Resource: whitepaper

Analytics and Big Data Innovation in Enterprise Software 

big data analytics

Simply having data, reports, and analytics to show users is not enough. Data is only useful if it is presented in the right way to the right user in the right context. Here are four rising usability trends to tackle the Big Data challenge.

Today’s software product managers and product executives face a major inescapable trend that affects their customers on a daily basis. The amount of digital information for businesses is exploding. In fact, IDC estimates that the total amount of digital information in the world is growing at 60% annually— essentially increasing ten-fold every five years. It won’t slow down any time soon, thanks to the growth in mobile devices like smartphones and tablets, and social media like Twitter, Facebook, and many others, some of which haven’t even been invented yet.

This unprecedented growth in data volume, velocity, and variety has created the Big Data challenge that software vendors must face, whether their product is an established Business Intelligence (BI) system or simply a solution that manages users’ key data—a healthcare information system, a supply chain management solution, or even a social media monitoring tool.

But simply having data, reports, and analytics to show users does not automatically translate to better products. Data is only useful if it is presented in the right way to the right user in the right context. It is the ultimate usability challenge.

As Big Data goes mainstream, product managers must capitalize on four rising trends or risk being left behind by the companies that do. They are:

  1. Ease of Use
  2. Verticalization
  3. Mobile Business Intelligence (BI)
  4. Visual Discovery

Visual Discovery

Most solutions offer visual analytics in the form of dashboards, allowing the user to monitor specific metrics and KPIs and take action on them. But visual discovery is about exploring data without a pre-determined goal, digging into different views—heat maps, spark lines, trellis plots—and visually uncovering insights.

Visual discovery is the holy grail of self-service. Users of all skill levels and across different industries explore views of data and spot patterns, from a government worker digging for ways of optimizing a grant program to a sales manager exploring geographic sales history using a virtual map.

Interactive Dynamics

There are important criteria for evaluating a visual discovery solution. Are advanced visualizations such as trellis plots available? Can multiple data sources be displayed at once and joined? Can the user perform tabular manipulations and aggregations? But the most important criterion is simply ease of use.

Traditionally, the field of data visualization has focused mainly on the creation of charts and graphs that were easy to interpret. Stephen Few is a thought leader in this area, educating the industry in the best practices of table and graph design and dashboard design.

But in visual discovery, these are table stakes. The new challenge is guising users through the interactive dynamics of a visual discovery session, in other words, the entire flow and experience from starting the initial views of data, to exploring the data, to annotating and recording the results. Jeffrey Heer from Stanford University and Ben Schneiderman from the University of Maryland published a paper, “Interactive Dynamics for Visual Analysis,” one of the few publications to date that begins to establish best practices around the overall workflow of a visual discovery session.

The paper essentially divides a visual discovery session into 3 parts: data and view specification, view manipulation, and process and provenance. Each is then further broken down into different usability considerations, from filtering and sorting to navigation and annotation.

The Three Parts of a Visual Discovery Session

Visual Discovery: Open for Design Innovation

Heer and Schneiderman’s paper is remarkable in that it encompasses a framework for the ease of use in visual analysis, but it also reveals many areas that are very new and open to design innovation.

For example, within the Data & View Specification area, the paper recognizes that most design solutions today are still complex to the point that novice users would need help from power users to get started. “Novel interfaces for visualization specification are still needed. A formal grammar that uses graphical marks (rectangles, lines, plotting symbols, etc.) as its basic primitives provides a conceptual model compatible with interactive design tools. New tools requiring little to no programming might place custom visualization design in the hands of a broader audience.”

In the View Manipulation section, the authors point out the potential to significantly improve ways that users could visually select data for deeper investigation. “Designing more expressive selection methods remains an active area of research. For example, researchers have proposed methods to map mouse gestures over a time-series visualization to select perceptually salient data regions such as peaks, valleys, and slopes or to query complex patterns of temporal variation. […] Of course, selection need not be limited to the mouse and keyboard: input modalities such as touch, gesture, and speech might enable new, effective forms of selection.”

Unlike Mobile BI where the fundamentals of mobile design are established and the challenge is simply finding usability professionals with the right experience, designing for visual discovery is wide open for design research and innovation.

Academic circles and industry thought-leaders have begun to establish frameworks and point out areas for further research. The industry is beginning to copy a handful of leaders like QlikTech, Tableau and TIBCO. But when it comes to the interactive dynamics of the entire visual discovery experience, particularly for non-expert users, the area is still wide open for new leaders to make a name for themselves through design innovation.

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  • Didier Thizy
    VP of Sales