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  • 執筆者の写真David Creelman

Escape from Big Data


You probably feel under some pressure to do something with “Big Data”. What you should actually do is probably far less clear. Let me help.

Q. What Big Data does HR have now?

Not much, not in the sense of Facebook which gets billions of pieces of data a day. The closest we get to Big Data is probably the time keeping data we have for hourly workers. There is a lot of data in the HRIS, but we don’t need to invoke a new category (i.e. Big Data) to describe that; it’s just a lot of data.

Takeaway: you should think about what to do with ‘lots of data’, but that’s just an extension of what HR has always been doing, grappling with the new concept of Big Data need not be a priority for most HR groups.

Q. Will HR have Big Data in the near future?

Certainly the amount of data will grow significantly, and there already is potentially interesting data we don’t normally analyse such as every email every employee has ever sent or received. If your HR department is big enough to have a kind of internal R&D function, then you might have reason to investigate this data.

However, the takeaway for most of us is to wait until vendors have reasonably mature products that allow us to get some value from this potential data resource.

Q. How to we discover the insights hidden in, if not Big Data, “lots of data”?

This is a bad question. Yes, there are some circumstances where an R&D team might do ‘data mining’: digging around to see if something is there. However, there are no guarantees that there is a useful insight in some pile of data. Let’s say that again since it is so important and so rarely said: There is an unchallenged assumption that if we have a lot of data then there must be some valuable insights there. This is no more true than believing that if you have a large plot of land there is bound to be treasure buried somewhere within.

Takeaway: Please do not hire a team with a backhoe to dig up your backyard on the assumption that ‘there must be treasure in there somewhere.’

Q. If we are not looking for hidden insights, what should we do with “lots of data”?

This is almost the right question. But let me stay controversial by saying you should not necessarily do anything.

Q. What question should we be asking?

The question we should be asking is “What is the best course of action to deal with this issue?” In other words, we should always be starting with a specific business issue. There should be a question to be answered or a decision to be made. Data, and other forms of evidence, will help us choose the best course of action. We should make an effort to look at that pile of “lots of data” to see if it will help us make a more informed judgement on what to do.

Q. But surely HR already is looking at data to make decisions; what’s new?

Yes, HR does use data, but not nearly as often as it should and often it falls far short of the sophistication which is now possible. You want to, as a matter of standard procedure, go to the people who are good with numbers and say, “Forget the Big Data hype, forget the ‘finding insights from data’ storyline, I just want to know how we can more identify workers who are likely to cause accidents” (or whatever the business issue is).

Takeaway: The average HR person is at the centre of this, they have identified an important business issue which is driven by human factors. They then ask analytics savvy folks to unleash their special data analysis skills to help inform their judgement. And you should do this regularly, it should feel odd not to have data.

Q. So as long as I know how to use the analytics experts, they can add value?

Yes, absolutely, and as we get better data and better analytics tools their value will continue to increase. However, they are not all you need.

Q. Okay, I’ll ask the expected question: “What else do I need?”

There is a misconception, common among analytics professionals, that the answers lie in the nicely controlled laboratory of a data warehouse. This is only one of the sources of evidence you need to make an informed decision. You should also be looking at academic research, interviews with experts, focus group with stakeholders, experiments, one-off surveys and so on.

Q. Can you summarize all this with some to do’s?

1. Instead of building an analytics team, build a decision support team that includes analytics.

2. Ensure the decision support function reports into the CHRO. This is a part of HR strategy, not just a laboratory staffed by data scientists or a backroom HRIS reporting function.

3. Provide training for your whole HR team on how to use data to make better decisions. They need to appreciate what advanced analytics can do, because sometimes they’ll need to call on it, but the only new math skill they need is some practice in estimation. Focus the training on how to be more sophisticated in using evidence to make decisions.


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