Ok, I must really say that I start to get afraid of this hunt for “Big Data” in HR. Apparently on the blogosphere and in the HR domain there is nothing else to talk about. Up to the point that somebody labels this as a A Revolution That Will Transform How We Live, Work and Think.
Don’t get me wrong, a lot around this is inevitable. We are producing more and more data everyday. Somebody should be able to do something with it. What I do question is this hunt for a miracle recipe that is lying within this huge amount of data, sort of resembling the famous needle in the haystack. And apparently there’s somebody that holds the secret recipe (in the form of an IT system) to find it.
Sorry, but I don’t buy this. First of all because the issue is to define what we are looking for, and not explore the data just for the sake of it. As any student of statistics can demonstrate, give me enough data and I can demonstrate practically anything. Big Data cannot (and will never) replace thinking.
A second point is which data are we talking about. Since the beginning of the “dot economy” there’s a huge wave around data. But what are the information that we are trying to get? We have been able to already explode a huge financial bubble thanks to too much data (that was completely meaningless). This just for the people that forgot how many Dot.Coms got public basing their business plans on “clicks”.
The third point is what consequences will our analysis have. What are we going to use these data for. What I have been reading around Big Data for HR is the possibility to analyze and collect data related to employee activities, and that can help understand future performance patterns.
Of course collecting this level and type of data about employee activity will inevitably collide with employee notions about privacy first, and then once most if not all employees accede to this nature of data collection, (perhaps under threat as a condition of employment), to concerns about the ‘fair’ or proper interpretation of the data. What employee actions and activities are ‘good’ or ‘beneficial’ to overall performance of the organization as opposed to the individual’s own performance will also be a bone of contention – it really is a big data version of the classic ‘results vs. how those results are obtained’ conundrum.
This issue is really relevant, as the question remains: simply because we can collect the data, should we do it? Does it really make sense to track employees walking around the office? What can we grasp from the frequency of their toilet breaks?
What scares me the most is the parallel of Big Data as the New Oil. Apparently this is something that is particularly hitting many marketing departments, because every 15 minutes somebody around the world is using this metaphor. But tho my opinion this parallel is only useful to identify a key issue with this big revolution. Aren’t we just assisting to a new form of pollution?
Perhaps the “data as oil” idea can foster some much-needed criticality. Our experience with oil has been fraught; fortunes made have been balanced with dwindling resources, bloody mercenary conflicts, and a terrifying climate crisis. If we are indeed making the first steps into economic terrain that will be as transformative (and possibly as risky) as that of the petroleum industry, foresight will be key. We have already seen “data spills” happen (when large amounts of personal data are inadvertently leaked). Will it be much longer until we see dangerous data drilling practices? Or until we start to see long term effects from “data pollution”?
This especially in the context of an HR Job family that is, in most cases, not even capable of handling small and medium data. The bulk of companies around the world still have only basic payroll systems, what are we really talking about?
The issue is that everybody would like to find the secret recipe to avoid making sense of the numbers, and get down to the real meaning behind them. The problem is that there is no easy answer to this. The process is not new: how much information you can extract from your data? How much knowledge you can create from this?
At the end of the day your data is worth only the Story you can tell from it.