The unmistakable conviction of visual business intelligence

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Virtual reality

Regardless, VR’s continuum of the progression of BI from a visual perspective is readily demonstrable, especially for time-consuming data exploration. In much the same way that interactive visualizations could encompass greater varieties and amounts of data than traditional spreadsheets or reports could, viewers can traverse even more quantities and types of data with VR technologies. In that regard, the most widely used metaphor for VR’s applicability is gaming and the immersive experiences video game players undergo with certain role-playing products. Some vendors offer VR data visualization experiences in which organizations “typically can look at different views, either micro or macro; you choose your journey through there in a way in which there’s a larger construct but you follow your chosen path based on how the dynamics are unfolding around you,” explained Harry Blount, chairman and CEO of DISCERN.

The level of detail offered by such immersion within data and the multitude of factors related to it can enrich visual BI’s decision-making process in a way that exceeds that of other visualizations—particularly when organizations are including differential, relevant data and multiple people in the decision-making. Like video games, the gamification of BI enables organizations to seek certain goals in terms of budgetary constraints, revenue targets or portfolios, and to equip themselves with varying data-related capabilities to achieve their objectives. “The same principles apply when you think about business gaming,” Blount said. “You want to look at data at a point in time; you want to look at data over time. You want to see the interplay of that data over time. You want to see things that are related to what you’re looking at.”

Visual data management

The true measure of the comprehensive effect of visual BI is probably best found on the back end with the construction of models and integration of data required for analytics. The same utility gained from visualizing analytics on the front end is applicable to those efforts on the back; the increasing usage of visualizations for the former purpose has resulted in expanding visual capabilities for the latter’s. In that respect, the growing reliance on visuals for data management has paralleled, if not outright purveyed, the self-service movement by making facets of data modeling and transformation more user-friendly with the presence of preset, configurable graphic representations to prepare data for analytics. Those capabilities are enhanced by the advent of machine learning and deep learning in such data preparation platforms, as well as by their inclusion in solutions that only a few years ago focused solely on data discovery courtesy of their visualizations.

According to Syncfusion VP Daniel Jebaraj, “In general when working with data, reporting or dashboards, we find that the way that you build the dashboard, whether you do it visually or with code, has a big impact not only on your productivity, but also in terms of opening up avenues on how you see the solution. With the more intense kind of code-based approach where you’re building custom solutions, you’re able to get more control over the system but it comes at a big price: It’s more expensive to build a solution, more expensive to test it, [and] more expensive to iterate on it.”

The evolution of BI

The evolution of business intelligence to a visual approach is noteworthy for two dominant reasons. On the one hand, it has buttressed the self-service movement and made analytics much more accessible to business users bereft of the coding skills typically required to actuate analytics on their own. BI has become much more democratic through the enterprise because of the visual movement and the relative ease in which it is facilitated throughout a bevy of environments. Secondly, the gravitation to visual, self-service means of accessing analytics has proliferated throughout data management itself. Visuals are appearing in all facets of analytics from origination to the point of action and are facilitating similar user-friendly experiences that make data more valuable to the enterprise. According to Paxata’s Michele Goetz, the latter trend could possibly be even more influential because it’s “providing the visualization of data conditions so that you better understand and interpret what those conditions are, whether they’re good, they’re bad, they need to be fixed and how that’s going to work. But you can also use those visualizations to filter, to drill in, to start the steps for any sort of business logic you want to be able to provide so that the data interaction is a completely different experience than trying to write a function or script some code.”

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