3 enterprise IoT lessons learned from triathlete training gear

As emerging technologies evolve they often find an initial niche in highly specialized scenarios, or in specific industry verticals, before expanding to wider areas of applicability.  Within these initial niches, the early adopters can be anything from digital enthusiasts to fashionistas, or they can be folks simply using the technology because it serves a specific need extremely well.

As evidenced by the recent Mobile World Congress in Barcelona, there’s an amazing amount of innovation and experimentation going on right now related to wearables and the Internet of Things (IoT). Gartner estimates that the installed base of the IoT will grow to 26 billion units by 2020, which excludes PCs, tablets and smartphones.

In terms of wearables, it will be interesting to see which technologies, brands and products — wearable fabrics, glasses and watches — really catch on in both the consumer and enterprise marketplaces, how these are integrated into a variety of IoT ecosystems, and what new, digitally transformed, business models and processes gain the most traction.

To get a sense of where things may be heading, at least in the “quantified self” arena (just one portion of the IoT), I thought it would be interesting to look at an already well-established, specialized wearable/IoT ecosystem to see what conclusions could be drawn for other ecosystems.  

As an amateur triathlete for the last several years, I have a wearable / IoT ecosystem in the form of my training gear. I have a wearable GPS watch that provides time, distance and speed/pace for the swim, bike and run, and three wireless sensors in the form of a heart monitor, a weight scale, and a cadence and power meter on the bike. All three of these sensors communicate their data back to the GPS watch, which provides a highly configurable display. The GPS watch and the heart monitor are from one manufacturer and the weight scale and power meter come from two other manufacturers. Together this custom ecosystem gives me valuable data about my training and overall progress.

Here’s a few items I’ve observed that may also apply to other wearable / IoT ecosystems:

It’s trial and error until you find the right ecosystem for your needs – My ecosystem took a while to build out as I tried different products and finally arrived at the best of breed solution I was looking for. The ecosystem was not “out of the box” and differs significantly from one person to the next.

Some wearables simply don’t work and end up in the drawer – Some of the first lap counters for swimming didn’t work too well until I found my latest all-in-one GPS watch which counts laps in the pool reliably (even stroke count, stroke type and efficiency) and is my single “wearable” for all three sports.

One device becomes the main interface and sensors are expected to look after themselves – My main interface is the GPS watch itself since I rarely spend the time to upload data to the laptop. I therefore get my results directly from the wearable and expect the sensors simply to operate and be reliable with minimal maintenance.

Display customization is part of the user experience – While the end results after the workout provide the data for analysis, the real-time display during the actual training session or the event is another key factor. The ability to personalize this display to show the right set of variables on screen really enhances the overall experience and usefulness of the device.

Form factor is important and highly specific to the activity – In athletics, device weight and form factor are important purchasing decisions (particularly for use during races) and requirements are highly specific to the activity and field of use.

Battery life must be fit to task – In terms of battery life, I’m fine with charging regularly, but expect the device to last comfortably for the duration of my training and events.

Standards are crucial for the best of breed ecosystem to be possible – Fortunately there’s a wireless standard called ANT+ in my ecosystem that allows all these devices to talk to one another.

Wearables can be as “sticky” as smartphones – When it has to do with your personal data (i.e. your personal data cloud), then the devices and sensors become very personal items and “very sticky” in terms of your loyalty to the brand.

Information overload is not an issue, but access to rich data is – While my GPS watch captures an incredible amount of information about all aspects of my training, I’m able to pick and choose what I wish to utilize. The main consideration is simply having the rich set of data on hand whenever needed.

Technology refresh is a way of life – Every couple of years (if not sooner) some breakthrough features come along that provide an incentive to upgrade the ecosystem. For my triathlon training, this was a new device that combined more features into one package so I could use a single device across three sports. Even now, there’s new devices coming out that are getting into predictive analytics in terms of estimating your race time based on likely endurance.

So what are the lessons learned for the enterprise? I think there are three main areas as follows:

1) The first area is that all the usual rules apply in terms of technology evaluation. Pay attention to form factor, battery life, durability, and standards to ensure your best of breed ecosystem meets your technical requirements. Security and privacy are vital considerations as well the minute any of your devices or sensors connect online. In addition, pilots and proof of concepts are equally important to arrive at the optimum solution (for example, if you’re thinking about applying wearables such as smart glasses to provide detailed guidance for complex manual tasks such as inspection, maintenance and repair).

2) The second area is that there’s a lot of upfront business model considerations in here as well. Think about where you wish to play in the Internet of Things ecosystem. If you want to get close to customers and increase your stickiness with your audience, it’s important to play a part in the display or analysis of the customer’s personal data cloud (or your partners’ data clouds) and be the application “arms-dealer” to help them interpret and apply their data to meet their personal needs and interests. If you want to empower the ecosystem with intelligent sensors, or with an underlying platform, it will be important to understand the breadth of application scenarios you can potentially enable and what kinds of data and services, at what levels of granularity, will be most pertinent.

3) Thirdly, the solution all comes together at the user experience level. Presenting the information to the end user in a real-time, highly personalized, highly customizable, even predictive manner, really seals the deal in terms of the newly digitized process.  It’s this digital customer experience value proposition that fuels the entire ecosystem. So whether, you’re a device manufacturer, a data provider, an analytics provider, a platform provider, or some other form of player in the ecosystem, the digital user experience will be vital to growth, differentiation, solution longevity, and overall customer satisfaction.