The "Good News/Bad News" of Mobile Device Generated Analytics
Sandeep Raut's Mobile Analytics and impact on Digital Transformation is a succinct overview of mobile-generated digital analytics. What follows is a "good news/bad news" view of what he discusses.
The "good news" is that when mobile devices are effectively used as sources of key analytics (Raut suggests marketing and app performance as key data types), the small screen size and the reality of short user attention spans should cause application developers to focus on the task at hand and avoid unnecessary or distracting features, functions, and information that might confuse the user.
This is a lesson learned early on when standard desktop apps were moved to mobile devices. Developers realized that mobile device performance, form factor issues, and usage environments argued against attempting to duplicate desktop application features. As a result we now have purpose built mobile device interfaces that are much more focused and efficient in how they support user interaction. In many cases such design principles have fed back to simplify even desktop browser based applications.
The "bad news" is that, conversely, the need to focus mobile application development primarily on selective high priority specifics can lead to a lack of contextual information being gathered and available for analysis. For example, the mobile device might not be generating data to help understand the "why" and "how" associated with how the device is being used. As a result, other sources of contextual information might need to be combined with data supplied by the mobile device. This may complicate overall system design as well as how analytics will be processed.
The importance of context
There may be tasks where gathering strategic or contextual information will be relatively less important. This might be the case when Augmented Reality and Virtual Reality are applied via realtime displays to assist with and guide certain well defined tasks without distracting the user. In such situations the analytics being gathered may sufficiently reflect only the most important events that the mobile device user is working through, an example being certain repair or diagnostic applications that have been well documented and sequenced by experts.
Analytics: after-the-fact or real time?
On the other hand, a complex set of tasks or decisions that rely on the user's professional judgement to move through a complex sequence of scenarios with multiple decision points and options is also an opportunity to overload the mobile device user while at the same time posing challenges to the interactivity possible with mobile devices.
In such instances it will be useful, as early as possible in the design process, to determine how the analytics being gathered will be used. For example, will the data thus gathered only be used after the fact to analyze the events or decisions the mobile device is supporting? Or will the analytics also be used as a basis for realtime feedback to the mobile device user?
These are two very different data collection and analysis scenarios and have profoundly different design and testing implcations.
Copyright (c) 2017 by Dennis D. McDonald