Big Data Fitness

A man with one watch knows what time it is, but a man with two watches is never quite sure. This old saying could be modernized to, that a person with one smart device knows the truth, but a person with two smart devices is never quite sure.

An example from my own life is measuring my daily steps in order to motivate me to be more fit. Currently I have two data streams coming in. One is managed by the app Google Fit and one is managed by the app S Health (from Samsung).

This morning a same time shot looked like this:

Google Fit:

google-fit

S Health:

s-health

So, how many steps did I take this morning? 2,047 or 2413?

The steps are presented on the same device. A smartphone. They are though measured on two different devices. Google Fit data are measured on the smartphone itself while S Health data are measured on a connected smartwatch. Therefore, I might not be wearing these devices in the exact same way. For example, I am the kind of Luddite that do not bring the phone to the loo.

With the rise of the Internet of Things (IoT) and the expected intensive use of the big data streams coming from all kinds of smart devices, we will face heaps of similar cases, where we have two or more sets of data telling the same story in a different way.

A key to utilize these data in the best fit way is to understand from what and where these data comes. Knowing that is achieved through modern Master Data Management (MDM).

At Product Data Lake we in all humbleness are supporting that by sharing data about the product models for smart devices and in the future by sharing data about each device as told in the post Adding Things to Product Data Lake.

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