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)

Cedar Crestone HR Technology Survey: Create a Winning HR Function

All too often, I get an industry report to read and end up saying to my colleagues, “wow this is crap.”  Case in point, at the end of 2012, I got a widely read industry report that rated a halfway decent HCM provider’s payroll engine to be better than one of the major payroll outsourcers.  They stated that a vendor’s almost non-existent compensation functionality was a top pick.  Each year, I go through the CedarCrestone HR Technology Survey, and hope there is something wickedly out of sync with conventional wisdom.  Each year, Lexy proves why she is the queen bee of HR surveys and is meticulously above reproach.  I just can’t stand it.

What’s great about this particular survey is that it’s not just gathering data and spitting it back out at you.  I know we all care how many people are buying Workday versus Fusion versus Employee Central versus … this year.  I know we all are interested how many of us are still on premise with our core HCM.  That’s so not the point.  What Lexy does is far more interesting.  She takes all of this data and compares it to company profiles.  What’s the correlation of profitable companies to those people who are running Software X or Technology Y?  This makes up the part of the report I’d like to chat about.  Lexy published 7 habits, and I’m going to summarize so you’ll just have to ask CedarCrestone for the report to read the whole thing.

The attributes that defined successful companies were pretty much higher than usual revenues per employee, profits per employee, operating income and return on equity.  Pretty good measurements.  I’m not sure if CedarCrestone evaluates which is causal, but they do evaluate correlations, so in that sense, go after what you can control, which in our case is the HR side.

  • User Adoption – “If you build it, they will come.”  What a load of crap – wasn’t that some baseball movie Kevin Costner was in?  I don’t remember, but it certainly does not apply to HR technology.  Instead, we have to implement ridiculous change management strategies just to get our managers and employees engaged with us.  If not, we only hear from them when their payrolls are wrong, or to complain about the vacation policy.  The reality is that organizations who successfully implemented solutions, had good change management programs resulting in high user adoption also ended up being among the more successful companies.
  • Buying Habits and Governance – Governance always seems to play into things.  I’ve found that the few organizations that are great at governance tend to be awesome places to work, make good decisions, and have high employee engagement.  So I’m stretching Lexy’s observations here, but basically when I reflect on her finding that successful companies have more technology and spend less per employee, I almost immediately translate that into good governance.  How do you get to better utilization of what you have, and only buying what you need after all?
  • Technology Decisions – There was also a couple of themes that I translated into low maintenance overhead, but also the ability to use industry best practices.  It kills me when I walk into a client that is so highly customized they really don’t know what they are doing anymore other than accepting new requests and implementing full time.  Most of these organizations don’t even know why or what the business case is – they just do it.  Successful companies are correlated to low customization, which is also correlated to SaaS purchases.
  • Data – One would automatically think that successful companies are good with data.  It seems obvious.  The survey actually points out a couple of great tactical elements to get you there.  The first one was integrated talent management with your core HCM product.  Companies that were there tended to have a significant advantage than others.  The second was the utilization of mature business intelligence models, along with the deployment of that data into manager’s hands where agile business decisions can be made.

At the end of the day, HR just wants to be heard.  Interestingly enough, there are elements of shoring up our own house as well as focusing on outcomes here.  If we make bad decisions and have crappy governance, well that’s problem number 1.  But if we also have crappy user adoption and poor data, we’ve also lost the game.

Note – nowhere in this did we correlate functionality to success!

Better Measures for Engagement

Is it Gallup that has the “Do you have a best friend at work” question?  We’re so into doing employee surveys to measure employee engagement.  They provide us with a statistically validated measurement of our workforce once or twice a year.  We can look at the engagement studies, and if we have any luck at all, capture some high level data about the organization and then correlate the data back to turnover and productivity in specific population groups.  My question is this: Isn’t waiting 6 or 12 months for engagement measurements rather a long time in today’s world of real time analytics?

How about this:  ((The idea for this post came from:  Ariely, Dan.  “CEO’s probably think of their employees as more like rats in a maze than as people.”  Wired Magazine, UK Edition.  April 11, 2011.  Page 44))

  1. Measure the time of day employees log into their PC in the morning.
  2. Measure the time of day employees log out of their PC in the afternoon.
  3. Measure the cost per day per trip (expenses) calibrated to some standard.
  4. Measure the number of sick days on Monday and Friday.

I mean, why would you wait 6 or 12 months?

  • If your employees are (on average) coming to work later or leaving earlier, they are less engaged.
  • If the aggregated cost of a trip to NYC costs more per day, employees are “fudging” their expenses, and they are less engaged.
  • If Monday and Friday sick time is increasing (faked sick time), they are less engaged.

I mean, come on, we want to have close to real time measures, right?  I’m not saying that employee engagement actually changes on a day to day basis, but charted weekly, you could get some really cool trending data and identify exactly when the engagement curve increases or decreases.  You could then correlate all of the events that happened in that timeframe and figure out what is actually causing increases or decreases in engagement.  You could also isolate specific groups and populations (sample size would have to be large enough).  Say a VP leaves and is replaced, and 6 months later employees are staying at work later.   Or, the cost of a meal in NYC seems to be getting higher for a specific project team – are they celebrating, or are they all depressed and eating more?

How cool would it be to then look at performance in correlation with a weekly trend in engagement?  This is assuming that we start managing and developing our employees on an ongoing basis rather than once a year, but the possibilities are out there.

Recruiting Effectiveness Measurement

Last post I wrote about recruiting efficiency measures.  From the effectiveness side, we’re all used to things like first year turnover rates and performance rates.  Once again, we’ve been using these metrics forever, but they don’t necessarily measure actual effectiveness.  You’d like to think that quality of hire metrics tells us about effectiveness, but I’m not sure it really does.

When we look at the standard quality of hire metrics, they usually have something to do with the turnover rate and performance scores after 90 days or 1 year.  Especially when those two metrics are combined, you wind up with a decent view of short term effectiveness.  The more people that are left, and the higher the average performance score, the better the effectiveness., right?

Not so quick.  While low turnover rates are absolutely desirable, they should also be assumed.  High turnover rates don’t indicate a lack of effectiveness.  High turnover rates instead indicate a completely dysfunctional recruiting operation.  Second of all, the utilization of performance scores doesn’t seem to indicate anything for me.

Organizations that are using 90 or 180 day performance scores have so much new hire recency bias that they are completely irrelevant.  It’s pretty rare that you have a manager review a new hire poorly after just 3 or 6 months.  For most organizations, you expect people to observe and soak in the new company culture before really doing much of anything.  This process usually takes at least 3 months.  And while the average performance score in the organization might be “3” your 90 and 180 day performance scores are often going to be marginally higher than “3” even though those new hires have not actually done anything yet.  However, you’ll have a performance score that is advantageous to the overall organizational score making you think that your recruiters are heroes.  Instead, all you have is a bunch of bias working on your metrics.

I’m not sure I have any short term metrics for recruiter effectiveness though.  Since we don’t get a grasp of almost any new hire within the first year, short term effectiveness is really pretty hard to measure.  I’m certainly not saying that turnover and performance are the wrong measures.  I’m just saying that you can’t measure effectiveness in the short term.

First of all, we need to correlate the degree of recruiting impact that we have on turnover versus things like manager influence.  If we’re looking at effectiveness over 3 years, we need to be able to localize what impact recruiting actually has in selecting applicants that will stick around in your organizational culture.  Second, we need to pick the right performance scores.  Are we looking at the actual performance score? goal attainment, competency growth, or career movement in # years?  Picking the right metrics is pretty critical, and it’s easy to pick the wrong ones just because it’s what everyone else is using.  However, depending on your talent strategy, you might be less interested in performance and more interested in competency growth.  You might want to look at performance for lower level positions while the number of career moves in 5 years is the metric for senior roles.  A one size fits all does not work for recruiting effectiveness because the recruiting strategy changes from organization to organization and even between business units within the same organization.

Overall, recruiter effectiveness is not as simple as it seems, and unfortunately there isn’t a good way to predict effectiveness in the short term.  In fact, short term effectiveness may be one of those oxymorons.

Recruiting Efficiency Measurement

If you look through Saratoga, there are all sorts of metrics around measuring our HR operations.  For recruiting, these include all the standard metrics like cost/hire, cost/requisition, time to fill, fills per recruiter, etc.   Unfortunately, I’m not a fan of most of these metrics.  They give us a lot of data, but they don’t tell us how effective or efficient we really are.  You’d like to think that there is going to be a correlation between fills per recruiter to efficiency, and there probably is some correlation, but true efficiency is a bit harder to get a handle on.

When I’m thinking about efficiency, I’m not thinking about how many requisitions a recruiter can get through in any given year or month.  I’m not even sure I care too much about the time to fill.  All of these things are attributes of your particular staffing organization and the crunch you put on your recruiters.  If you have an unexpected ramp-up, your recruiters will be forced to work with higher volumes and perhaps at faster fill rates.  Once again, I’m sure there is a correlation with recruiter efficiency, but it may not be as direct as we think.

Back to the point, when I think about recruiting efficiency, I’m thinking about the actual recruiting process, not how fast you get from step one to step 10, or how many step 1-10 you can get through.  Recruiting efficiency is about how many times you touch a candidate between each of those steps.  Efficiency is about optimizing every single contact point between every constituency in the recruiting process – recruiters, sourcers, candidates, and hiring managers.

The idea is that you should be able to provide high quality results without having to interview the candidate 20 times or have the hiring manager review 5 different sets of resumes.  If you present a set of 8 resumes to the hiring manager and none of them are acceptable, you just reduced your recruiting efficiency by not knowing the core attributes of the job well enough and not sourcing/screening well enough.  If you took a candidate through 20 interviews, you just reduced your efficiency by involving too many people who probably don’t all need to participate in the hiring decision and who are all asking the same questions to the candidate.  Sure, there is a correlation between the total “touches” in the recruiting process to time to fill, but “touches” is a much better metric.

I know we’ve been using the same efficiency metrics for ages upon ages, and most of us actually agree that we dislike these.  Touches within the recruiting process makes a whole lot more sense to me, as it gets to the actual root of the efficiency measurement.

Misinterpreting Apple and Microsoft

As a global community, we all hate Bill Gates.  Actually we all hate Microsoft.  While we might depend on things like Microsoft Outlook and the MS Office suite of products, most of us probably believe that they have a bit too much of a monopoly and probably have bullied around other companies to ensure that they have a strong foothold in their industry.  Alternatively, we all love Steve Jobs and Apple.  This guy has reinvented Apple and given us some great products with amazing usability.  Instead of the “blue screen of death” from Microsoft, we have the incredibly usable iPhone.  Apple is also all about community, and their technologies tend to have brought us closer together, creating now ubiquitous applications (also on Google Android phones) that help us better connect in real time.

But sometimes image and marketing is everything.  The Bill and Melinda Gates Foundation is one of the largest philanthropic institutions in the world, providing funding for diverse programs in health , global economic development, and education.  Steve Jobs on the other hand had a foundation for about 15 months, but it was shut down after never doing much of anything.  In comparing the two business leaders, it appears that Gates is rather selfless in his charitable intentions, but Jobs (in the rare circumstances that he endorses a cause) only mentions a cause when it serves the purposes of Apple and the growth of his personal wealth.

It’s easy to look at something, especially a set of data, and be swayed by our own personal experiences with it.  We often have events or our relationships with the business provide specific opinions that may or may not be close to the truth.  In the Gates and Jobs example, we even have clean and quantitative data prove that Jobs is the better guy.  Apple has overtaken Microsoft in market capitalization, and therefore the consumers have voted, not for the big corporate giant providing software to big corporate giants, but to the provider of tools to everyday individual consumers.

I’m not arguing what set of technologies is more deserving of our approval in terms of market cap, but how these images inform our ability to interpret data in the absence of external influences.  The reason that I’m an advocate of business intelligence analysts that view data and operate in a function that is not touched by the externalities of the “real world” is that they can touch and feel the data with an objective eye.  Those of us who operate in business and business process can often be blinded by prior results that are not directly related to the data, but we think they are.  I’ve seen organizations completely disregard employee engagement surveys that identified terrible managers simply because productions or sales happened to be “hot.”  We’ve been influenced by circumstantial evidence that very senior managers can’t be “messed” with or that diversity data is better than it really is.  We’ve completely missed the mark on interpreting trend lines because we don’t have analysts who know how to look at data from a mathematical perspective.

At the end of the day, situational evidence is critical in how we interpret our version of reality.  In no way can we ignore this, but at the same time, we have to be able to see through it and objectify the data before we can reach a conclusion.  We can’t let our judgment around data be clouded by only what our perceived reality already is, because if we do, our role to the business as part of the decision support chain is completely irrelevant.

The Art of Story

Whether you are at a conference watching a videographer recording the event, or witnessing a $100M film getting made, the process of recording to final editing is always the same.  Actually seeing how stories unfold is rather amazing – it’s the real life is nowhere near as linear as the resultant film that everyone sees.  Instead, what gets recorded onto the raw film is more of a patchwork of completely random thoughts, statements and images.  If you actually watched all of the film in the order that it’s recorded, most of it would make absolutely no sense in the context of what surrounds it.  The editing process is about bringing together the common elements and magnifying the key points, and then putting everything back into an order than is meaningful in the sense of a story.

The problem with HR is that HR executives are not like finance or technology executives.  The art of story is a bit more important than the science of numbers.  Where we can always count on having a detailed TCO or ROI study ready for our CFO’s, sometimes HR looks at the numbers and wants to know “why?”  And the “why” is never about the number, but about the qualitative.

Whether you are a consultant or a HR practitioner creating a business case, the same thing tends to happen.  You pick up random conversations, have random meetings, perform sets of broad interviews, and at the end of collecting data, you have… lots of data.  It’s not until you distill everything you have that the major concepts and key points start to emerge.  You then start analyzing each of these key items and start to observe where interactions are and how they are related to each other, interweaving them into a storyline that executives can digest and understand.

The art of story is important in HR because even though we are interested in efficiency and cost savings, we are really about effectiveness.  We enable employees to grow and managers to execute, and as much as we hate it when people say that HR is “touchy feely,” the truth is that we are not a strictly quantitative function.  At the end of the day, we use the science that we have (cost studies, analytics, data) to enable increased effectiveness in process, engagement, and talent.

I obviously love talking about technology, and I’m pretty good at figuring out what data is telling me, but presenting data to executives is never the answer.  Sure a nice graph helps out, but there is always a story behind the data, and that story tells us where we have been and where we should be going.  What the data does not tell us, is what the outcomes are that we need to achieve.  We use data to inform our stories and direct where we need to get to based on HR strategy.

HR is comprised of quite a few random pieces of data, from technology enabled analytics, process outcomes, talent data, HR transactional data, etc.  HR outcomes and strategies are usually aggregations of each of these areas as individual data points combine to create overall direction and outcomes – formulating the data in such a way that it can actually give us a sense of place, direction and story is more important in HR than any other function that I can think of.

The Marketing of Snowflakes

If you ever look at a snowflake dangling from the window at your local Macy’s or Bloomingdales, realize that this snowflake is only a piece of marketing, there to draw your eye, but not an accurate representation of reality.  You see, most marketing snowflakes have either five or eight sides to them.  Nobody seems to know how or why this happened, but I suppose some marketer out there thinks that it is more aesthetically pleasing to have a five or eight sided snowflake.

The reality of the snowflake is that they almost always have six sides.  Sometimes they may have three or twelve, but those are relatively less common to the six sided variety.  The reason for the multiples of three is simple, snow is made up of water, or H2O molecules, and chemically have so many bonds to offer to other H2O molecules.  The end result is that water molecules can create snowflake structures with three, six or twelve sides.

The beauty of the snowflake is a wonderful thing.  Certainly it does draw our eye and our attention.  Certainly the thought of beautiful fresh white snow brings to mind a white Christmas, skiing through fresh power, kids and snow angels, or whatever else you have in mind.  But at the core, it is still just a marketing figment of our imagination, inaccurately portrayed.

Manager and executive dashboards are quite the same.  Often, we have planned and conceived for months or years about how to best capture the attention of our executives and bring them thoughtful HR data.  We’ve given them tools and pretty graphs, and indeed, these dashboards carry the flare and flash that can draw anyone’s attention with a state of coolness and color.  But at the end of the day, the dashboard is just the dashboard.  VP’s of HR and other executives often don’t really look at the dashboards we’ve worked so hard on.  They might glance at a particularly high turnover rate, but rather than digging through the detail themselves, they might instead pick up the phone and call they nearest HR director with an inquiry about what’s going on.  At the end of the day, they still rely on the same old mechanisms for information.  They want us to create reports, and have meetings.

The cause of all of this is particularly simple.  Executives have no use for data.  The best representation of an HR analytic, whether it be a trended graph, or some sort of drill-through crafty piece of eye candy, is still just data.  What executives want is information, and our dashboards still don’t interpret data for them.  That’s why they still need the rest of us, our reports, and the face time in meetings.

I don’t think I’m being critical of dashboards – in fact I rather love them.  But we have to understand the gap if they are to get better.  Dashboards can give execs a glance at the health of their organization, but they don’t provide understanding and diagnosis.  We need to be able to provide information, not data.