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Whether reaching the goals of a business or a personal fitness goal, understanding what the key influencers are will guide individuals towards the right direction in making the necessary changes to drive success.

Fitness tracking capabilities through wearables technology have surfaced in recent years allowing users to gain an understanding in their habits and routine. The opportunity to look at activity data and adjust an individual’s daily routine is fundamentally important in developing a healthy lifestyle. Along with the predictive capabilities of SAP Analytics Cloud, business users can make better decisions with their business and their health.

Some consumer-oriented fitness tracking devices now allow the user to export the data collected by these devices to allow users to analyze the data themselves. Using SAP Analytics Cloud, you will be able to surface patterns in your behavior to understand the key influencers that are affecting your lifestyle.

To start, have the desired fitness data in an excel file ready to import into SAC. Create a new model by selecting “Import a file from your computer”

In the data wrangling page, ensure the appropriate columns in which numerical values that mathematical functions work on are labelled as a Measure. In this case, activities-calories and activities-BMR will need to be switched to measures. When finished, click “Create Model”

After the model has been created, our model can be used by creating a new story and selecting “Import & Explore Data”.

Choose “Use Existing Data” and locate your model.

The analysis begins through the Smart Discovery feature to find the key drivers of a measure, for example, the activities-calories being the number of calories burned. To take an in depth look of the Smart Discovery capabilities, learn more here.

Select the measure and click “Run”.

Seeing the influencers of the calories burned, the number of steps taken is highly correlated to the measure chosen. As well, being lightly active is more influential than being very active. From this, there are ways to incorporate more rigorous physical activity into a day’s routine.

Looking at the Unexpected Values, there are various points where the predicted value was lower than expected. Selecting one of the points, I can see that on June 2, 2017, I was far more active than usual and this was due to a spontaneous hike I had been invited to. Seeing how I was able to burn 1,500 calories more than expected shows me that I should plan more hikes for the year.

Not only am I wanting to understand the activities I partake in, I would also like to see how I can change my diet. With the Simulation feature, I can better plan my diet according to the level of physical activity I am participating in. I know I will be involved in two hours being very active for the day, in which I can prepare my meals to achieve a reasonable caloric deficit.

After a few clicks within SAP Analytics Cloud, patterns have surfaced allowing me to create opportunities towards a healthier lifestyle. Seamless data analysis was done to easily discover ways in making effective, informed decisions.

 

— Many thanks to my colleague Benton Li for contributing to this blog post.

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