Commonizing Meaning

I have some favorite phrases that I’ve been picking up for years.

  • “Eh, voila!” universal for “eh, voila!”
  • “Ah, asodeska” Japanese for “I understand”  (sp?)
  • “Bo ko dien” is Taiwanese for highly unlikely or that’s ridiculous. (sp?)
  • “Oh shiitake” (shitzu is also appropriate), is an imperfectly polite way of saying “oh &#!+”

Basically, these are phrases that i love, but at least the latter two are meaningless to most people i say them to. I could of course go to Japan and most people will know what I’m talking about when i tell them I understand them, but they will then look at me funny when i exclaim in the name of a mushroom in anger.

We face the same problems when we talk about data calculations in HR. The most common of which is the simple headcount calculation. “Simple?” you ask. I mean, how hard can it be to count a bunch of head that are working in the organization on any particular day, right? The smart data guys out there are scoffing at me at this very moment.

First, we put on the finance hat. Exactly how many heads is a part time person? HR exclaims that is why we have headcount versus FTE. But finance does not really care, and they are going to run a headcount using a fraction either way.

Second, we put on our function and division hat. Every division seems to want to run the calc in a different way. And then there are realistic considerations to be made, such as the one country out there that outsources payroll, and does not have a field to differentiate a PT versus FT person. or the country that has a mess of contractors on payroll, and can’t sort them out.

Then you put on the analytics hat, and realize that when you integrated everything into your hypothetical data warehouse, the definitions for other fields have not been standardized around the organization, and you can’t get good head counts of specific populations like managers, executives, and diversity. I mean, is a someone in management a director and above? Or is she jut a people manager? How many people does she have to manage to be in management? Are we diverse as an organization simply because we have a headcount that says we are more than 50% people of color even though 2000 of those people are in Japan where the population is so homogenous that any talk of non Japanese minorities is simply silly?

Then you put on your math hat and some statistician in the organization tells you that you can’t average an average, or some nonsense like that.

So the Board of Directors comes to HR and asks what the headcount of the organization is. You tell them that you have 100,000 employees, plus or minus 10%. Yep, that’s going to go over really well.

I’m not saying its an easy discussion, but all it really takes is getting everyone into the same room one (OK, maybe over the course of a couple of weeks) to get this figured out. I’ve rarely seen an organization that is so vested in their own headcount method that they can’t see the benefits of a standardized calculation. I fact, most of the segments within are usually clamoring for this and we just have not gotten around to it yet, or we think they are resistant. In the end, it’s really not so hard, and we should just get to it.


How to Read a Newspaper

So, when I get on a plane, I often have a newspaper with me.  Whether you are on a plane, train, or anywhere with close quarters with other people, there is a bit of etiquette involved, and a standard trick that frequent travellers are supposed to know about.  Adherence with this trick is unfortunately minimal however.  The trick is as follows:  Take the paper as it was delivered, and unfold just the middle crease without opening the paper – you have only page 1 in front of you.  Fold the paper in half lengthwise and backwards, you should be able to see the left half of page 1.  Using this fold down the middle of the paper, you can read the entire paper without ever bothering the people sitting next to you.

When it comes to data, keeping everything in it’s place and not dispersing data into unwelcome areas is paramount.  HR data is probably the most sensitive data in the organization.  I’m not saying that other data that may contain trade secrets is not equally important, but HR knows stuff about our employees that they really don’t want released.  While openness about jobs and salaries has seemed to increase with the younger generations, there is still a great deal of sensitivity around many issues, and certainly a large amount of data that must be protected from a compliance perspective (such as diversity information and ER claims).  While we have tried to segregate data in such a way that prevents unauthorized access into the database, security and access rights to the systems of record is only the tip of the iceberg when it comes to unraveling the solution to this problem.  Like an email, once a report is generated or an interface is created, the owner of data simply loses control and can’t really ever be sure where that data is going to land.

There really aren’t any good solutions at this time.  You can restrict data so that it does not land in a data warehouse, or prevent integration to other systems, but at some point, there will be a hardcopy report floating on a desk, waiting to be whisked off by the wrong person’s hands.  I’m not really an advocate of putting huge amounts of controls on data.  I think that you appoint a system of record, data owners, access rights, and do your best in a well managed data environment.  I am curious about what others are doing out there to prevent unauthorized or unplanned dissemination of sensitive data other than simple data governance and data management measures.  Is there anything out there that can handle this yet?

Understanding HR Data Governance

We bought my wife a new bike this weekend.  She is not usually a bike rider, and the last time I took her out riding with me, she swore never to go riding with me ever again.  I suppose that one does not necessarily realize this if one does not have sufficient self awareness or awareness of the surroundings when in college, but it’s been about 15 years since my last bike ride with her.  This time around, rather than trying to take her out on (what I guess were) fairly advanced mountain biking trails, we decided that we would buy her a bike for cruising around town – a bike to have fun on, but certainly not to go fast on.  Today was my first bike ride with her, and we decided to head out to San Francisco’s new Chinatown over on Clement street to buy some dim sum.  We then proceeded to the Golden Gate Park where we sat by Stowe Lake eating. 

I know that we’re all sick of talking about Data Governance, but the reality is that most of us still have not implemented it.  In fact, I’m going to go as far as to suggest that most of us don’t really know what it is, even though we execute forms of data governance in our everyday lives.

Data Governance basically consists of three things:

  1. How we make decisions about data,
  2. How we define the data,
  3. How we execute processes that involve data.

To start, I really needed to make a decision about riding a bike around the town with my wife.  While I might enjoy the fast and aggressive weekend rides, spending time with my wife in a way that we both would enjoy was paramount.  The next step was to define the type of bike we’d get her – in this case, not a race worthy mountain or road bike, but a street type of a bike.  Finally, we needed to head out and ride at a pace and in a style that would keep her interested, in this case, food and the park.

The HR data governance structure begins with an organizational structure that allows us to make good decisions.  Usually this is a committee or set of groups that escalates what the needs are and how to deal with them. 

We then need to define the data.  Thinking that as we reach across multiple HR systems in a variety of global countries and regions, that we can easily define data might be naive.  Each system has their own definition and countries have widely varying approaches to data as well.  Without a common understanding, it’s next to impossible to have a resultant set of data outputs and outcomes that is reliable.

Lastly, we need to reformulate all of our data processes in a way that is consistent with our data definitions and maintains our quality standards.  Data governance and the definitional process is a predecessor to any HR process, but without HR process, the purpose of data governance (data quality and access controls) is a promise that cannot be kept.

At the end of the day, HR data is an enabler, and we have all experienced HR data that is so messy that it no longer enables anything.  Data Governance is the solution to this problem, but it comes with multiple components, each of which must be implemented for an overall governance program to have any use.

Everything in its Place

I write this in the usual place – the airplane.  I’m in a window seat, so I’m only surrounded on three sides.  The guy in front of me has decided that all the stuff he does not want should not go in front of him, but that “out of sight, out of mind” is a good solution as he shoves the stuff under his seat at my legs.  I was about to start throwing stuff back at him over his head, but thought better of it.  The guy behind be decided the armrest was actually his footrest.  When his shoe was on my arm, he didn’t even have the sense to pull back a bit.  I had to push his foot off the thing.  The guy next to me is a good guy – he just has wide shoulders.  I can’t really blame anyone for that except his parents.  Worst thing yet, someone on this plane is flatulent.  So not only is my space being intruded upon on all three sides, my very airspace is also becoming a bit offensive.  (yes, I am the guy on the plane that will shout “No Farting!!” to everyone)

I was recently talking to someone about how Spain and Mexico uses 2 last names for employees.    I don’t know if you watch tennis, but the only example I can think of is Aranxa Sanchez-Vicario (I think that’s how you spell it).Apparently PeopleSoft only has a 30 character last name field.  I’m not sure why they have not expanded this yet, but honestly, 30 should be enough, especially if you bought the right country packs.  Anyway, I was recently talking to someone about Mexican last names and the thinking was that they would put the mother’s last name in the middle name field, and the father’s last name in the last name field.

Fundamentally, I don’t usually have a problem with workarounds.  But as you know, I’m a data governance guy and I have a huge problem dumping a last name somewhere other than the last name field and taking the middle name field and dropping in a name that is not the middle name.  Not only are there just issues about using fields for the wrong purpose, but there are practical issues around interfaces and analytics.  How do you search if you don’t have a single last name field?  If you have an interface, do you write it with specific instructions to look for Spaniards and Mexicans and re-arrange the names?

There is a point to order and a point to “everything in its place.”  Especially in terms of systems and data governance order is of utmost importance.  You start playing with order, and you wind up with what we call (in technical terms) “crappy data on a massive scale.”  You see, you mess with the wrong workarounds now, and there’s a pretty high probability that you’re going to pay for it later.  Later might be a couple of years.    Before you do the wrong workaround, do the right resource, figure out the implications, and then do it the right way anyway.