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Why adopting people analytics is like Google Maps

By: Dirk Jonker

Maps on phone with purple overlay

The benefits of people analytics are well known, but many  struggle with adoption in their organizations. The evolution of Google Maps is one example of how a solution can go from ‘seems useful’ to everyone wanting to use it.

It doesn’t matter how slick, fancy or whiz-bang your technology is if no one uses it. Likewise, there’s no point having a Ferrari if you can’t take it for a spin. The same goes for people analytics: any solution you have needs to be used, it has to be useful, and it has to have impact. Over the years we have seen the people analytics community develop solutions and struggle with their adoption – and we have all learned lessons along the way.

I like to compare the way the technology has been adopted to Google Maps. In the early days, the first iteration was really basic – it was essentially a digitized version of a paper map. When I first saw it, I thought it was useful, and it seemed easy. You could search for things like florists, pizza, hotels, but I – like many people at that time – didn’t use it as there wasn’t a compelling reason to do so.

That all changed when I went to San Francisco. I was visiting the city, and so was a friend. He wanted to eat ribs, but we didn’t know where to go – so I asked Google Maps. Not only did I get an answer, I got the location, phone number and directions. I had an opportunity to use it, and I haven’t looked back. And these days, everybody is using Google Maps.

The adoption of people analytics follows a similar trajectory, and there is a typical flow of how individuals have come to use the technology. We have done a great deal of research into the adoption of people analytics and have a powerful model that conceptualizes this.

The constructs that drive the adoption of people analytics are: ‘seems useful’, ‘seems easy’, ‘intention to use’, ‘opportunity to use’, and ‘organizational readiness’ (i.e. everyone is using it.)

This model of the five stages of the perception of technology helps organizations understand why even the best solutions don’t always gain traction.

In the first stage of ‘seems useful’, the technology is typically perceived to be insightful and can help an individual do their job better. It is crucial that the solution is useful. Also, the individual needs to trust the quality of the output, and they need to see the benefits of using people analytics.

If users also perceive the technology to be easy – in addition to being useful – then they are far more likely to intend to use it. Then – like me looking for ribs – an opportunity will come along that cements the usage, such as a business leader asking why employees are leaving the organization.

In terms of the intention to use, there are a number of drivers that can impact it. We have found that usage needs to be optional; if using the tool is mandatory, people are less likely to use it. People don’t need to be told to use people analytics; they need to be inspired to use it.

Our model can be used to analyze adoption in an organization, which we suggest should be tailored according to user groups. For example, if individuals score highly with ‘seems useful’ and ‘seems easy’ but don’t intend to use it, they should be grouped together so their needs can be addressed. There can be numerous reasons why adoption isn’t picking up, such as change fatigue among HR teams who are unwilling to embark on yet another initiative.

We also found that the readiness to adopt depends on the maturity level – in terms of their understanding of people analytics – and not their role within an organization. Also, users go at different speeds: some people just want to know where the buttons are and be left to get on with it. The millennial generation, for example, are used to this kind of tooling and they just need a trigger – and an opportunity in their working lives – to use it. And when more opportunities come, usage will spread and eventually everybody will be using it. Just like Google Maps.

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