Predictive analytics in HR – barriers to deployment (Part 7 of 8)

by Jan 15, 2014Blog0 comments

Predictive analytics in HR – barriers to deployment

While predictive analytics can bring all sorts of benefits, it will not always be easy to deploy it effectively. But if you know the most likely barriers you can plan for them and improve the chances of success.


The introduction of any approach dependent on computing involves facing some obvious barriers. Your company needs to be able to identify and afford the right software, as well as any extra hardware needed to support it. This involves a significant investment of time and money, so having a good case for introducing predictive analytics is vital.

People with the skills to use and support the software are also a necessity. For predictive analytics this is not just a matter of IS technicians. You need someone with the statistical knowledge to put the right data into the calculations, to clean that data up beforehand, and to interpret the results in a way that makes them accessible to decision makers without losing their meaning. So you need someone who combines these skills with knowledge of how the software works, or two people with the appropriate skills and good communication between them.

Data integration

Having data is one thing, being able to combine it is another. Few companies are lucky enough to work off of a single integrated system. The information you have may be stored in different programs or formats. Bringing the data together is part of the challenge of using it – finding ways to integrate your systems or to transfer information from one to another.

You also need to be aware of complications that can arise in this. Exporting data from one program and importing it into another can lead to problems with corrupt data, and having systems in place to fix this, both at the point of transfer and on an ongoing basis, is important.

Access to data

In trying to understand the big picture that you work in, you will often be limited to data that is publicly available. If there is little information published on, for example, staff turnover in your industry, then this will limit what sort of predictions you can make, at least until you have gathered a larger data set.

When using predictive analytics to judge the suitability of potential recruits, the issue is one of data privacy. Of course you can use any information they provide on their application, but the Internet is full of information that could help you to make the right decision. You need to check where you stand, both legally and ethically, and where you draw the line in what available data you use.


Introducing any change involves facing barriers thrown up by human psychology. We’re an instinctively risk averse lot, and a combination of defensiveness and the sunk cost fallacy can lead many people to cling to what they already have rather than accept change.

This applies double when introducing predictive analytics. By introducing it you are saying that you have found a better way of thinking about data. This will be interpreted by many as an attack on their decision-making capacity, and the much romanticized ‘gut feel’ for a situation. They will find ways, whether overtly or covertly, to resist the change.

Prepare the groundwork before introducing the change. Listen to people’s concerns. Apply appropriate change management techniques, and you should be able to move past these barriers.

So predictive analysis is leading your company to a bright new future. But what does the future hold for predictive analytics?

Links or other articles in this series:

Article 1: Predictive analytics – looking to the future in recruitment
Article 2: Predictive analytics in HR – looking at the big picture
Article 3: Predictive analytics in HR – smarter recruitment
Article 4: Predictive analytics in HR – training and development
Article 5: Predictive analytics in HR – retention
Article 6: Predictive analytics in HR – getting it right
Article 7: Predictive analytics in HR – barriers to deployment
Article 8: Predictive analytics in HR – the future

Image credit: Kazmin / 123RF Stock Photo

Mark Lukens, MBA

Mark Lukens, MBA

Founding Partner at Capatus
Mark Lukens is a founding partner at Capatus and located in the New York office. He leads the Capatus’ Global Talent and Advisory practice. He is also an expert in the firm’s research and nonprofit practice. Lukens has more than 20 years of c-level executive and consulting experience delivering strategies and transformational programs to firms ranging from start-up to Fortune 50. He has worked with clients in Europe, North America, South America, and Asia. Lukens worked extensively in various product and service categories including health care, life sciences, government, nonprofit, technology, and professional services. He also advises clients in other industries including commercial and industrial, retail, logistics and transportation, media and more. Lukens serves on several Nonprofit Boards and is a professor at the State University of New York where he teaches in the School of Business and Economics with a focus on marketing, international management, entrepreneurship, HR, and organizational behavior to name a few. Lukens has a technical background as a MCSE and earned an MBA from Eastern University.
Mark Lukens, MBA


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