The Difference Between Impact And Utility In Mobile Analytics Design

looking-at-notebook.jpg

When designing for mobile analytics solutions, two variables are always in play. I refer to them as “utility” (not to be confused with utility in economics) and “impact.” At the micro level, they influence directly how we develop our mobile assets (reports, dashboards) in order to effectively deliver actionable insight through the mobile user interface and experience. At the macro level, they influence how we design and execute our mobile analytics strategy.

Utility is about efficiency

Mobile analytics is about faster, better-informed decision making through the use of mobile platforms. In this context, the utility value represents the degree at which we achieve maximum productivity with minimum waste to deliver insight through our design. However, this definition goes beyond just the design of the report user interface and includes all aspects of the mobile analytics design (user access, support, communication, and so on).

Let’s take a look at an example. The utility value often drives how we design differently for different audiences and/or requirements. For example, a sales top-deals report that shows key accounts may differ significantly in design from a sales dashboard that shows key metrics. This difference may exist even when the audience of both reports is the same—account executives.

The design of the former report puts the emphasis on highlighting top deals that require immediate follow-up. A straightforward tabular listing with the use of simple formatting options (such as use of colors) of specific fields (such as deal size or close date), may produce the maximum utility value.

On the other hand, the objective with the dashboard may be to deliver key metrics with less detail and with greater use of data visualization methods (charts, map). This requires additional techniques to enhance the interface for delivering summary-level information.

Impact is about effectiveness

Impact is about smart and effective designs that can effectively deliver actionable insight to mobile data consumers. For example, we talk about visual impact when we consider how effective the chosen data visualizations are in data consumption. Do they accelerate the three steps to insight? Going beyond the ease and speed, impact also deals with depth and perspective.

Looking at an example of report design, we may consider the use of a line chart to highlight trends and provide insight into large, historical data sets. Similarly, an analysis with geographical data points may require a geomap. In both cases, the simple rendering of the underlying data sets alone will not provide much value.

The impact comes into play when we enrich the basic analysis. The steps include:

  • Remove any distractions (e.g. use proper scale)
  • Provide context (e.g. comparison to targets or averages)
  • Provide depth (e.g. highlight key findings)

Bottom line

In some cases, these two elements may be in conflict with each other as a result of technical limitations (missing functionality or features) or misalignment of design and requirements. Other times, they may perfectly complement each other.

Understanding the differences between these two dynamics can greatly improve our mobile analytics execution.

Our mobile users may not fully appreciate or care about how involved and challenging the design of mobile analytics assets can be. But when it comes to mobile analytics execution, we can’t be better-informed unless we accelerate the three steps to insight.

Stay tuned for my next blog in the Mobile Analytics Design series.

You may also like the Mobile BI Strategy series on IT Peer Network.

Connect with me on Twitter @KaanTurnali, LinkedIn and here on the IT Peer Network.

A version of this post was originally published on turnali.com and also appeared on the SAP Analytics Blog.