Common Sense KPI’s Gone Wrong

I love dashboards.  I have a goals list on my phone tracking how many miles I’m supposed to run or ride on my bike.  I have a trending graph on the device that tells my how much I weigh.  The only reason I have not bought one of those fitness wristbands yet is because I just can’t stand things on my wrist!  I was just on the company call, and we do the company performance dashboard, we stack ranked the all time leaders for ideation at the company, and all sorts of other visual and gamified graphics.  As employees, we should be managing our goals and goal progress, and some systems now have cool mobile components that can visually show where we are with each performance goal.  It’s great to be able to track where we are at any given time in almost any area of our lives.

Sometimes our KPI’s go desperately wrong, even though they seem to make sense.  My current personal goal is to get back to 10% body fat.  For those of you who don’t know me, let’s just say I’m already one “skinny ass dude.”  The problem is that less fat for a person who is generally athletic and out of doors as often as possible sounds like a good thing.  The question is, is it the right thing?  We actually face the same problem in our HCM KPI’s.  Here are a few examples.

Employee Referral %:  

  • Employee’s who are referred to us by other employees are our best people.  Right?  Almost all of us would agree that this is true as these employees will have higher levels of engagement, are pre-screened as people we’d want to work with, are capable and smart.  The referrer has a stake in the person’s success, but their credibility is also on the line so they probably won’t be referring crappy people.
  • Often, we’ll see that companies want to achieve as high a referral % as possible.  This allows the company to get more great people, but also reduce recruiting costs.  The problem is that there’s also a tendency to refer people who are similar to us.  This is a problem on a couple of fronts.  First of all, there is an ideation and innovation problem.  If you recruit people who are similar to you, who have similar experiences, have worked in the same places, you are not getting your company’s due in diverse thinking.  Second, people like us are not demographically diverse all of the time.  if you have a lot of “white dudes” and you want a 100% referral rate, you’re still going to be a bunch of “white dudes” in 5 years.

Employee Turnover %:

  • This one is fun.  Some organizations are SO proud of the low turnover that they have.  I’ll walk into a new project and within day’s I’m inundated with how they have achieved 5% turnover.  I mean, having great employee engagement so much so that nobody ever wants to leave is a great thing, right?  We see targets of 8% and lower all the time.
  • Depending on which philosophy to subscribe to, there is such a thing as “desirable turnover.”  Those are the Jack Welch bottom 10%, or in your forced bell curve performance ratings the bottom 5-15%.  Let’s just say that there are 10% of people in your organization at any time that SHOULD leave, and you should be encouraging to leave.  So if your desirable turnover is up to 10%, and your target is less than 10%, something is pretty much wrong.  Right?
  • The key is to figure out how to shift the conversation to unwanted turnover rate instead of total turnover rate.  A very high performing organization could have a total turnover rate at 12%, but if their unwanted turnover is only 2%, I’d say they are doing fantastically.

We all want high referral rates, and we also want low turnover rates.  These are great KPI’s, but we take too much for granted and at face value.  Going to extremes just because there’s a number to hit impacts our organizations in a pretty negative way, and in HR, it usually means that we have some of the wrong people working for us.

Big Data For HR & Recruiting: 2 Use Cases

Forget about Google.  One of the world’s favorite sites has got to be LMGTFY.com.  “Let me google that for you.”  With the wondrous world of the internet and ubiquitous information, ubiquitous access through our mobile devices, and ubiquitous connectivity, whenever someone asks a dumb question in email, you get to just send them back a link to LMGTFY.com (since most of the time you don’t actually know the answer anyway – I can’t count the times someone asked me something I didn’t know but I gave them the answer after googling it.)  It’s a bit sad though, that we have such incredible access to information in our personal lives, but our access to HR information seems to be severely limited.  We are just starting to do cool things in HR to answer interesting questions about our employees, but in a couple of segments of the industry, it’s about to get completely fascinating.

(NOTE TO READERS:  I stopped writing about specific vendors a long time ago, but today I’m going to highlight a couple that I think are doing really great work – but it’s more about the work that I’d like to highlight, so don’t take this as a vendor plug!!!)

Oh, we love buzz words.  I know I’ve been writing about big data and social HR a lot this year.  If the last decade was about the shift from HR administration to talent management, the next decade is going to be about creating insights about our people and making them own their own development.  When we talk about information, Google, Yahoo, Microsoft have been creating algorithms to search data for years.  HR just hasn’t had to tools to do it.  Things are about to change rapidly.

Use Case 1:  Using Big Data to Use Your Employees:
Use your employees?  Oh that sounds terrible.  The reality of this is that it’s really cool.  A small company called Careerify (careerify.net) started selling a product last year that allows you to easily grow the volume of high quality employee referrals you get.  What they do, is they allow employees to connect their Facebook, Linkedin, Google+, Twitter and whatever other networks they have, so that you can help them understand when they should refer someone to the organization.  Let’s say for example that you have an opening for an electrical engineer.  Careerify would help you push an internal campaign to your employees, perhaps sending them an email that automatically identifies people in their network who are electrical engineers.  It goes a step further though.  Because you might be linked to a number of the employee networks with a variety of data, you can target the location of people on top of jobs.  But it even gets better… let’s say you know you have a culture of people who are active, outdoorsy, and fit, and there is an electrical engineer who happens to be a bass fisherman and loves to skydive (based on photo tags in Facebook).  This EE would be scored higher than individuals without those interests.

The ability to mine employee networks (with their opt in) and present employees with easily selectable pictures they can click on to invite someone in their network to apply is almost too easy.  We all know that employee referrals are the fastest, highest quality, and lowest cost recruiting source we have, but we just never knew how to make it easy for the employee.  By using big data to reach into employee networks and analyze profile attributes and even tags and update activity, you could literally improve your core recruiting metrics (time to hire, cost of hire, quality of hire) in weeks.

Use Case 2:  Bringing More Information to Recruiters
To be totally honest, I’ve been trying to work out why recruiting is always ahead of the curve when it comes to HR technology.  I think at the end of the day it all has to do with the fact that it’s a function the business actually depends on.  If they (managers) don’t do performance reviews, their organization probably won’t suffer for months or years.  If they don’t fill the open seat though, their productivity is suffering the very next day.  A company called eQuest (been around much longer than Careerify) has been using big data to answer some of those questions around “why aren’t I hiring anyone right now?”  What is interesting and wonderful is the completely different approach organizations like Careerify and eQuest are taking.

eQuest looks at the market through job boards, and correlates your recruiting activity to activity in the market.  Basically, if that same electrical engineer position is still open in 60 days, eQuest can tell you what ever other company is doing differently.  For example, based on what is out on the job boards, you have 3 EE jobs open out of 50 in your local area.  It just might so happen that you are offering a lower compensation rate, so you are getting 60% fewer candidates than the average EE job opening (demand is up, but you have not adjusted to market yet).  Or, it could just be that in the last 6 months, there are fewer EE candidates even looking at and opening those job postings (supply is down).

Unlike Careerify who tries to solve the problem by making it easier for your employees to help you, eQuest tries to solve it by putting better data in your recruiter’s hands.  Both are completely valid mind you, just different approaches to big data.  With either technology, I actually think we are getting closer to LMGTFY.com for HR and recruiting.  When my internal recruiter asks me if “I know anyone,” I’d have to think about it.  But if Careerify asks me, and automatically sends me a clickable picture of 5 people who already match, I only have to think about whether I really want that person around or not.  On the other hand, if my recruiters are asking “Why the F isn’t anyone applying for this job?” eQuest can probably give you a pretty good answer as well.

Jocks vs. Nerds

“There’s this idea of the jocks vs. nerds thing. That sort of ended when the nerds won decisively. We now live in this era where your big summer tentpole movies can be hobbits and minor Marvel Comics superheroes and boy wizards. If you had told me when I was in junior high there would be a $200 million movie about Hawkeye andBlack Widow, I’d be like, ‘Hawkeye — that guy’s lame!'” Jennings says. “Those nerds started running Hollywood studios, and our captains of industry became Asperger types with acne scars.”

I was reading about what Ken Jennings (of Jeopardy! fame) was up to these days, and there was the above quote that I found hilarious.  It’s totally try though, especially for a guy like me who lives in the Silicone Valley.  High school might have been a time when the jocks ruled the world, and college was a transition time, but once you get into the workforce, there are really grate charismatic guys running businesses, but the people who are really redefining the world and how we change our behaviors to adapt to the world on a daily basis are quite clearly the nerds.

(Credit to the HR Technology Conference and Bill Kutik bringing IBM’s Watson computer for making me think about Ken Jennings)

I’m continuously thinking about analytics these days and I start to think that HR has also started the transition from “people people” to something a bit nerdier.  Maybe we are in that college stage I mentioned above – we’re no longer just the people that you go to for benefits and worker’s comp – that was over a couple decades ago.  We’ve started down the path of Talent Management, and we’re probably still trying to figure that out.  We keep talking about really great analytics, but we really don’t do it well.  I think what we need to really get to a mature level of HR as a profession is we need to get nerdier.

Talent Management:
It’s entirely possible we’ve been wrong for the last decade.  We’ve built these incredible competency models, tracked how and when a goal should cascade, and automated all of our talent processes.  I don’t think the business is convinced that we’ve actually improved the core employee’s ability to get developed.  Think about what you yourself did 10 years ago.  Big deal that you can now enroll yourself in training on-line and you have a cooler performance tool that is not a piece of paper.  Have the majority of employees in any company really experienced a perceptible difference in talent and development outcomes?  I’m guessing not.

It’s entirely possible we need some nerds to take over.  I don’t care how much HR shepherds the process along – if the employee and manager don’t own their own talent, it’s game over.  The only way to do this (that I can currently think of) is to create easy to use, social, real time, talent engines.  I’m thinking of an engine that quickly allows a manager to give feedback or development instructions when and where they think of it, then have seamless execution (again in real time) by the employee.  All of this has to happen without the HR practitioner and then roll up at the end of the quarter or year so we get that macro view of progress.  Without real time integration with the employee and manager though, all we have is another failed HR process.

Where HR gets involved is not in shepherding the process, but instead in managing success.  If we can mine the data and understand who is doing what, what works, and where we are missing the mark, that is where the value is.  Somewhere and some point, process people are still important as we make the transition, and certainly we need great change people to get manager adoption, but what we really need are analytical nerds who get how to interpret data.

HR Analytics:
I really hope we don’t have illusions that we are any good at this – we’re not.  We have technical people and we have functional reporting people helping our organizations create reports.  We have vendors feeding us cool dashboards that we then flip and roll out to our managers and executives.  What seems to be missing to me… the statisticians.

Have you ever talked to the finance guys about what they are doing in their analytics functions?  The stuff they produce is absolutely amazing – and they are set up in a pretty different way than we are.  Financial models are very complicated, but shouldn’t our models of people resources be just as robust?  In fact, if anything we have more complex, more dynamic, and more diverse data sets.  If we were dealing with numbers, our lives would be easier – but we deal with more complexity with less sophistication.  No wonder we walk into the executive boardroom and don’t get credible respect.

It’s interesting – when I look at the type of people HR hires, we automatically know we’re not going to have the best friend relationships with Comp, Payroll, IT, etc…  Those guys are just different from us.  I mean, my God, they are analytical in a totally different way.  Embrace the difference – it’s what HR needs, and it’s not even enough.  I’d love to see us start to hire the nerds – math majors and people who can come up with complex statistical understandings of the HR world.  We are in our infancy for understanding HR, but it’s because we don’t structure our organizations in such a way to create deeper understanding.

Get used to the fact that the nerds have won.

Bread & Butter

It always frustrates me when I’m dieting – I have to forego one of my favorite food items:  Butter.  Butter (fat) along with bacon fat (fat) is one of those amazing joys of life.  When butter is great, a bit salty, a lot smooth, and a lot fatty, it is a wonderful thing  Unfortunately, one cannot generally eat butter straight off the spoon without incurring some ridicule from friends.  Therefore, one must also eat bread.  To me, bread is not just a necessary evil.  Great bread on its own is also a joy of life.  It can be beautifully crusty on the outside, warm on the inside.  But sometimes when the bread is not great, it’s just a delivery system for the butter.  Perfect harmony ensues when both the bread and butter a great.

HR service delivery (you knew it was coming, don’t roll your eyes) is quite like bread and butter.  Imagine your HR business partners as the bread and butter as amazing data and insights.  When the HRBP is great, you have a wonderful partnership of a person who actively gets to know the business, builds great relationships, communicates, plans and collaborates effectively.  Unfortunately, the HRBP is often paired with crappy systems, inaccurate data, and poor reporting capabilities.  The business wants a partner, but they also want a partner that can help diagnose what is going on with their people.

Butter on the other hand is like great data.  When systems and data are in good order, access to reporting and discovering insights become possible.  Insights into the organization and people don’t mean anything  however if all you have is some people at corporate that don’t have relationships into each business segment.  Data and insights get lost in the fray, lost y the wrong people, poorly communicated, and otherwise rendered meaningless.

Just as you can’t eat butter straight (again without incurring ridicule), you need a good delivery system.  That’s the bread. In this case, the delivery of the insights can’t even be consumed without great HRBP’s.  In a prior consulting firm that I worked for, we used to have a line at the bottom of each powerpoint that said something like, “content should be considered incomplete without contextual dialog.”

We’ve been so caught up in data, big data, business intelligence, predictive analytics that we’ve been on a quest to spend millions of dollars to fix all of our foundational data systems.  In a few years, we’re hoping to deliver amazing insights into the organization.  Pair processes with real time intelligence that allows managers to know exactly what actions to take with people.  I’m the downer guy to tell you that without the context of the great HRBP who understands the business, 80% of that cool data analysis is meaningless.  You don’t get insight without understanding the business – all you have is a cool analytic.

That poses the second problem.  Do we actually have great HRBP’s?  The analysis of that has been done in many other places, but the answer for the vast majority of us is “no.”  We’re spending millions of dollars on the data, but we still have not figured out how to transform our HRBP’s.  I’m not saying they are the HR generalists they were 10 years ago, but they still don’t usually have the full trust of the business, the ability to make business, people, financial, operations… correlations, and they still don’t understand the business the way they understand HR.  We still have work to do here, so realize that we can deliver the data, but whether we can make it meaningful is still uncertain.

In our quest for great data delivery to the business, let’s not forget that it’s the pairing of two great elements partnered effectively together than makes the data meaningful.

(this post was made possible during the consumption of some pretty good bread and butter)
(I thought about using “meat & potatoes” but I’m not quite as passionate about that)

It’s Time For the CedarCrestone HR Technology Survey

It’s that time of year again.  The IHRIM conference is going on this week (no I’m not there), and the CedarCrestone HR Technology Survey is in full swing.  I publicize this particular survey every year for a couple of reasons.

  1. I gain the most insight from CedarCrestone’s survey about HR Technology than any other.
  2. This survey is the largest in the industry, and more participants means better insights
  3. Lexy Martin, who runs the survey, is on the leading edge of surveying not only what people are doing now, but what they are thinking about for the future.  This is one survey you can look at to evaluate not only your current practices, but also to predict if you are going to be a laggard next year.

For a number of years running, systematicHR readers have contributed more usable survey responses than any other social media outlet.  I’d like to keep that up.

If you are a person of the many thousands who likes to hear about the survey results later this year (at the annual HR Technology Conference, this year in Las Vegas), then take some time and contribute so that everyone can have better access to the insights.  (Filling out the survey also gets you a $500 discount to the HR Technology conference)

http://www.cedarcrestone.com/survey/systematicHR.html

 

Is Big Data An HR Directive?

I have an argument with my wife every few years.  I tend to like cars with a bit more horsepower.  I mean, that 1 time a year when there is a really stupid driver about to crash into you, a few extra horses comes in handy when you really need to speed away.  The problem is that 99.99% of the time, that extra horsepower is a luxury you really don’t need.  You’d get from point A to point B just as safely, and probably just as fast.  Sometimes though, that engine really does matter.  (My wife wins 90% of arguments by the way)

Everyone in HRIT is talking about big data these days.  Unless I completely don’t get it, I thought this is what we’ve been working towards for years.  I mean, having ALL of our talent data, core HR, learning, recruiting, payroll, benefits, compensation, safety, etc data all in the same place and running analytics against it all was always part of the data warehouse plan.  I mean come on, what else is ETL for if not to grab data from all over the place, aggregate it into the ODS, and then figure out how to make sense of it all?  We’ve built a nice engine that caters to our needs 99.99% of the time.

I’m going to propose something:  Big data does not matter to HR.  It’s just a new naming of something that does matter.  Business intelligence and truly focused analytics is what makes us focus our actions in the right places.  BI, Big Data, I don’t care what we call it.  Just do it.  Either way, HR does not have a big data need at this point.  I’d propose that we can use Big Data technology to speed up our analytics outcomes, but that’s about all we need for the next few years.

In my simplified definition of Big Data, it comes down to two major attributes: the use of external data sources, and the lack of need to normalize data across sources.  If we look at it from this point of view, the reality would state that almost no HR department on the face of the earth is ready to take HR data and compare it with government census data, or employment data.  Let’s get really creative and take local population health statistics combined with local census to get some really interesting indicators on our own employee population health.  Right, we’re just not there yet.

Let me reverse the thinking for a moment though.  What about the other 0.01% of the time that our traditional BI tools just won’t help us out?  Going back to benefits examples, how many global organizations can really directly compare benefit costs across the entire world?  How many of those same global organizations have a great handle on every payroll code?  Much of the problem is that the data is often outsourced, and definitely not standardized.  Collecting the information is problematic in the first place, but next to impossible to standardize annual changes in the second. The beauty of Big Data is that in these cases, you’d actually be able to gather all of that data and not worry about how to translate it all into equal meanings.  The data might aggregate in a more “directional” way than you’d like, but you’d probably still have an acceptable view of what global benefits or payroll is doing.  It seems to me that this puts us quite a bit further ahead of where we are now.

Listen, I know that HR has some place in Big Data at some point in the future, but the reality is that the current use cases for Big Data are so few and far between, and that we have so many other data projects to work on that we should continue investing in the current report and analytics projects.  Big Data will come back our way in a few years.

As I said, my wife usually wins the arguments.  We end up buying a car that has 175 horses under the hood, and I end up wishing we had more once a year.  But inevitably, automakers seem to up the game every few model years and come 5 years down the road, that same car model how has 195 horses.  If I just wait long enough, those extra horses in the engine just become standard.

 

 

 

 

I Don’t Want No Stinkin’ Analytics!

I’m a nerd.  I get on my bike or I go for a run, and I’ve got my Garmin GPS running the whole time telling me how far I went, how fast I went, what my heart rate was, (ok I’ll stop the list well before it gets to 20 items).  I also happen to weigh myself 4 times a day.  I like to know how much I weigh, what my % of water weight is, how fat I am, etc.  I’m a total nerd and I like my data – lots of it.

When I get home from a routine Saturday ride, the first thing I do is download all the data.  The data by itself is interesting, but only for about 3 minutes.  The second thing I do is I trend the thing.  I’ll look at my ride side by side with the prior week’s and maybe several others.  Basically I’ll compare the stats and get an idea if I was slower, faster, more powerful, etc.  In other words, within a few seconds, I’ll know if I’m better or worse off.

The thing is, I really don’t care about that either.  What I really care about is that I was really slow up Mt. Tam, or that I got dropped on the way into Pt. Reyes.  It’s not that I care that I suck (that’s a given), it’s that where I suck tells me what to work on.  Sometimes, it even tells me that I didn’t eat enough (a common problem if you guys know me – yes, I’m neurotic).

In the last 15 years, most of us have gone from minimal data in our reports, to some pretty decent analytics and/or dashboards.  We’ve started moving away from the static and columnar operational reports and into trending and drillable analytics.  With these new reports, we’ve prided ourselves with the ability to tell our executives that “turnover is down compared to a year ago”, or “the cost of hiring in #BU has skyrocketed.”  It’s a wonderful day in the neighborhood!

But I don’t want operational reports.  Neither do I want analytics.  What I want has almost nothing to do with the data.  I want insightfulness into the business.

Let’s pretend I go to the head of my business unit and tell her that turnover is up over the last 3 quarters.  “Crap,” she says.  “What is the problem and what do we do about it?”  Joe HR stammers and says, “looks like we have an engagement problem???”  Trends and drills are really nice things.  I know I’m lying, our managers really do want this stuff.  But they only want it because we’ve starved them for data for decades.  They really are only mildly interested in the data.  Just as I only look at my bike ride speed out of curiosity, I know that the number alone tells me zilch.  Just as knowing my average speed compared to last week tells me only if I was a little better or worse, ignores conditions on the road, and the environmental context.  What I really want to know is how everything fits together to provide an analysis that give me the insight to act and make a decision.  I want to know what to act on, when, and how to do it.

HR’s job in data is starting to transition yet again.  We’re moving out of the business (I hope) of creating trends and drills, and moving into the business of context.  So the turnover trend looked bad.  Now the questions is how we create a regression model around our data to figure out what the primary actors are for that turnover trend.  Maybe for once engagement only has a small contributing score to turnover.  Maybe what we didn’t know was that the leader told their entire BU that nobody was getting a bonus this year.  Perhaps the cause of that was actually some severe cost containment driven at the corporate level.  How about some good analytics here to compare the cost of total turnover to the cost of those bonuses?

At the end of the day, we have much cooler data.  I’ll give us that one (and the vendors especially).  It’s time though to stop being producers of just the data alone.  It’s not enough anymore.  Perhaps when half of HR were “generalists” a decade ago this was ok.  But we’re supposed to be partnered with the business now and we still can’t really diagnose what’s going on, let alone how to fix things.  Once again, let’s stop being producers of data and become analysts of data.  We need to start producing some insightfulness.

Using HR Analytics to WIN

So I have to admit it’s hard to come off the election and not write about this.  This election was defined by some pretty deep population analysis, incredible forecasting and pretty significant actions to try to address the most important populations.  All of this went right along with a prioritization model that was pretty strong.  If we look at Obama/Romney, they each had target demographics: Obama needed the youth vote, managed to capture more than his fair share of the female vote (because the GOP was being stupid, and a couple contributions by Romney as well), and he also predictably got the “minority” vote.  As the election neared, both candidates narrowed down their activities to a few key states (Ohio, Florida, Virginia, etc.)  They had incredible models about what percentage of each population’s vote they would get, and if they followed those models, all they had to do was figure out how to get those people to the polls.  It turns out that in the end, the Obama machine was far better, and the Romney machine actually broke down (the phone app they were going to use literally went down on election day, and thousands of faithful Romney campaign volunteers had no idea which houses to go to and make sure people actually voted).

At the end of the day, the story is the same as the one we have in HR.  It’s about winning.  The difference is that we all want to win at different things.  Some of us want to win at engaging our employees.  Others want to win by having the best IP.  More yet want to win by producing the best products in our category in the world.  What is great is that if we’re good at what we do, we’re not focused on running analysis about turnover and headcount (although we’ll do that anyway), but instead we’re focused on understanding exactly what 5 things increase engagement in our populations.  Taking that a step further, we’d know exactly what populations are the most impactful on the entire workforce and target those people.  A 1% increase in engagement in the 30-something sales guys might yield an advantageous network effect while it takes a 3% increase in another population for the same to happen.

Similarly, if I want the best IP, I need to understand the roots of this.  Chances are it’s not just the standard talent management equations we need to figure out.  I mean, performance and succession are only going to take us so far.  Instead, if we’re figuring out the profile of the employees that created the most patent applications, the people who have the highest levels of trust in their subject matter, and specifically what are the attributes that made them successful, then we can start recruiting for those people, aligning our performance reviews not to achievements, but to the competencies we know work, and doing talent reviews that direct us to the right employee profiles rather than who we think is ready from a job perspective.

My assumption is that if you have created your HR strategy in the right way, and you have aligned that strategy with the corporate strategy, then HR is designed to make the organization WIN.  Therefore every analytic you are running should be directing your organization to that win.  Don’t forget about the mundane operational reports, but understand that focusing on that isn’t really helping you.

At the end of the day, Romney actually did almost everything right, and he really thought he was going to win.  The problem that he had was that he made a few wrong assumptions.  The GOP really thought that all the pollsters were wrong – that they were over emphasizing the Democratic vote that would turn out on election day – that Democrats, the youth, and Hispanics were really not as excited about Obama as they were 4 years ago.  Obama on the other hand had callers identifying who were voters, if they were voting for Obama, and then instructing the voter where their polling place was, good times to go, and what the plan was to get them there.  At the end of the day, forget about the other guy.  Just go run your analytics, make sure you are focused on what matters, and go out there and WIN!

Note:  Please assume no political commentary, simply an example of who analytics can be put into action.