Employee fitness tracking – Is there a privacy issue?

I was just doing some research on wearables and the fitness market for IoT devices and came across this employee fitness tracking service offered by Endomondo. Endomondo are one of the original fitness tracking apps and, based on my use of it over several years, it works well. Their employee fitness service basically allows employers to offer their employees access to the premium level. According to the company,

Employee Fitness lets you create a program which motivates your staff to get fit and feel engaged with your company whenever they work out.

This seems like a good way to encourage exercise amongst team members and perhaps improve staff fitness and morale. However, it was this bit which caught my eye,

Your company page will come equipped with an extensive statistics module which enables you to view how active your team is, as well as to analyze their engagement levels on specific challenges.

The screen grab from their website illustrates how this might work in practice.

endomondo employee tracking

It is not clear whether the employer has access to individual employee-level data about exercise levels but, as the employees will each have individual accounts within the corporate account, that data will be captured.

Some of the privacy aspects of this are obvious. Do we want to reveal to our bosses and colleagues how much we exercise? Could our exercise regimes be tied into our remuneration? Is there peer pressure to join the scheme? Do we want this aspect of our personal life being tied into our work environment?

As the Internet of Things reaches further into our work and personal lives over the coming years these, and similar, questions will come to the fore.





Machine learning – McKinsey Insight

machine learningInteresting piece on the McKinsey Insights site on machine learning. It’s aimed at executives to give them an overview of some key issues and trends and worth a read if you want some examples of how companies and researchers are innovating in this sector.

While useful, it does fall into the trap of overplaying the significance of machine learning in the short term:

Now is the time to grapple with these issues, because the competitive significance of business models turbocharged by machine learning is poised to surge. Indeed, management author Ram Charan suggests that “any organization that is not a math house now or is unable to become one soon is already a legacy company.

Steady on. People were saying similar things about the internet and retailing almost 20 years ago and, despite the rise of online retailing, we still have high streets and old-fashioned shops. Of course, the high street is changing and more money is being spent online but in the UK it still only accounts for 15% of retail spending. But I suppose you get less attention by claiming that things are going to change slowly over the next couple of decades rather than “you’re all going to go out of business next week”.

This caught my eye:

Last fall, they tested the ability of three algorithms developed by external vendors and one built internally to forecast, solely by examining scanned résumés, which of more than 10,000 potential recruits the firm would have accepted. The predictions strongly correlated with the real-world results.

I can see that if this software takes off there will be a whole new business in helping people write CVs which score well with these algorithms, a sort of SEO for job applicants. CV Algorithm Optimisation (CVAO) – wonder if that will catch on.