Workforce Analytics – Alex Hagan and Steve Pell in Conversation.

Last week I had the honor of being invited to speak at the Melbourne HR Talent Community with Steve Pell of Intrascope Analytics. Steve and I discussed analytics for HR, why “Big Data” is all the rage, and why most of the valuable data about your workforce is already sitting inside your internal systems (Big Data or Small Data, it’s all about the insights). The below video is 8 minutes extracted from the 45+ minute conversation.

Steve is the Founder and CEO of Introscope Analytics.   Intrascope’s mission is to help business and HR leaders connect the dots between people strategy and business results, by delivering powerful insights about workforce behaviour in real time.

The Melbourne HR Talent Community isn’t your usual networking group.  It now has over 600 members, and 70+ members meet monthly to discuss a broad range of current HR issues.  If you think networking is a 4-letter word, you can’t count but you can enjoy their monthly meetings and the conversations in the LinkedIn group.

Transcript (provided by Rev.com):

Alex Hagan:     Big Data is used very extensively in the consumer space to take all of these different interactions that consumers have with brands, and find patterns in that data to essentially, to put it cynically, sell better to those people.  So, the type of data that people are crunching in the consumer space are things like Visa that apparently can predict whether you’re about to get divorced based on your spending patterns of those of your spouse.

Steve Pell:       But, I think just to jump in there that kind of takes it to a scary level that can be intimidating for a lot of people to think about …Or you read the case about Target where they were talking about predicting pregnancy, but do you want to go back to the way that we kind of talked about it in terms of, the best way to think about what everyone’s talking about, when they talk about big data, is really just understanding patterns.  Instead of thinking about this as being a huge scope of plugging together everything in your digital life, it’s really just looking at data whether it be big or small, and finding patterns about people.

Alex Hagan:     And that’s the thing about big data as well, it’s increasing the consciousness that using data to support your decision-making, leads to better decisions.  So, whether you’re analyzing segments from Twitter feeds or whether you’re analyzing Visa transactions, and all this very complicated stuff that people are doing, the value for HR is realizing that there is a lot of data we already have within our systems; within our organization, that can give you insights and we’re not getting today.

Steve Pell:       The first take-away is size doesn’t matter in this game.  Big or small it’s about finding the patterns in the data.  Whenever anyone says big data, they’re usually it as a term for basically finding patterns in data, but it doesn’t really matter about scale here.  Don’t get caught in the technicalities.  Do you want to jump into; I think the ‘Moneyball’ analogy is a really good one.

Alex Hagan:     Has anyone seen the film Moneyball?

Speaker 3:       I’ve seen it yes.

Alex Hagan:     So whenever someone talks about data particularly for HR, it’s kind of a gimme – someone wrote a book, and then made a film about analytics and using data to support HR decision-making.  So you can’t hear about analytics for HR without hearing about the ‘Moneyball’ analogy because it’s a great one.

Moneyball essentially was about the Oakland A’s in their 2002 season, reinventing what success looked like for that organization, and then hiring to that profile of what meant success.  The common wisdom was that if you want to put together a team of superstars, they could all hit home runs, they look really good on the field, they were good for the marketing dollars, but when they analyzed the data, what they actually found was, those things didn’t matter so much, as just getting on to the next base – however you did it.  You could walk, you didn’t have to hit a home run.  The measures of success; what actually drove how many games that you won, was much simpler than that.  A hundred plus years of the way they’ve been recruiting these people was wrong essentially – or at least efficient.  What they did was realigned all of their talent acquisition strategies, which is essentially what they were, and they found they could get three players and pay them a quarter of a million dollars a year each, still a pretty good salary I would suggest, but that would get them the same result as one superstar player that they would pay seven million dollars a year.  In that way, they were able to compete.  They had a payroll budget of thirty two million, I think it was, and they were able to compete effectively with teams like the Yankees who had a hundred million dollar payroll.  So, three times their payroll.  They did it essentially through data.

Steve Pell:       I think frankly for me, one thing that’s really meaningful about using Moneyball as an example, is that to use the HR analogy, none of the day-to-day stuff really changes.  You’re still stepping up to the plate.  You’re still hitting, you’re still pitching you’re still throwing, in the same way that the day-to-day practice of what happens, from a transactional HR perspective, doesn’t really change.  Where this changes the game is at the strategic level.  When you’re planning for the future of the business, and when you’re dealing with the C-Level.  That’s where this fundamentally changes the game.  But in the day-to-day level, not so much, you’re still doing the standard practices that people know and recognize as HR.  That’s kind of I think, an important distinction to make.

Alex Hagan:     I think what you get is the ability to find out where those practices are actually delivering value for your organization as well.  So, to give you an example, does anyone here use psych assessments in recruiting within their organizations?  Okay.  So, psych assessments are used to predict who’s going to be a top performer once they become an employee and get into your organization.  But how many people have actually connected that predictive value to results once …one person, (laughing) a psychologist studying for your PhD so, that’s the one person who’s done this.  (Laughing)  So, we’ve got all of this data available to us.  We have the results of the psych assessment, and then in the long-term we know how well these people perform, how long they stay with the organization, or whatever it is that’s your measure of a successful candidate.  But actually connecting those two can give you a great deal of value.

Steve Pell:       There will be so much more valuable data sitting within the organization than outside the bounds of the organization that focusing efforts there because it’s so much deeper in terms of you have these longitudinal, the history of data where someone’s contributing inside of an organization between a couple of hundred, and a couple of thousand pieces of data a day potentially.  If you go external and look for these data points you might get one or two.  One of the really interesting things we can do is say, this is success.  We’re not going to tell you what success looks like.  You give us your talent ratings.  What is it in the way that people are behaving that is leading to that outcome?  What are the patterns that are happening at three, six, twelve months prior leading up to that review, that are different across your review levels?  In some of the cases that can be as simple as having … I commonly see major difference in senior sponsorship, so you look across the people you’re giving really high performance reviews to, and people you’re giving really low performance reviews to, and there’s just a really big gap in how many people at senior level in the organization those people have access to.  It’s common sense but when you quantify that, and say that there’s this 60% gap between these people, you can go out to the workforce and start talking about practical things that you can do to really push yourself potentially up those performance categories by doing the links.

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Are we Jumping the Shark on Big Data for HR?

Jumping the shark is the moment in a TV show when it begins a decline in quality that is beyond recovery, where the writers use some type of “gimmick” in a desperate attempt to keep viewers’ interest.  As a result, the show loses credibility and starts its long decline into irrelevance.  The term Jumping the Shark is actually named after this scene in Happy Days, where Fonzie did actually jump a shark on waterskis, inexplicably wearing both a leather jacket and swimming trunks at the same time:

Big Data Bandwagon

All aboard the Big Data Bandwagon

Lately I’ve been seeing a lot of HR vendors jumping on the Big Data bandwagon, and I wonder if as an industry, we’ve jumped the shark on this one.  Many of the articles and whitepapers I’m reading are describing Workforce Analytics, but claiming to be Big Data.  Some of them go to great lengths to define Big Data in ambiguous and overly-generic terms, so that they can claim to be offering a “Big Data” solution.  Let’s be clear – Big data is a collection of data sets so large and complex that it becomes difficult to process using traditional database management tools.  Think Sentiment Analysis from Twitter feeds; Behavioural Analysis, or Resume Screening for structure and soft skills – these data sources are largely unstructured, and require non-traditional approaches to data analysis like NoSQL and R.  

If you’re crunching internal data about your workforce from your HRIS and ERP systems, even if that data is coming from multiple sources, you’re not doing Big Data – you’re doing Workforce Analytics.  The good news is that:

  1. There are some really exciting potential applications for Big Data in the Workplace; and
  2. Workforce Analytics generates some meaningful and actionable insights for organisations, and is still gaining traction – though it’s been around for much longer than the term “Big Data”.

My concern is that Big Data for HR will go the way of Gamification for HR – another trend where some vendors will add token functionality and claim to be in the space for marketing reasons.  The result of much of this effort will be that, like gamification, clients will become disenfranchised with the field because it won’t deliver results.  The reality is that both gamification and Big Data have great potential – for the right organisations, and using the right tools – but you need to sort out the marketing spin from the significant offerings from vendors who understand and embrace the potential of these concepts.  If you really want Big Data, run a competition on Kaggle.  If you want actionable insights from analytics and, like 95% of organisations out there, aren’t suited to Big Data, then what you’re looking for is Workforce Analytics.

Big Data has big potential – just don’t jump onto the bandwagon until you know how to play an instrument – and remember, the plural of statistic is not strategy.

Jumping The Shark

Agile Workforce Analytics workshop at the Australasian Talent Conference

Australasian Talent Conference

Australasian Talent Conference

I’m happy to announce that I’ll be speaking at the Australasian Talent Conference from May 28th – 30th in Sydney.  The theme of the the conference is “Agile Talent Management”, and I’ll be running a 3-hour workshop on Agile Workforce Analytics, focusing on how organisations can uncover actionable insights into workforce challenges and opportunities.

Organisations are constantly looking to improve business performance, and doing more with less is critical. Agile workforce analytics enables HR to strategically lead the business in this improvement process. Participants in the Agile Analytics workshop will learn how to uncover actionable insights from the data they have about their workforce today; the ways in which statistics mislead us; and discuss the impact of Big Data on the future of talent management.

More information can be found about the conference here, and other places I’m speaking here.

Principles, Laws, and Effects at Work

Laws

Laws

In an earlier post, I wrote about The Peter Principle – the concept that individuals are promoted to their own level of incompetence in an organisation.

Here are some of my other favourite principles, laws, and effects:

Parkinsons Law “Work expands so as to fill the time available for its completion.” (tweet this)

Hofstadter’s Law – “It always takes longer than you expect, even when you take into account Hofstadter’s Law”. (tweet this)

The Dunning–Kruger effect is the concept that if you’re not very good at something, you’re also not very good at recognising that.  It explains why people who are unskilled in a particular area sometimes rate their own ability higher than more competent people.  (over-confident and under-competent)

Putt’s Law – “Technology is dominated by two types of people: those who understand what they do not manage, and those who manage what they do not understand”.

The Dilbert Principle (from Dilbert creator Scott Adams) is an adaptation of the Peter Principle – paraphrased, it states that companies tend to systematically promote their least-competent employees to management in order to limit the amount of damage they are capable of doing.

Goodhart’s Law – “When a measure becomes a target, it ceases to be a good measure”.  (tweet thisThis is something of particular relevance to Workforce Analytics, and something which I spoke about in The False Proxy Trap.

Most people know Murphy’s Law – “Anything that can go wrong, will go wrong”, but may not know there’s a related law,  Muphry’s Law – “If you write anything criticizing editing or proofreading, there will be a fault of some kind in what you have written” (tweet this)

The Pareto Principle is another well-known one, usually referred to at the 80/20 rule – for many events, roughly 80% of the effects come from 20% of the causes (80% of the revenue from 20% of the clients; 80% of the problems from 20% of the clients – not necessarily the same ones).

Are there any other principles, laws, and effects that should be added to the list?

The Conference Board

New Appointment – Guest Faculty at The Conference Board

The Conference Board

The Conference Board

I’m honored to have been asked to be Guest Faculty for The Conference Board’s Strategic Workforce Planning Academy, and my first presentation, on Workforce Analytics, will be at the New York Academy on the 17th / 18th April, 2013.

The Conference Board Strategic Workforce Planning Academy helps participants to develop and refine their talent processes and programs, aided by peers and seasoned SWP practitioners. Small classes, personal mentors, and face-to-face and virtual meetings over several months familiarize participants with the latest research in talent supply and demand, flexible labor strategies, and practitioner-identified competencies.

More information can be found about the academy here, and other places I’m speaking here.

The False Proxy Trap

Back in November, Seth Godin wrote:

“Sometimes, we can’t measure what we need, so we invent a proxy, something that’s much easier to measure and stands in as an approximation.”

We do this all the time in HR out of necessity – we measure employee satisfaction because there’s a connection between satisfaction and productivity, for example; and it’s difficult in many (but not all) roles to measure productivity directly.  Godin goes on to explain how this can become a problem when we focus on the proxy (in this example, employee satisfaction) and forget the goal (in this example, employee productivity):

“…When we fall in love with a proxy, we spend our time improving the proxy instead of focusing on our original (more important) goal instead”

I believe we often fall into this trap too – being obsessed with employee satisfaction metrics as if they are an end in themselves, forgetting that the point is to increase employee productivity – and that:

  1. There are many other paths to boosting employee productivity; and
  2. Not all of the ways to increase employee satisfaction will also increase employee productivity.

What are some other examples of the “false proxy trap” in HR?

(This post originally appeared at strategicworkforceplanning.blogspot.com, the other place I blog at from time to time)