worry about tomorrow when you’re trying to recruit fingers to put into the holes that keep appearing in the dyke of your present day.
To link these concepts to what Media 1 does, we are all about trying to innovate in the way that companies look at the people who work for them. To get companies to see how people can better contribute in the future and feel better about it. To predict and prevent business conditions that adversely affect those people and promote conditions that enhance both the company and the lives of those who work there. We help you start measuring the things that spur action to innovation in the real world of your business and your people.
I’ll leave you with my favorite slide of the day from Greg Galle, on the six keys to jumping the ingenuity gap. TEDx events are one day that you can dedicate to the future, but they are a just a starting point of changing the way you approach your business and your life. It won’t be easy, but it’ll be worth it.