“App,” a word previously only recognized by individuals with a computer degree, became universally known after Apple released the App Store for its iPhone mobile devices in 2008. Since then there have been over 1 million apps created with more than 60 billion downloads. Google soon caught on to this wave and launched its own app store, as did Microsoft. Apple set forth a trend defining how people interact with software on their mobile devices.
Analytics software on a mobile device should follow this same paradigm. If you want to analyze emergency wait time usage, readmission rates, or clinical coding, the phrase “there’s an app for that” should come to mind. The user experience should be comparable, if not superior, to that provided by other popular apps like Angry Birds, or Google Maps.
In a study completed by Flurry which analyzed time spent on mobile devices in the US, the firm concluded that consumers spent approximately 2 hours 42 minutes each day in 2014. The time spent was broken down into 2 categories: Mobile-Web and Apps. Unsurprisingly, US consumers spent 86% of their time interacting with their mobile device via Apps and only 14% via the mobile web browser.
Based on this evidence, there are several key components to making a successful analytics application on a mobile device:
- Native gestures supported by the device such as swipe, pull down, pinch and expand should be utilized as much as possible when interacting with the visualizations
- The user interface should refactor itself automatically and take advantage of different screen sizes to offer the best user experience (tablet vs. smartphone vs. watch)
- Interactivity with the visualizations and data should be instantaneous. For example, filtering on a field such as year or region should have almost no delay
- To accommodate slow or spotty network connections, the data should be cached on the device as much as possible or completely provided in an offline mode and synchronized
- The app should utilize and integrate with an organization’s mobile device management (MDM) infrastructure
- Security features such as local data encryption on the device, expiration of content, application pin and others should be provided and made configurable to the organization’s standards
Analytics vendors who follow these principles will see much better user adoption rates than those who try to provide a “one-size-fits-all offering” (defined as a single solution for desktops, tablets, and phones typically a web browser interface). The latter option is undoubtedly a more attractive choice for the analytics vendor given the ease in reusability, lower development costs and less maintenance. However, such an offering in the long run will suffer a much higher cost – lower user adoption.