The Decline of Corporate Elitism

When I was a kid, I always hoped and dreamed that I would get into one of those Ivy League schools. Unfortunately, I was a disaster of a student (and a ridiculously undisciplined Asian student to boot). Yes, I had well over a 4.0 GPA, took 26 total AP classes in high school, but I really succeeded by my ability to test well. I never studied, barely did my homework and didn’t read assigned texts. In short, I just happened to be really lucky. But I also didn’t have the drive that it takes to get into one of the elite institutions of higher learning. Those schools, like Harvard, Yale, Princeton and Stanford, were reserved for those voted “most likely to succeed” in their high school yearbooks.

Upon graduating from college (from what turned out to be an excellent, small, liberal arts college), I didn’t land that highly desired job in consulting or investment banking. Those jobs primarily came from colleges more elite than my own. So I fought and clawed my way into situations where I would learn and grow, eventually creating this blog not as a public space, but as a place to record my thoughts so I’d remember them in the future.

I now have the pleasure of helping my clients understand how strategy can impact all manner of HR, talent and service delivery outcomes. One of the most interesting in recent years has been a focus on understanding staffing outcomes in the college recruiting area. The old conventional wisdom has always been to get the most elite candidates from the most elite colleges. After all, these are the best and the brightest, and what CEO does not tell the world that they have the best and smartest team in the world? Unfortunately, this conventional wisdom does not work out for all it’s cracked up to be.

My favorite case study comes from a global financial analyst firm. This organization recruited from one of the Ivy Leagues for years, but ultimately they ran some analytics and discovered that the Ivy League candidates not only didn’t perform better, they often performed worse than candidates from “middle of the pack” schools. The most successful candidates (those promoted into management and eventually reaching executive ranks, those who had long tenure rates, and those who became great leaders and people managers), were often from public sector universities in “Po-dunk, Middle-of-Nowhere.” The analysis revealed a single striking characteristic difference: the sense of entitlement.

It turned out that candidates from elite schools were indeed smarter, but they also had in their belief system that they could do better – no matter where they were. There was not only a continuous striving to get to the top faster, but a sense of discontent no matter how good their present state was. This ultimately lead to early voluntary terminations in less than 2 years, and a striving for job/title inflation that was counter-productive to real experiential growth.

On the other hand, candidates from “Po-dunk” were so excited for the opportunity, happy they were in New York instead of “Po-dunk” and genuinely appreciative that they somehow got into one of the elite financial firms, that they worked their butts off and formed long term relationships with their companies. Ultimately, sticking around and putting in some real effort ensured that many of the “Po-dunk” graduates made it into executive ranks, probably as quickly as the elitists did (after 6 company changes).

Another customer, an engineering firm, found that not only did the school not matter (which would seem odd in engineering), but what did matter was how well this organization could form high quality relationships with Ph.D candidate advisors. A high quality relationship with a professor guiding many engineering Ph.D. candidates was a significantly better predictor of the new hire’s desire to put in hard work and stick around. After all, if your Ph.D. advisor got you and many of your predecessors an opportunity at a job, and they are all experiencing success, aren’t you going to do the same thing?

The point being that the old conventional wisdom isn’t the formula for recruiting success anymore. The pressure high school kids face these days to get set up for life by getting into the best colleges is also creating very bad precedent and expectations. Just the idea that they think they will be “set up for life” by getting into the right schools is turning off employers. More and more of my customers are doing deep analytics to understand where their top college candidates come from, and increasingly it’s not the elite schools. If you’re still following the old conventional wisdom, it might be time to revisit your strategy.

HR Technology Conference 2014: HCM Roundtable

Every year we’re trying to figure out what’s next.  7 years ago, I started hearing about social HR everywhere, but the market really wasn’t ready.  Every HR organization thought that social was a bad idea, with personal privacy challenges looming to kill any social enterprise initiatives.  2 years ago, we all took for granted that social was going to be a part of our businesses, and this year it really seemed like social finally became its own and is permeating many of our HR processes and technologies.  It lonely took 7 years after the vendors and advisor market predicted it for it to become reality.  (LOL)

During this year’s HR Technology  Conference HCM roundtable, it was fascinating to hear what everyone was working on (it was the first question posed to the group, and I’m not trying to bash any vendor, but I am representing my opinion of the answers).

  • What was fascinating was that 2 of the vendors were talking about a great user experience (Oracle Fusion and Ultimate).  Wait a second.  We’re still talking about UX?  How did these 2 vendors get a seat at this panel and only have UX to offer up for what’s new in the product.  It’s unfortunate.  Y’all gotta do better than that.
  • 2 of the vendors talked about machine learning.  (ADP and Workday).  Machine learning was part of an overall theme of the conference, and there was a follow-up conversation in this panel about it, but these 2 vendors were the ones who brought it up as a focus area in their opening comments.  When I think about social HR 7 years ago, I think that machine learning is what the next few years might be about and it seems like 2 vendors want us to know that they’re on top of it.  What is surprising here is who the vendors were – and it shows us that there can be surprises.  It wasn’t Oracle and SAP with their deep (and legacy) analytics engines and mountains of programmers.  It was ADP (wh-wh-wh-what?!?!  I LOVE that ADP is thinking about this as they have the largest client/employee base to run analytics off of.  Maybe I don’t give them enough credit.) and Workday (ok, maybe predictable since they seem to be thinking/innovating faster than the others).
  • Last up was SAP.  Can anyone say “extensibility?”  Actually, SAP was gearing up to talk about some really cool metadata and object architecture that will create extensibility, but they got cut off from a time perspective.  Leave it to SAP to make things more complex, but if we can get to configurable extensibility, that’s pretty cool.  Honestly, I would have expected Oracle to be on the extensibility bandwagon based on their application architecture.

I’m hard pressed to say whether machine learning or extensibility is what’s next, but I’d think that all the vendors should be working on both of them.  UX is table stakes, and you should not be allowed to talk at the table (or panel as it was) if that’s what you’re working on.  My guess is that SAP will have some chops in the machine learning space, but it just was not what they wanted to focus on.  It’s also interesting that ADP and Workday were not on the extensibility front as it’s clearly a focus area for the very large customers that SAP has as its client base (but maybe that’s why SAP is so focused).

In a few vendor comments unrelated to the HCM roundtable, the HCM vendor space is going to start reaching parity in the next year.  Oracle and SAP are picking up steam and finally starting to look competitive.  First of all, lets agree that I HCM software in vendor demo booths while I was at the conference.  The following is an aggregation of vendor demos and conversations I had with conference participants.  Here are a couple of comments around gaps or deficiencies that I’m still watching out for based on those conversations:  (alpha order)

  • ADP:  I was really quite pleased to see their new UX.  I can’t remember what it’s called, but they’ll be rolling it out to all of their products so that no matter what you’re on, you’ll have a similar experience.  My concern is really still around the back end.  ADP’s ability to stitch together a common front end on top of multiple back end (and still mainframe?) systems is pretty good, and perhaps when you’re outsourcing everything but the core HCM to a best in class payroll and benefits vendor, it might not matter what the back end looks like.  Maybe.
  • Oracle:  The main question is in the UX.  It’s simply not seamless, and it goes to the point of why they were focused on UX in the panel. It’s way better than the last couple of years, but one goes from the cool “mobile apply” look and feel into a slightly different transaction screen, into a completely non-appy environment in just a few clicks.  The first couple pages are well executed, but it just feels like they didn’t finish the job as you continue through a manager transaction.  The second question is in their customer base for sold Fusion Core HCM.  As I talked to conference participants, they were getting numbers from the Oracle booth anywhere between 400 to 600 (note to Oracle, please get your story straight).  There are still a lot of conference participants wondering why Oracle is giving Fusion HCM licenses away for free if they have market demand in the 100’s of customers.  It’s just not adding up, and nobody I talked to could figure out the story.
  • SAP:  I’m pretty sure that SAP is on its way to filling a few gaps.  Certainly per the above comments, if they are working to fill extensibility gaps that its large enterprise clients will need, they are also going to figure out benefits administration, timekeeping and payroll.  I talked to one conference participant who was told that benefits administration will be available to demo this quarter, and another who said they were told it would be in Q4 of 2015.  Either way it’s coming and that’s good news.  I think SAP’s original philosophy that payroll, time and benefits get outsourced, but for the top 250 clients in size, that’s a hard position to maintain.  (I don’t consider SAP cloud payroll to be comparable to Employee Central in architecture, agile configurability, or usability, so that’s why I harp on it.  I know that SAP would disagree).
  • Workday:  Everyone has been uber positive about Workday for years.  The questions among conference participants seemed to be around the viability of their recruiting module.  Granted this is their newest module, and the top vendors seem to have the capability to innovate rapidly over a couple release cycles.  Just as I’m confident SAP is going to figure out benefits quickly, same goes for Workday recruiting.

Having said all of this, I’m actually quite pleased with the vendor space.  The last couple of years (no matter what Oracle and SAP say) have been relatively uncompetitive.  There has been one clear winner in the market, and the fact that I don’t have to say who it was is a good indicator that it’s true. I think 2015 will get a bit more competitive, but 2016 will become an all out war.  This post is definitely “negative” about what my concerns might be, but what I don’t mention is the huge progress that all of the vendors have made and the very long lists of things they have done well and right.  I’m going to get in trouble from the vendors over this post anyway, but either way, I think 2015 is going to be interesting.  More viable vendors is always a good thing.

(Last comment.  I thought long and hard whether to post this.  Some vendor somewhere is going to be pissed at me, but at the end of the day, there were only 5 HCM vendors on stage, so any exclusion is not mine.  Also, each vendor chose to talk about what they talked about.  Perhaps they didn’t have enough time, but again, if Oracle really wanted to talk analytics but didn’t get to it, that’s not my fault.  Each vendor decided what they wanted to focus on by themselves.  The opinions in the latter half of this post are based on talking to other conference participants and seeing each of the vendors demo at their booth.  Posting this also saves me the effort of writing a year end post.)

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)

HR, Twitter and Osama bin Laden

Yeah – I’m going to write about this.  I just finished watching Zero Dark Thirty on the plane, and I’m thinking back to that day.  I remember landing in the Chicago airport, booting up my phone and checking Twitter.  Scrolling through the feed, one caught my eye: “bin Laden is down.”  The tweet was more than a couple hours old at that point, but I noticed it came from a friend of mine in India.  I then proceeded straight to the United lounge where I was in absolute disbelief – they had some random Court TV channel on or something.  I asked everyone to change channels to CNN saying something like, “Guys, bin Laden is down, we need some news.”  I got blank stares and a, “Who are you and what are you smoking?”  By the time I left the club, everyone was hanging out next to the TV’s, it had finally made US media more than 4 hours after the event.

There are all sorts of Twitter analogies I love.  I love that Twitter can figure out the mood of the country every single day (probably every single minute) based on keywords.  I know that we don’t all use Twitter (hey, I’m totally a late adopter and I still barely use it to this day), but this post is really about social media and the pulse of your organization.  Hopefully you have something running whether it’s Sharepoint, SFDC Chatter, Jive or anything else.  The question is, “are you listening?”

Speed:
There are all sorts of stories these days about customers who don’t go to the vendor customer service call center, but tweet problems on-line.  Service organizations are starting to get pretty good at monitoring Twitter and responding to people to fix problems.  I’m not saying that your HR service center needs to allow tickets to come in fiat social media, but when there is a thread about how bad the health insurance is, or that managers are not listening to employees, do you find out about that first, or does someone else bring it to your attention 3 days later?  You have the ability to get a view into the problem before it explodes into something bigger that execs are now worried about, but you have to be listening in the first place.  Seriously, do you want to bring it to your exec that there is a problem, or do you want your exec to bring it to you?

Mass Collaboration:
You can’t get this on email.  Even if you are using large distribution lists, most of the people on those lists ignore those emails.  Take it from me – I’m one of them.  You can get really interesting ideas out there, but if it’s in an email thread where the content is not managed, it’s not owned by the enterprise.  Social collaboration forums not only allow mass storage of insights, but they do it in perpetuity (until someone cleans up or archives).  If we’re all sitting in front of the news waiting 4 hours to get it, that’s pretty slow and we’re dependent on the distribution channel to tell us what’s important.  If we take to the user owned collaboration forums, we get to filter insights in real time.

Engagement:
Back to this idea of pissed off employees – there doesn’t always have to be a thread about something that is upsetting any group of people.  How cool would it be if you could create an algorithm that gives you a measure of employee engagement on a daily basis (ok, maybe weekly).  Apologies to the vendors who sell engagement surveys, but if you could put together an algorithm that gave you engagement, split it up on dimensions of level, job families, pay grades, organization, you’d have a pretty powerful tool.  You might complain that you don’t have specific actions, but I’d disagree.  What is the use of an engagement survey that gives you a report every year?  Just like the crap about performance management not being meaningful, if it’s a year later, it’s too late.  On a weekly basis, you could dig into what comments are causing lower engagement scores, deal with them in the specific populations, create engagement and solutions before things escalate.

Talent Management:
I wrote about this years ago, but I think it might actually be time.  I’m totally intrigued by the idea that you can get rid of your entire competency model and just use social media.  LinkedIn is getting closer, but it’s nowhere near perfect.  I don’t want anyone tagging me with skills.  What I do want is for HR to figure out what I’m good at by looking at my social media posts inside the corporate firewall.  If I post about HR Analytics and 20 people respond, that gives HR an idea that I might be interested in the subject.  If someone posts a question about HR Analytics and I respond, and I also get 20 “likes” for my answer, I might have some expertise.  As you aggregate all the social data over time, create a taxonomy to apply against business conversations, and apply all that data against employees, you have a pretty good idea of what people are thinking about and what they are good at.

I’ll acknowledge that listening is only part of the solution – much of the other part is figuring out how to listen, what to listen to, and how to decipher what you are hearing.  There is a lot of static out there and you need good tools to get good insights back.  I also don’t know how far off social listening is for HR, but hopefully this gets us thinking.  It’s something we need to do as our organizations get more diverse globally, disconnected geographically, and technologically savvy.  Conversations are moving to social, and we have an opportunity.  Let’s grab it.

SaaS Is Here: Get Over It IT!

There was a long time ago I could pretty much build my bike from scratch.  Yeah, I could assemble everything, that’s easy.  Putting on gears, lacing up spokes onto wheels, getting the brakes on.  I even used to pick out the individual ball bearings that went into my bikes.  Then came a day when the ball bearings got sealed into cartridges making them last longer, roll smoother and easier to maintain.  In a couple years, hydraulic brakes for road bicycles will be here.  The industry has gone past my ability to build my bikes from scratch.  I can still do most of it, but for the highly technical pieces, I rely on an expert mechanic.

A few months ago, I had a conversation with one of my clients about whether they should “buy it or build it.”  Really?  I honestly didn’t know those conversations even happened anymore.  I really thought all the conversations these days were about should we use SaaS or stay on premise.  I was reminded about this as I read the 2012 HR Technology Survey from Cedar Crestone.  One of the charts noted the differences between HR, IT and executive perceptions and challenges to move to SaaS.  Number 3 for HR and Executives?  Security and Data Privacy concerns.  Of course that was number 1 for IT.

I remember when I used to work for ADP a number of years back.  This is old school, but their tax service center was in San Dimas, California… quite at risk of a major earthquake.  It was in California for a number of reasons – primarily I assume because it gave them an extra 3 hours to file taxes in the U.S.  But while ADP’s state of the art tax facility was at major risk of earthquake damage, their backup facility was somewhere on the other side of the San Andreas fault in Arizona.  I remember talk about power lines coming in from all 4 external walls, just in case some guy with a backhoe ploughed through power lines on 3 sides by accident.

I also love conversations about data security.  Let me be blunt: unless you are Citi, Amazon.com, or Walmart, you probably don’t have an entire organization dedicated to data security and the upkeep of your SAS-## (whatever it is these days).  I’m sure you can do security well, but the chances you can do it better than the organization that does it as their core business, stop worrying about it.  Back to ADP for a moment – I remember always having a personal chuckle moment when a client or prospect said to us that they had their own tax accountants, and felt better about that than using ADP.  Guys, let’s be blunt again.  ADP has probably hundreds of tax accountants, and they are probably better than yours.

Just like taxes are not your core business, you probably don’t host servers as your core business either.  SaaS is here.  Get over it IT.

Still Grappling With Data Security

Today I was going through airport security with my wife.  I got randomly selected for a screening, which consisted of wiping my hands with a cottonish fabric and sending it through the scanner that detects explosives or something like that.  After the screening, I commented to my wife, “so don’t all the terrorists know to not go to the gun range or handle their explosives within 24 hours of going to the airport?  It seems to me that this particular screen is really not a deterrent.  Any half intelligent terrorist worth their salt has got to have investigated TSA, right?  ((if I end up on some FBI watch list for this post, I’ll be both highly amused and highly irritated at the same time))

I’ve been trying to figure this out for ages.  You see, the problem is that even if you have stricter limits on access to fields and tables in your security setup, even if you limit the number of users to sensitive information, you should not assume that your data is any more secure from unauthorized sources.  All you have done is make it harder to access.  Now, I’m not saying that making it harder to access is not a worthwhile exercise.  It is.  But let’s be honest with ourselves.  Harder was not the goal.  Impossible was.

Pretty much every reporting engine in the world allows you or the user to somehow download the data.  Before we lay blame on the vendors, let’s realize that it’s our own fault – we placed it as a requirement in every single RFP, or we “ooh’d” and “aah’d” when they demo’d how easy it was to download to MS Excel.  Either way, we lose all control over data security once data is downloaded by the user.  Privacy controls are voided, confidentiality issues arise, and we have no idea where the data ends up.  Not that this is all our fault either.  People who have security access to compensation data for example should know better than to email that stuff around.

There are a couple of nice solutions though, but I’m not sure how perfect anything is since at some point most of our organizations need to have data stored or downloaded.  We could of course disable downloading, and every manager, finance person and HR practitioner would just have to pull up a dashboard and view the data in real time.  Right…  At the same time, I’ve been advocating that all HR decisions are based in facts and data, and I can envision a world where meetings get really dull when we gather executives around the table but were not able to prepare decks full of analytics beforehand.

Here are a few things you can do to improve your reporting data security:

  • Make sure managers are certified and trained regarding their data responsibilities when they become managers and every year.
  • Review your security access periodically to make sure sensitive data is being accessed by the right roles – some roles may no longer need the permissions over time.
  • Build a prominent warning at the top of reports when data is loaded to ensure that dissemination of sensitive data is a breach of security.
  • Scrub your reports frequently – you may find old reports that are run with sensitive data that is not necessary based on the purpose of the report.

This is just one of those problems I keep grappling with.  We keep giving managers and non-HR functions access to more data – I do believe the business requires it.  We want everyone to be able to make decisions in real time, but we don’t trust our partners fully either.  I’m also completely uncomfortable giving up and going with the idea that some data is just going to slip through or saying that it’s just a change management problem.  Anyone have any thoughts about what they have done?  Please ping me.

Infographics Suck

I was riding my bike around Marin (north of San Francisco) this fall, it was a bit cloudy, grey and not as bright as usual.  Just the week before, I had purchased a new pair of lenses for my sunglasses, just for this occasion, and I was absolutely stunned at the difference it made to my ride.  I felt like I was seeing the road and the vistas for the first time.  Indeed, it was simply the first time I was seeing the views with a Yellow #20 lens.  The reality is that I’d done this exact ride dozens of times before.  I commented my amazement to my riding buddies, how different everything was, brighter, more cheerful, and happy.  But alas, it was just the Yellow #20 versus my usual middle grey.

The current world seems to be in love with the infographic.  Hell, I’m in love with the infographic.  They are pretty, colorful, easy to understand, present only the key pieces of information that you need.  In 45 seconds, every one of us can be conversant in a topic with a very defined point of view.  Well, actually, this is exactly the problem.  You see, while the infographic is a very valuable tool, we should all realize that it’s there as a precision marketing tool.  It is there just to provide a point of view, not a complete conversation.  Here are a couple of things you can do to combat “infographic conventional wisdom.”

  • Take infographics with a grain of salt – statistics are useful, but remember that there is a whole book called “how to lie with statistics.”
  • Question everything – we don’t always look at the source, nor do we ponder the alternative points of view when looking at these things.
  • Evaluate the publisher – if the infographic comes from a vendor, just remember it’s a marketing tool.
  • Rely on research – infographics will continue to be a good source for quick summaries, but research with full commentaries still outvalue the quick infographic by far.

So why am I writing this in an HR blog?  As buyers of HR technology and services, if we are not already flooded with infographics, we will be quite soon.  We love these things for good reason – they are so easy to use, and marketers know it.  Hell, I’ve been known to produce an infographic when I’m presenting a business case to a steering committee.  The problem is it’s too easy to take them without full context and conversation.  90% of the time they are a single point of view only, and an alternative vendor may have statistics proving why their own software is better in exactly the opposite direction.

This great infographic from http://visual.ly/effectiveness-infographics.

InfographicsSuck

 

 

 

 

 

 

 

 

 

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.

 

 

 

 

How To Give All The Wrong Answers

As per my last post,at the end of 2012, I was doing a family vacation in Taiwan.  Being with family for 2 weeks is quite an expose into mannerisms that each of us have.  I was particularly intrigued by my brother’s questioning of my mother.  My brother would constantly ask my mother things like “why are we going to [city_name]?” instead of “what are we planning to do when we get there?” and “how much time will I need to prepare the kids to sit in the car?”  Luckily, we had my mother there fueling the ridiculous line of questioning.  90% of the time, her answers had nothing to do with the questions he was asking.

  • “Why are we going to [city_name]?” “Oh, let me tell you, when I was growing up, I used to play with my cousins there.”
  • “Mom, why are we going to [city_name]?” “Oh, did you see that beautiful view over there?”
  • “Mom, can you please just tell me why were are going to [city_name]?” “Don’t worry, you will love it.  It’s beautiful there.”

There are two items I’d like to diagnose.  First, are we actually listening to the question?  Second, did we understand the question?

The first is fascinating to me because I’m not sure we actually are listening.  Many of our reporting organizations are pure intake, create, output engines.  We grab the data that is asked for, create the report and send it out hoping we got it right.  Basically, we are spec takers.  Second question follows right after the first.  Much of the time, we don’t know why report requesters want the data at all.  We could be asking ourselves why they want to know, and if the data we are providing helps them solve a problem.  If we are really cool, we could be asking if they are even trying to solve the right problem or not.

Here are a few questions you should explore when data requests come your way:

  • How are you going to use the data?
  • What is the core problem you are trying to solve for?
  • Are there other data elements or analysis that we have that can help further?
  • Are there other correlated problems that we should try to answer at the same time?

For all intents and purposes, this post is the exact corollary of the prior on how to ask the right questions.  The problem with being a non-strategic reporting organization is that if the wrong questions get asked, the output is doomed to be the wrong information as well.  But even works, sometimes the wrong question gets asked and we still give the requestor the wrong data back.  All this does is create turn – another report request, or bad data going to managers (who in turn trust HR a little less the next time around).

In the case of my brother, he asked the wrong question in the first place.  It would have been much more advantageous had he explained why it was important for him to prepare the children for the outing, have the right clothes, have enough food along, and maybe get them extra sleep.  I’ll never know if my mother would have given him the right information in return, “yes it usually rains on that side of the island, it’s 40 minutes away, and we will be in a friend’s house so they can’t get too wild.”  But the crafting if the right answer is a tight collaboration of both sides creating understanding of what the objectives are.

 

How To Ask All The Wrong Questions

At the end of 2012, I was doing a family vacation in Taiwan.  When I say family vacation, I mean not just my wife and me, but my brother’s family along with my parents, visiting all of the senior members of the family (an important thing in Asian cultures).  There is an incredible exposure of habits and an interesting (but sometimes undesirable) analysis of where my brother and I got those habits from.  I was particularly intrigued by my brother’s questioning of my mother.  Let’s just say that getting 2 grown sons, their spouses, and our parents together creates a certain amount of strife.

Let’s also just say that my brothers’ hauling around of two young children may have added to the stress – he really needed to understand the daily schedules and what was going to happen when.  Back to the questions: my brother would constantly ask my mother things like “why are we going to [city_name]?” instead of “what are we planning to do when we get there?” and “how much time will I need to prepare the kids to sit in the car?”  (more on my mom’s response in the next post)

The problem in the questions was not the question itself, but in the thought process.  All too often, we ask questions about what we think we are supposed to know.  We want to know about turnover, headcount, spending per employee.  This is information that is useful, but does not actually inform us about what our next actions are.  Being “strategic” to me means that we have a plan, and we are actively managing our programs towards that plan.  If we’re using data that just skims the surface of information, we have no ability to adjust direction and keep going in the right direction.

I’ve often heard storied about HR executives who go into the CEO office for a meeting to present data, and all they get are questions back that cannot be answered.  Some HR teams go into those meetings with huge binders (sometimes binders that I’ve sent with them), and those teams come out still not having answered the questions.  The problem is not with the data.  The problem is that the team has not figured out what the actionable metric is, and what the possible actions are.  No CEO cares about the data – they want action that ties back to what the strategic objective is.  In other words, why do they care?

Here are a couple things you can do to craft better questions:

  • Always think about the root of the question:  HR tends to analyze at the surface more than some other functions.  We have finance doing complex correlations and marketing doing audience analysis.  We’re reporting headcount and turnover to executives.  What kind of crap is that?
  • Be a child:  Ask why/what/how up to 3 times.  Why 1: “Why are we going to [city_name]?”  Why 2: “Why do I want to know what we are going to do there?” What 3: “What do the kids need to be prepared with?”
  • Take action:  If you ask a question that can be answered in such a way that you can’t take action, you asked the wrong question.
  • Create an intake form that customers can request through: make sure you ask the right questions here to ensure they think through the process and understand what they need.

Many of the organizations I consult with have some pretty robust analytics organizations.  When I dig under the covers, they are reacting to create ad hoc reports for managers and HR business partners.  Once a quarter they scramble to create a CEO report card to depict the state of HR programs.  This state is sad to me.  We should be doing deeper analysis and diagnosis on a daily basis.  If we asked the HRBP’s what/why that wanted data for, we’d probably find there is a huge amour of quality analysis being performed in silos that could be leveraged organizationally.

 

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.

Social Taxonomies: Tagging versus Crowd Metrics

Every now and then, I’m parked at a mall, convention center, airport, and I ask myself, “now where did I park my car?  OK, so I don’t lose my car that often, but on occasion it happens.  OK, I’m not at the mall or convention center that often either.  At any rate, the appropriate action is to walk around the parking lot for a while constantly hitting the alarm button and waiting to hear that familiar chirp.  (Actually, I do that even when I know where the car is and I’m just walking over to it – no idea why…)  At some point, I’ll eventually locate the car.  The alternative, since I’d never really go to a mall or convention center or whatever alone, is the hope that someone I’m with actually remembers where the car is, or the general vicinity.  Depending on the person I’m with, there is either a high level of confidence or not, and sometimes none at all.

Here’s the problem with social enterprise.  Stuff can be really hard to find.  Let’s say that we remember that something was said on a particular subject, but we don’t remember who said it, if it was in a group message board or a blog, or even when it was.  How the heck do we find this stuff?  Even if we did remember it from a blog, the content might be 2 years old and still take a while to find.  Social tools all seem to use a variety of different search tools, but the tools that have emerged seem to deal with either tags or crowd metrics (or a combination of each).

Tagging is the job of either the content author, or content manager.  Sometimes tags can be community driven as well.  The point being that people can tag content with topics that they feel are associated with the content they are presenting.  You’ll notice that this post will return a tag of “social” and “social enterprise” among other things so that those get indexed by the blog and search engines.  It’s not an exact science like the good old dewey decimal system we all learned in elementary school, but if authors are tagging, then it’s likely to have a decent relationship.  If you give readers and the community the ability to tag, now you have even precision as the readers are also the searchers of the content and will have a pretty good idea if the original tags are off.  Every now and then on systematicHR posts I’ll actually adjust tags based on what searches are driving hits to the content.  Lastly, if you have a content manager involved that can further tag, now you have an element of standardization, so you know that similar posts will always be tagged in a similar way – in other words there are no concerns over someone tagging only “social” and a different author using “network.”  The content manager can leave the original tags intact, but would also communize the tags being used across the community.

Crowd metrics are also a wonderful thing.  For those of us who are Facebook users, we’re probably pretty familiar with the news feed that tends to launch more popular items to the top of the list.  The assumption is that if lots of people are looking and commenting on a particular piece of content, there is a higher probability that you’ll also be interested in the content.  The same goes for social enterprise in the workplace.  If many people are looking at content that you follow in some way (through a person, group, topic…) then chances are you want to see it also.  The assumption is that hits, reads, comments, thumb ups indicates some degree of quality of the content.

Things get better when you combine tagging and crowd metrics.  If you do a search for “talent management” in your social enterprise tool, hopefully it brings up the things that are not only tagged with the topic, but also finds the ones that were most popular first.  This blends not only the topical result, but also the assumption of quality as well.  The issue with this is that you can still miss content.  Some things can be mis-tagged, or some items just go unread by the crowds, and continue to appear lower in search results because of it.  Good search should also index words inside the content automatically, but that alone does not mean a high search result.

Obviously for me, the best result is if I just remember where my stupid car is.  But if I can’t hopefully some crowd intelligence in combination with my alarm clicker will work pretty quickly.  I don’t wander aimlessly in parking lots that often thankfully.

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.

 

 

Why Aren’t We Using Our Analytics?

There are everyday annoyances, and then there are just things that piss you off.  Usually the things that make me really angry are when something should have been fixed, but it hasn’t been.  Or something is happening too slowly where I should have just done something myself and been done with it.  Personally, I think I’m a pretty patient guy, but every now and then I’m shocked into anger.  Today’s topic?  NPR last week did a piece about the male and female wage gap.  Apparently it’s still there:

So, as the Washington Post notes, the authors tried to make everything as similar as possible. They tracked graduates with identical collegiate experiences, limited familiarity with the work world, and those who didn’t have spouses or children.

But the wage gap persisted.

The study found that in teaching, female college graduates earned 89 percent of what men did. In business, women earned 86 percent compared to men. In sales occupations, women earned 77 percent of what men took home.   (1)

So here’s the thing: WHY THE EFF ARE WE NOT USING OUR BASIC REPORTING SKILLS???

If we were back in 1989, I get it – it was hard to pull and aggregate all of that data.  If it was 1995, only half of us were on PeopleSoft and the other half of us were managing something so clunky I’ve probably never seen it.  By 2001, everyone had upgraded due to the Y2K non-event and we all had basic reports.  We’re a decade after this and if we all just ran a report that had 2 rows on it, we’d have fixed this already.  Row 1 = Average Wage of Males in Org.  Row 2 = Average Wage of Females in Org.  You can get more sophisticated and run this by jobs or departments.  You could run this by job AND tenure.  You could get really creative in whatever you want to do.  But here’s the bottom line:

EFFING RUN THE REPORT!!!!!  (and while you’re at it, RUN IT ON ETHNICITY TOO!!!)

And helpful hint number 1:  You might want to run the thing and give it to managers right before/during merit time so they can see just what bastards they are.  And just so we are clear, there is no hiding behind “we don’t have the budget to give you more than 3%” on this one.  This is called the equity increase.  What you don’t have budget for is the $1M lawsuit that I hope is coming if you don’t act on it.

Helpful hint number 2:  Organizations I’ve worked with on analytics always had a gender and ethnicity pay, hiring and tenure scorecard as part of their monthly or quarterly deck.  Companies who do this right are looking at this every month/quarter and know exactly where they stand.  They won’t get it right 100% of the time because we’re still looking at aggregated data on a scorecard, but at least they know they are on target as a whole.

What’s sad, is that while there are a variety of reasons this stuff happens (some managers really do suck, sometimes women may not negotiate as strongly, whatever), the effects of this are pretty serious.  Whether it’s gender or race, the impacted have about 10% less money in doing the same job to pay their bills, buy a nicer house, and sending kids to better colleges.  Guys, this is a big big deal, and we are actually empowered to do something to expose it, and are empowered to bring this to light.  There’s no excuse if we don’t.

</end rant>

  1. Korva Coleman, NPR.org. October 24, 2012, “ Equal Pay For Equal Work: Not Even College Helps Women”

(apparently I’ve forgotten how to APA footnote and I don’t care to look it up…)

HR Technology Conference Reactions: Naomi’s Master Panel – SaaS

Talk about a stacked panel.  This one was moderated by a thought leader, and staffed by thought leaders.   They included:  MODERATOR: Naomi Lee Bloom (Managing Partner, Bloom & Wallace), Steven Miranda (SVP, Applications Development, Oracle), Mike Capone (VP for Product Development and CIO, ADP), Sanjay Poonen (President Global Solutions, SAP), John Wookey (EVP, Social Applications, Salesforce.com), Stan Swete (CTO, Workday), Adam Rogers (CTO, Ultimate Software)

I’ll admit that towards the middle, it got a bit salesy as the vendors started spewing stats about how great they were and what amazing market reach they have, but I’m ok with that for the 45 minutes of gold nuggets I got first.  Even the panelists eventually admitted that they could have argued with each other more, but I’m ok without that as well.  Here’s what I heard.

Theme #1:  Data aggregation across clients. I should say I told you so (I think I just did), but I was talking about this years ago.  What is really cool about this is that so many of the SaaS vendors now have the ability to mine data across their client base.  The data in a perfect SaaS world should be totally standardized since everyone is on the same software, so some instant benchmarking should be in order.  I don’t think there’s much risk to be able to aggregate and share the data, but some opt-in by clients is a reasonable tradeoff, and I’d expect that most clients would opt in with the understanding that none of the client specific stuff would be shared outside of an aggregated format.  Imagine a world where all of the analytics the vendor is providing can also show a benchmark with a push of a button.  Your CHRO pulls up a turnover trend for the last 12 months, and with a click of a button sees the trend lines for all other clients and clients in the same industry.  All of a sudden, your CHRO is hunting you down trying to understand why your turnover rates are suddenly trending higher than competitors.  This isn’t reality yet, but we could be close.

An example that was quite interesting was the ADP payroll examples.  We all know that the ADP payroll numbers come out ahead of the government jobs reports.  The government surveys a number of people every month, but ADP has an exact number of paychecks they cut.  Which one do you think is more accurate and which one do you thing most people trust?

Aggregation also benefits the vendors.  The vendors have a view into what every client is using and not using.  Thomas Otter came up with a wonderful new term this week: SaaS = Shelfware as a Service.  The truth is that vendor can now see what is in demand, what products need enhancement, and what products where the investment opportunities are.

Theme #2:  Realign focus. We’ve spent over a decade being worried about enhancements, the next patch or upgrade, and how we manage internal hardware and networks.  Let’s get one thing straight – all of that is gone.  If you no longer have 5-10-15 headcount worried about the management of the application, you have that many extra heads to worry about optimizing business processes or how to engage more users.  Instead of worrying about the request that came in from APAC and how you are going to address a small piece of code for them, you can worry about what the bigger picture is and trying to collaborate with your vendor to have it deployed.

Theme #3:  Shelfware. We talked a little bit about shelfware in theme 1, but I think it goes beyond knowing what gets used and unused.  Organizations used to have trouble with buying applications that were never deployed.  Or buying applications as part of a package that were never deployed.  The problem is a bit different now.  With 2-3-4 releases a year, clients just can’t keep up.  One of the great quotes of the conference, “God could create the world in 7 days because he didn’t have install base.”  Since everyone is on one system, you don’t have to worry about coding for multiple upgrade paths, multiple back end databases, etc.   It’s also a great thing that everything comes turned off, but after a year, there is so much “stuff” not getting used that the planning process of how and what to deploy can get pretty complex.   Vendors have to be really thoughtful about what functionality to deploy, and one of the ways many are dealing with this is by creating social communities where customers can vote on what functionality gets released next.  By doing this, vendors minimize the impact of releasing functionality that nobody wants.

Theme #4: Social. Social was the theme no matter where I went at this conference.  That’s not a bad thing, it just shows where everyone’s brains were.  Partly because of the SaaS strategy and not having multiple environments to grapple with, mobile applications can be created quickly and with little fear of platforms.  Similarly, social may be threaded into processes and functionality more seamlessly, although with so many customers going with third party social tools, this might be getting hard to embed in SaaS HCM business processes.  At the end of the day though, the idea is simple.  Engage your employees where they are comfortable engaging and where they do their work.  This might mean extending functionality to mobile, or creating tools to facilitate conversations in social tools.  Unfortunately, in today’s worls this might also mean embedding ways to perform actions in email since that is where people are comfortable today.

 

HR Technology Conference Reactions: Predictive Analytics

I’ve always thought I was pretty good at analytics.Not being a practitioner who is sitting in the middle of data all the time, I get more time to just think about the type of analytics that it takes to really run the business.  It’s been a really long time since I discounted the usefulness of things like time to hire preferring things like quality of hire (efficiencies versus effectiveness measures).  But I’ve always fought with predictive analytics.  In my opinion, they don’t really exist in HR yet.  We can trend our data and draw a trend line, but that does not predict our future – it simply tells us that directionally, something is going to happen if we don’t change course.  I’ll admit that I walked into this session with a great deal of skepticism, I walked out with some great insights.

The panel was made up of some great speakers.   Moderator: Jac Fitz-enz, Ph.D., (CEO, Human Capital Source), Laurie Bassi, Ph.D., (CEO, McBassi & Company), John R. Mattox II, Ph.D., (Director of Research, KnowledgeAdvisors), Eugene Burke, (Chief Science & Analytics Officer, SHL), Natalie Tarnopolsky, (SVP, Analytics and Insights, Wells Fargo Bank).

Theme #1:  Descriptive, Predictive, Prescriptive. Let’s start with some definitions as the panel did, but I’ll use a tennis example.  I don’t know if anyone has been watching the last few grand slams, but they have been using a good mix of all these types of analytics.  Descriptive is simple.  Roger Federer has one 16 tennis grand slams.  (I’m guessing as I’m on a plane typing this).  Predictive is next and basically tells us what our destiny is going to be.  Roger’s record against Nadal in grand slam finals has not been particularly good.  If Rafa is on his game, hitting his ground strokes with the huge topspin he has, Roger is going to have to figure something out or lose again.  Here is where the last few opens has been interesting.  The broadcasters will sit there with the stats and say things like, “If Roger can get 67% of his first servers in, he has a 73% chance of winning” or “Roger needs to win 55% of Rafa’s second serves to have a 59% chance of winning.”  Now we have prescriptive – the specifics of what to do in order to change our destiny.

Theme #2:  Engagement. We probably focus in on this a bit too much.  It’s not because it’s not important, but it’s not specific or defined enough.  I mean, we all have a definition in our heads, but for 99% of us, it’s fluff.  My definition of engagement is the intangible quality that makes an employee want to provide that extra hour of discretionary work when other non-work opportunities exist.  Total fluff, right?  We can provide some correlations around engagement.  If engagement increases by 1%, then turnover decreases X% and so on.  What it provides is a great predictive measure, high level as it may be.  We know we need to increase engagement, and it is indeed important.  But it’s not the key measurement we have all been lead to believe will solve all our problems.

Theme #3:  Predict winning. OK, so if engagement is not the key metric, then what is?  Well, I have no idea.  I’m not being snide, I’m just saying that it will change for every single organization.  If you are (mall) retail organization, then having really good salespeople might be what hits the bottom line.  You could run the numbers and find out that if you rehire sales that worked for you the summer/holiday season last year, those salespeople are 20% more productive, whereas engagement reduces turnover by 1.3%.  Which metric are you going to focus on?  Right, how do you get those experienced salespeople back?  Instead of spending $1 on engagement, you could get 5 times the ROI on that same dollar elsewhere.  What we want to do is not predict outcomes.  We want to predict winning and understand what our highest contributors to winning will be.

Let’s take another example, this one from the panel.   Let’s say 5% of your workforce are high performers, but you can only give 3% of them promotions this year.  You also know that the 2% of top performers who don’t get promotions will likely leave the organization.  Now you have a problem.  You can’t afford to promote these people, but the cost of replacing top performers is extraordinary.  Analysis like this quickly leads you to decisions which are actionable.  At the end of the day, we need to compare our top drivers against our weaknesses to really figure out our greatest opportunities to invest in.

Theme #4:  HR can’t do it. This part sucks.  Towards the end of the session, we walked through a statistical model.  Yeah, we can end this post right here, but I’ll continue.  The rather brilliant by HR terms model was presented by Wells Fargo.  Go figure an ex-finance person working at a bank would have this all put together.  The point being, this was an ex-finance person, and the bak part is ot wholly irrelevant.  All the stuff I said above really makes great sense.  But when it comes down to executing it, HR in most organizations does not have the skillset to execute on it.  We don’t have very many statisticians in our HR staffs, and even if we did, HR executives would have a hard time seeing the vision and have the willingness to implement these technologies and models.  All is not lost however.  Finance has been doing this stuff forever.  I mean, I’ll bet you anything that if the interest rates drop by 1 basis point, Wells Fargo knows within seconds what the impact on profits are for savings, mortgages, etc.  Can’t we have/borrow/hire just a few of these guys?