When bad data happen to good HR people!

20/08/2012 at 16:47 13 comments

You are an HR executive and you are sitting in your monthly strategy meeting with the top executives of your organization. You are about to present your monthly workforce data and updates on your KPI’s and strategic initiatives. This is the moment when the people who matters are really listening to you. You have the floor. But you dread this moment, because you know that they know that the data is bad. You hand out the status reports and you begin your presentation. And everybody in the room – including yourself – are thinking the same thing: “this report looks nice, but we don’t trust the data. It just doesn’t look and feel right”. And actually you know that the data isn’t right, but it is the best that you and your analytics department can find. It is not that the data is completely wrong – it is just not right. But you know that if you were to try to make the data right it would require so much work and resources. Resources you don’t have. So you continue with your presentation and hope that nobody asks. They usually don’t.

This is a problem for many in HR (as well as all other functions). You are using workforce data which doesn’t quite feel right but you use it because that is what is available and you or your analytics department have no alternative.

Bad HR analytics data

Bad data is a big problem and it affects every part of an organization, from sales to HR. Many studies have shown that bad data quality cost a lot for organizations. A Gartner survey revealed that in US:

  • 140 companies surveyed lost an average of $8.2M annually due to bad data
  • 30 companies surveyed estimated their losses at $20M
  • 6 companies surveyed estimated their losses to be more than $100M annually

Why does it cost so much? There are three reasons; bad data quality lead to bad decision, bad processes and ultimately bad data can lead to mistrust with your customers.

All organizations have bad data to some degree, but it seems that some have much more than others. There are four reasons behind why bad data happens:

  1. Lack of a coherent data strategy. Having a purely operational approach to your data is probably the biggest reason behind bad data. Data is suppose to support you in your strategic decision making and a lack of a coherent data strategy to support your organization’s strategy means that you approach becomes random and often meaningless.
  2. Assume that analytics software is the answer. HR analytics software is great, but it is simply just another data collection tool, albeit one with more potential than most. To get the most out of HR analytics you must go through a strategic data process and decide what data is of strategic importance to you and how they ideally look like.
  3. Garbage in, garbage out. This one is often overlooked although it should be clear to everyone. Your data is only as good as the component inputs.
  4. Lack of critical data sources. While the quality of the data is critical, what data sources you incorporate is equally important.

So how to avoid bad data? Thomas Redman, writes in his blog that “data creators must create data correctly, the first time, with full understanding of what that means to customers, those who use data they create. Data customers must communicate their data requirements to sources of data, and they must provide feedback when data are wrong. Virtually everyone recognizes they are at once data creators and data customers. There is, of course, a lot more to data quality management. But let’s not make this any more complicated than it needs to be.”

I agree. It is not that difficult, but once you are using bad data in your reporting, ROI’s and updates it is so difficult to change it for primarily psychological and political reasons. So 1) get it right first time and 2) when you observe bad data correct it immediately.

Ultimately, I believe that many executives are sitting with status reports, KPI’s and ROI calculations based upon bad data. And many know this to be true but it takes too much effort to correct it. The bad news is that you have to change it. There is simply no excuse to continue to use bad data. If you don’t have the resources to make them better at least stop using them. The good news is that good data makes a big difference. The quality of the decisions, processes and programs based on good data is worth so much more than it cost to find them – even if it means that you have to change many existing processes and disregard existing data sources. You (and your data) will be so much more trustworthy.

So what to do when bad data happen to good HR people? Fix it.

Entry filed under: Analytics, Measuring HR. Tags: , , , .

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