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Data to Build a Dream on

Business journals appear a bit sleep-obsessed at times. Of all health aspects, sleep is the one written about most. That’s because sleep deprivation, as it relates to cognitive function and work, has been the focus of reliable research over the last decade, making sleep ready fodder for business writers.

These articles and blogs make compelling arguments, leaving the reader with a few snooze-related action items, like a Forbes blog from contributor Christine Comaford, which appeared last November. The general sleep commandment seems simple: Do more if it.

I have trouble imagining anyone who has anything against more sleep. However, whether implementing the suggested behavior in your life will actually have a bearing on your productivity isn’t clear. Sleep eight hours, exercise three days a week, mindfully meditate for 20 minutes—all these mandates are based on populations, not you. Who’s to say you need all eight hours of sleep? Perhaps you need only six. (If so, you’re part of a rise-and-shine minority who function fine on six or less.)

Health studies give us meaningful guidance on where to start changing our routines, but to know if that change actually works for the unique individual you are, that requires tracking—data collection of home-life and work productivity.

As yesterday’s implements of the domicile—the phones, televisions, and appliances that once stood lonely on their own—come online and become interconnected, there will be a great deal of data we can gather about ourselves away from work.

You may already track important health-related behaviors—such as sleep, exercise, and diet—but do you track the work metric? Relating personal activity to a measurement of productivity could give you insight into how home-life affects work-life. Many companies acknowledge health as a predictor of productivity.

As each of us amasses our own individual big data, we need to consider how that information could be used to analyze behaviors at home and predict productivity. Multiple variables measured over time won’t be used just to measure a population, it will be used to measure you.

For that to happen, personal data would need to be available to some mechanism that could consume it and relate it to work performance. That mechanism is possible and will probably be in the form of an app. Just look at Microsoft’s vision for enterprise apps—dual use for work and personal life.

Since the measurement of one’s performance is subject to the nature of one’s industry, these productivity measurement apps will probably be built by line-of-business developers for private companies.

The challenge for those developers will be working with the company’s business side to quantitatively define productivity and collect the appropriate data. Some systems, such as, already collect that information by tracking outgoing sales emails, sales transactions, and an individual’s overall sales for a given month.

Building an analytical system that can consume both business-side data and the personal data from various third-party apps will also be a challenge. You could have employees who use one app to measure sleep while another app measures the number of miles they ran that month. The business productivity app would need to consume both data sets.

And that, comparatively, is the easy part. The highest hurdle will be convincing employees that volunteering personal data to be crunched by the company app will be secure—no one in the company will actually see the personal data—and that it will be meaningful to their own success, helping them calibrate home-life with work-life, and vice versa.

For more on what Syncfusion can do for your big data collection and predictive analytics projects, keep an eye out in the coming weeks for two suites of tools that will make working with Hadoop and HDInsight easier for Windows developers.


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