The true goals of HR Big Data projects

Buried near the end of this fairly standard but still pretty interesting piece on how software giant SAP is deploying Human Capital workforce analytics solutions in their internal organization from their recently acquired SuccessFactors product suite is perhaps one of the most clear, coherent, and instructive observations about the goals (or what should be the goals), of any HR organization embarking on an analytics or (buzz work blog police look the other way) 'Big Data' project.

Here's the quote from SAP's Helen Poitevin:

We see this (the implementation of modern workforce analytics solutions) as a transformation for us first, moving from being specialists in extracting data from our systems, to being specialists in answering workforce related business questions.

I know that this seems like a kind of overly simple and somewhat of an obvious point of emphasis, but I think it is one that serves to remind those of us that like to talk, read, or prognosticate about how Big Data will have a truly transformative impact on HR professionals, workforce planning, and human capital management need to remain mindful that collecting more data, and even making the extraction and presentation of that data simpler and even more beautiful, is only the first step in the journey to realizing better business outcomes.

The goal of these analytics and Big Data projects, as the SAP article makes plain, is not just the ability to organize, describe, extract, and present workforce data (which in truth are necessary and important steps), but to leverage that data, to have the data lead to the asking of the right questions, to illuminate a path towards answering these questions, and to help the organization understand and relate the story that their human capital data wants to tell.

Again, the SAP piece makes it clear what their goals are, and what has to be the end-state for HR analytics and data projects:

(the analytics projects) represents a transformation for our business, by virtue of leveraging data-based insights and analysis about our workforce to make better, more sustainable decisions

Again, you probably already know this. Probably.

But it is a telling reminder just in case you've let your goals slip a little, or if you want to (or feel like you have to) claim victory with the initial successes in your analytics programs. 'Look we have reports!'

You're not really there, (and hardly anyone is yet), until the workforce data becomes an essential part of how your business makes decisions, and is not just a set of cool dashboards or a slick set of charts on an iPad app.

Have a great week all!

Off Topic: Infographics of the 1870s

If you are a data/design/visualization mark like I am, then I apologize in advance for the half hour or so you are about to waste on the amazingly cool A Handsome Atlas site.

The clever folks at Handsome Atlas have taken several old government and census documents from the late nineteenth-century, (primarily The Statistical Analysis of the United States, published from about 1870 - 1920), and breathed new life into them, by creating a user-friendly tool for viewing the old works close-up, and in high resolution.New York, 1870

Don't really get why this is cool?

Then spend a few minutes looking at this beautiful chart/infographic titled 'Gainful Occupations and also as Attending School' , a look at employment and education across the states taken from the 1870 census data, (a small snippet of this graphic appears on the right of this post).

The Handsome Atlas site is full of amazingly interesting and detailed data tables, charts, graphics, and visual analyses of demographic, statistical, and economic data that was compiled in the census and published in The Statistical Analysis of the United States. With a big assist to the technology and presentation developed at Handsome Atlas, this data serves to remind us that the current fad and fascination with infographics and data visualization have their roots in the past.

Infographics and other visualizations help us, mostly, to make more sense of the world - breathing life and creating dimension, contrast, comparison, and most importantly, interest in data sets. 

We want to better understand the world around us certainly, and that longing and need for understanding is definitely not only a modern phenomenon.

If you take a few minutes to play around on the Handsome Atlas site, please let me know what you think.

Have a great weekend!

'There isn't any more truth in the world than there was before the Internet'

I've been grinding through Nate Silver's book 'The Signal and the Noise' over the last few weeks and while it can, at times, get perhaps a little too deep into some dark statistical alleys, overall it is a fascinating read, and one I definitely recommend if for no other reason than for an excellent chapter on handicapping NBA basketball games.

If there is one major theme or takeaway from the book for me, I think it is best articulated in this quote, about two-thirds of the way through the book, in a chapter about how difficult it can often be in making sense of data, a problem only getting worse as the amount and availability of data continues to explode:

The US Government now publishes data on about 45,000 economic statistics. If you want to test for relationships between all combinations of two pairs of these statistics - is there a causal relationship between the bank prime loan rate and the unemployment rate in Alabama? - that gives you literally one billion hypotheses to test.

But the number of meaningful relationships in the data - those that speak to causality rather than correlation and testify to how the world really works - is orders of magnitude smaller. Nor is it likely to be increasing at nearly so fast a rate as the information itself; there isn't any more truth in the world than there was before the Internet or the printing press. Most of the data is just noise, as most of the universe is filled with empty space.

In 2013 I promise that you, as an informed, and opportunistic Talent professional will be hearing, seeing, talking, and thinking about Big Data. Data about job ad posting, data about talent assessment scores, data about compensation and retention, data about engagement, data about performance, and maybe even data about data. 

As I wrote a couple of weeks ago, most organizations have plenty of data. More than they know what to do with. And the more they collect, as made really clear in the example above, the chances are high that it won't lead to a faster discovery of the truth - it will just unearth more paths to explore.

Which sometimes, certainly, might be needed, but other times, and maybe most of the time, only results in more ways to get lost.

Don't get caught up chasing data just to have more data. The truth isn't going anywhere, and once you think you have it figured out, and feel that the data you do have supports your beliefs, then you'd probably be better served acting, rather than collecting even more data. 

Have you read The Signal and the Noise yet? Better get on it, just in case it becomes the 2013 version of Moneyball, and you won't want to feel left out!

You probably already have plenty of data, Big or otherwise

I really wanted to title this post - 'There is almost certainly too much crap on your iPhone', because that is what I was actually thinking about before writing this post. This was in recollection of a few wasted moments the other day when I simply could not find the particular app on the phone that I was looking for. But then a blog post about my frustration with my inability to properly operate a phone seemed the most dire kind of post - self-indulgent, inconsequential, and worst of all - boring.

But this silly little example, the fact that I've put too many apps on my phone, combined with a lack of organization or semblance of order, with a sprinkle of 'I had a BlackBerry for so long, I am still trying to figure out how to use this thing', and I've ended up (at times) squandering the opportunities that having access to an incredible resource and range of applications should represent.Remember how your phone once looked?

The best project manager I ever had, when trying to run a year-long, 50-person plus, and technically complex systems integration and implementation project had a general rule of thumb he followed to help manage what threatened to be an impossibly growing 'Issues list.'  His rule?  No project team member was allowed, after the initial requirements discovery period was complete, to add a new issue to the list, unless he or she could prove an existing issue was closed, or was no longer an issue after all.

This rule, and the discipline it instilled in the process and the team, served to force the team members to think really critically when new potential issues arose, and kept us focused on making consistent progress against what issues had already been raised. It was not a perfect system, and the project manager did make an occasional exception to this rule when it was essential, but it basically worked. 

Why bring up an old project manager's quirky practice in a post that seems to be about my inability to use my iPhone and with a title vaguely alluding to one of 2013's 'You might already be sick of it' terms, Big Data?

Well, because for the same reason you and I have too many apps on our phones and belong to too many different social networks and sites, and spend way too much time checking for likes, follows, and retweets - Big Data at work threatens to create even more complexity, confusion, and chaos if we are not careful.

So as 2013 starts here is my first recommendation for how to approach Big Data for HR - start by figuring out just what data you already have, have been routinely collecting either by design or as a by-product of another process, and take some time to consider what kinds of insight and value could be gained by simply asking some simple questions about this information.

My guess is just like you already have 'enough' apps on your iPhone, (or at least have a few you can happily set free), you probably have plenty of internal data to commence your own version of a Big Data project without launching some kind of new initiative to collect even more data.

So that's my advice. Take an inventory. Ask around to see what data folks are collecting on their own spreadsheets. Talk to the creepy guy in IT once in a while. See what additional information is locked up by your Payroll and Benefits providers.  Start there.

And after that, and only after that, start looking for more data.

Now, I need to run I have a few more screens of Apps to delete.

Have a great week all!

Cause, correlation, and chemistry

I am willing to be you have probably read, heard, or even repeated the following admonition in the last few weeks:

Correlation is not causation.

Here's why I think this assertion need to be retired, or at least pushed off to the side and filed away for a while with your Vanilla Ice CDs, He-Man and the Masters of the Universe figures, and Gloria Vanderbilt jeans.You have the power.

First, on the inability of correlation, i.e. lining up two sets of data that seem to track in the same direction and making a claim that one event or activity 'causes' the other, well sure, I think everyone understands that trap.

After seeing everyone at the State Fair rocking their bad tattoos, and therefore thinking that getting a bad tattoo will make one attend the State Fair is not a conclusion most rational observers would reach.

But the problem with the 'Correlation is not Causation' admonition is that it has the effect or shutting down the debate and stifling the potential discovery of useful information. A strong correlation between two related and relevant data series may not imply or prove causation, but it probably implies something. And that something might just be really important for us to understand - say the correlation between the course of study our last dozen newly promoted employees took, or the relationship between managers that successfully completed the latest leadership development program and the 12-18 month success of their team members.

In HR and Talent Management I am not sure the goal should be to try and 'prove' any one thing can actually cause another thing to happen anyway. We are dealing with people, not robots, (not yet anyway), and attempting to make sense out of interpersonal relationships, motivations, rewards, and capabilities. It is, in many ways, much harder than sitting in a chemistry lab tracking how agents react to one another. 

If I remember my high school chem lab accurately, loading up a beaker with a few solids and enough unstable acids and stopping up the top caused it to explode every time.  People, while often predicable, are not always that consistent.

The last warning I will raise is that team 'Correlation is not Causation' like to use this conclusion as a fake scientific argument against any proposals or ideas that they disagree with, or did not come up with themselves.

Here's a simple example:

'Hey, I noticed last week when his car was in the shop, Jake got a tremendous amount of work accomplished working from home - maybe we should explore letting some of the other developers do some teleworking?'

Can we know for certain that working from home was the reason for the spike in Jake's productivity?

Of course not. 

Might there have been a dozen other factors that might have been more responsible for the increase in output?

Sure.

Does your organization have the time or capacity to set up highly controlled experiments to try and figure it out - assuming that is even possible?

No way.

In science, proving causation might be the goal, the desired end state, but in Talent, we are much better served finding the correlations, using our understanding of work, people, and the world, and seizing on the ones that make sense for our business and our teams.

What do you think? Do you ever drop the 'Correlation' bomb around the office?