How to ruthlessly use data like a boss without becoming inhuman

How to ruthlessly use data like a boss without becoming inhuman

How to ruthlessly use data like a boss without becoming inhuman

Through the power of predictive analytics, I can tell you when your employees are looking for new jobs, based on such factors as the timing of their sick leave requests, their word choice in company memos, and the number of emails that they send and to whom in your organization.

If I want to outsource that responsibility, I can tell you through the efforts of any one of a number of third-party vendors what the likelihood of your employees leaving you is, simply by examining the employees’ behaviors on social media sites, such as Facebook and LinkedIn and aggregating that into a risk factor.

The use of such analytic examinations in human resource functions isn’t a widely established practice yet, but it’s already well-documented in identifying likely customer behaviors and responses. For example, in perhaps an unlikely place, consider amusement parks.

In research conducted by Pikkemaat and Schuckert, they identified key factors that determined customer behavior, including warning signs of customer behaviors that would lead to the failure of parks altogether.

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Predictive analytics are being used to seemingly trivial things, such as determining which items Amazon recommends for you to the challenge of predicting civil unrest in Latin America, which Virginia Tech’s EMBERS project has been doing since November 2012.

I’m not denying either the importance or the power of using predictive analytics to help you better understand your employees or your customers. Having data and utilizing it in a timely fashion to drive planning is the hallmark of a good business plan. You should be appropriately investing in these segments, but at the same time you’re doing so, you shouldn’t forget that behind each of these data points is a real human being.

Some of this is the ease which data can be amassed and quantified; quantitative research is fairly simple to conduct, assuming that your data points are clear from the beginning, and that you have enough of them, appropriately sampled, to make a generalizable conclusion about the population.

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Some of it is science; Dunbar’s number, a theory proposed by anthropologist Robin Dunbar, proposes that humans can hold space for approximately 150 close stable social relationships, although we can obviously tangentially know many more than that.

 



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