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The unmistakable conviction of visual business intelligence

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Visual business intelligence represents the summation of BI’s time-honored journey from the backrooms of IT departments to the front offices of business analysts and C level executives alike.

It seamlessly merges the self-service movement’s empowerment of the business via user-friendly technology with the striking data visualizations servicing everything from data preparation to analytics results.

Such visualizations, in conjunction with in-memory technologies and natural language processing (NLP), significantly contributed to the climatic shift of the analytics landscape from data discovery to the era of data exploration in which users parse through datasets at will with point-and-click manipulations of images.

Moreover, interactive visualizations have rendered visual business intelligence capable of achieving that rarest of feats, combining previously distinct realms of data management—data preparation, data modeling, data discovery, analytics, data publishing and data visualizations—into what has frequently become a single platform designed for the business user.

In the process, such tools have made those data management hallmarks and others more intuitive, celeritous and democratic throughout the enterprise today.

Visual, interactive exploratory analytics

Visual business intelligence is based on the interactive visualization technology defined by Gartner Glossary as that which “enables the exploration of data via the manipulation of chart images, with the color, brightness, size, shape and motion of visual objects representing aspects of the dataset being analyzed … These tools enable users to analyze the data by interacting with a visual representation of it.” The virtue of those visualizations lies in their ability to not only provide the user with a comfortable format through which to discover and analyze data for exploratory purposes, but also to furnish the means of publishing the results of analytics with a visual conviction surpassing the mere regurgitation of statistical facts or static graphics. The numerous forms of interactive visualizations include:

  • interactive reports like traditional BI reporting except the former include a mutability of data types and expressiveness well beyond the inert nature of the latter.
  • interactive dashboards that display different types of information and are innately valuable in their ability to express greater quantities and varieties of information than traditional reports could. A Forrester posting states: “Even with the smallest readable font, single-line spacing and no grid, you can’t fit more than a few hundred numbers onto a screen. However, data visualization techniques allow you to fit tens of thousands of data points—a difference of several orders of magnitude—into a single figure that fits on a screen.”
  • infographics well-suited for the external publication of data, such as on a company website or a third-party platform.
  • data presentations that are ideal for potential customer meetings or speaking engagements at conferences.
  • virtual reality technologies that create a three-dimensional experience in which users are immersed within data and explore them in a quasi-physical fashion.
  • augmented reality (AR) technologies that deliver supplementary data about the business world that enhances understanding of data-centric issues.

The initial proliferation of interactive visualizations serviced by vendors such as Tibco, Tableau and Qlik not only made traditional dependencies on IT for BI archaic, but also contributed to the transitioning of data discovery to data exploration while solidifying the notion of visual BI as the most self-serviceable manifestation of business intelligence.

Beyond images: NLP

The transformative effect of data visualizations on the analytics landscape is beyond dispute. What is much more controversial, however, is the outcome of its impact. Opponents of visualization mechanisms claim they create an inordinate reliance on “pretty pictures,” which either distracts or, worse, misleads organizations from focusing on the requisite action begat from analytics insight. Those arguments are currently addressed by visual BI technologies in two ways: with the increasing prevalence of data-driven narratives and the influx of NLP capabilities attending solutions. The latter are widely notable for their artificial intelligence capabilities—cognitive platforms such as IBM Watson present users with a host of options (in natural language) regarding courses of action, likely results of such actions and hierarchized outcomes of analytics for best case, secondary and tertiary scenarios.

Visual BI vendors are enjoining their solutions with NLP capabilities largely to create the sort of narratives that help address points of departure found in visualizations. Sophisticated entities in that space enable users to both ask and answer questions via natural language. As Garter recently indicated, “The language understands context—so it’s more intuitive than straight key word search. For example, a user can ask about the ‘largest earthquakes in California.’ A slider will auto appear to let the user refine magnitude. Or in the screenshot, ‘houses near Ballard’—and ‘near’ is a slider for distance.” NLP capabilities for data visualizations also provide succinct summaries—something like a photo caption—for graphics as well. The similarities to cognitive computing are evidenced by the fact that in most instances summaries of visualizations are both automated and involve natural language business users readily understand.


The growing necessity of synthesizing data visualizations with narratives has given rise to the overall credence of data storytelling. One of the fundamental principles of this niche of data-driven activity is that pictures alone do not convince viewers; the overall context and significance of analytics must be explained. Perhaps the most compelling aspect of data-focused narratives accompanying visualizations is the emotional element they include and its effect in the decision-making process. According to Forbes, the artful weaving of terse narratives regarding the significance of analytics for business problems aids statistical conviction with the sort of engagement, persuasiveness and memorability that is partly attributed to story’s inherent emotional appeal because “neuroscientists have confirmed decisions are often based on emotion, not logic.” The combination of narrative-driven explanations and interactive visualizations can compel audiences by taking a multisensory approach that does not solely rely on visual imagery. Whether utilizing automated, natural language summaries or distinct storytelling techniques to explicate the results of analytics, each method reflects the emphasis on analytic presentation attributed to visual BI and its means of abetting overall conviction in data.

Augmented reality

BI’s progression from static to interactive reporting and from fledgling data discovery tools to full-fledged analytics exploratory ones is set to continue with augmented reality and virtual reality (VR). It’s too early to determine exactly which technology will dominate in that space; it will likely vary according to use case and organizational priorities. The immersive experience of virtual reality may be better suited for analytic exploration because of its three-dimensional nature and unequaled means of showing viewers data and their relationships. Conversely, that technology may be too exorbitant for the pragmatic decision-making BI is intended to facilitate, in which case augmented reality’s supplemental data about the concrete business world might prove more effective. There are still certain three-dimensional characteristics of the AR experience that are less immersive than those of VR, yet may still become influential to the presentation of analytics results.

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