Jason Yeung

Jason Yeung

Senior Director, Analytics Center of Excellence at SAP | Passionate about enabling and helping customers on their analytics journey.

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This year, my New Year’s resolutions is to try to get in shape… again. According to Inc., exercising along with eating healthier and losing weight are the top three New Year’s resolutions every year.

My challenge is that over the years I’ve gone from being an avid runner to a car service, driving my children to hockey rinks and lacrosse fields on the weekends. As a result, my exercise routine has been non-existent for very long stretches of time. However, this year I’m hoping that things will be different.

As an analytics geek, I wanted to start with analytics to weed-out all of those built-in excuses that I always seem to use and learn from my past failures.

The Analytics Challenge with Fitness Apps

Today, there are a huge number of fitness trackers available that provide data on our personal well-being. We wear fitness trackers on our wrists, have them in our pockets, and they’re even built into our exercise equipment. While these tools serve as great motivators, they seem to fall short in 3 key analytical areas.

  1. Data in. Not data out.
    These tools do an excellent job collecting large amounts of data but fall short in analytics. These tools provide a great summary level view of fitness activity over time but have limited abilities to explore the data further.
  2. Lack of self-service.
    The “dashboards” are never quite the way I want them. We all use slightly different metrics and may want different ways to visualize our fitness activity. But unfortunately, these tools limit us to static dashboards that can’t be modified easily.
  3. Limited types of data.
    No one fitness tracker tells the whole story because they’re each used for specific purposes. Additionally, these “apps” are all isolated from one another with no unified way to bring data together, especially data from other sources, like weather, music, or schedule. This is important because when it comes to physical fitness, we tend to only see the data relationships and associations that we want to see.

Test Driving SAP Analytics Cloud

Each day, I work with individuals and organizations to build and develop innovative analytical solutions to help solve complex problems with trusted information. While there are many analytics tools on the market, SAP Analytics Cloud gives me the ability to dynamically visualize, predict, and plan.

And as fitness can be a complex problem, I think SAP Analytics Cloud is the perfect tool to help me come up with a plan for future success.

Data Source #1: My iPhone

The iPhone is a very reliable activity tracker. The Health App tracks activity, distance, steps, and flights climbed. Apple allows you to export this data and after importing it into SAP Analytics Cloud you can easily visualize your overall activity.

How active have I been? 

The following graphics summarize the activity data that my phone has been collecting over the past 2+ years.

By visualizing this data it’s easy to see that 2018 was a down year.  I’ve traveled less, climbed fewer flights of stairs, and decreased my overall step count.

When am I typically most active?

The following two graphics show my activity (over a 2.5 year period) by day of the week and then by the hour of the day. It looks like I’m the most active on the weekends and the least active on Mondays – which is not a surprise. What did surprise me is that my activity is the highest late at night and very early in the morning.

Data Source #2: My Running App

I (fairly) consistently use runkeeper to track my overall running activity.  Similar to the iPhone, it’s very easy to export runkeeper data for analytics.

Importing this data into SAP Analytics Cloud allows me to easily see my running distance, duration, and pace and then to break down both my total weekly distance and average distances.

What has my running been like this year compared to last year?

Here’s a view into the dashboard that I built to track my fitness activity.  Unfortunately, 2018 was overall a down year – not just with steps but also with running. I ran less, went on shorter runs, ran slower, and ran flatter routes.

How consistent has my running schedule been?

If I drill into the individual months, there’s a lot of Null values.  I had a big spike in the summer/fall of 2017 and I’ve been very inconsistent since then.

Can I get enough steps even if I don’t run?

The following graphic shows the combined numbers between my iPhone and Runkeeper. If you look at the individual days, there are plenty of days where I’ve gotten a significant amount of steps in without running.

However, if you aggregate the data at a monthly level to remove the outliers, you see a different trend. Here you can see that running is critical to get my steps up. In the months where I don’t have any significant runs, my overall steps are much lower.

Data Source #3: The Weather

How does weather impact overall activity?

In the following visuals, you see that there is a small correlation between activity and temperature (left) and a small correlation between activity and rain (right). That is, the colder it is, the less active I am. Also, the more it rains, the less active I am.

How does weather impact my running?

The following two visuals show the impact temperature has on running distance (left) and rain on running distance (right).

It seems like there isn’t much of a correlation between running and temperature. However, in the second chart you can clearly see that I don’t like to run in the rain.

What does all of this information mean?

While each one of these data sources is helpful and useful to understand, combining them together allows you to see hidden insights in your data.

For me, my key discoveries are the following:

  • Structured running activities have been my best form of exercise in the past.
  • Running alone is not enough. Walking with (or chasing) your kids on the weekend, walking your dog, or going on a hike with friends can be much more effective in upping your overall activity than running alone.
  • Get gear that makes exercise fun and comfortable in rain or shine is a necessity. Apparently, the weather has been my built-in excuse to not exercise for years.

As you can see, analytics is all around us. In addition to being a top priority across all business organizations, it should be a top priority for individuals to better manage their lives and health. Using data to gather and build new insights allows us to be more productive, make better and more informed decisions, and hopefully live healthier lives.

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