Learning from Data: Why These Three Styles Matter
For decision makers to get the greatest benefit from data, BI needs to better align with how people learn and remember that not everyone benefits equally from data visualizations.
- By James Richardson
- March 2, 2016
For as long as I've been involved with the field, the hard-headed school of "we need a cost benefit analysis" or "build me an ROI justification" has defined the business-benefit of BI as mainly its ability to "speed up and improve decision making."
With the rise of self-service BI, the first part of that aspiration has been successfully satisfied. People are getting decision-relevant data quickly. However, the second outcome -- improved decision making -- is a less certain result (and also much harder to model in an ROI calculation than is agility).
What is it that really makes the difference when it comes to decision-making outcomes? The answer is simple: learning is what makes the difference. Through exploring data and asking and answering the Socratic question why?, people are able to learn and gain insights about their organization and its situation, ultimately improving decision making. I'd argue that the real-world benefits of BI are largely derived from assisting institutional learning. This is often overlooked; I very rarely see the question, "How will this BI software promote learning?" asked in RFIs.
In turn, institutional learning is built on individual learning. As such, to get the most benefit from data for the most decision makers, BI needs to better align with how people learn much more completely than it has before. To do so, in the next few years BI will begin to support a fuller range of human learning styles. The visual representation of data is dominant in 2015, but not all people that need to use data are equally visually oriented. Humans use an individual mixture of sensory inputs to learn, often defined as three learning styles: auditory/reading, visual, or kinaesthetic.
In the near future, business intelligence will make use of information delivery media to engage all three learning styles. For example, for auditory learners, auto-generated narratives in written or spoken form will describe the shape of the data selected or the contents of a chart. Having a technology such as Amazon Echo "speak" these narratives may provide a compelling option for people who learn through hearing.
In the future, haptic feedback and 3D printing will likely play a role in creating tangible outputs for the kinaesthetic learners who assimilate information best when they can physically get their hands on something. However, it seems that it doesn't need to be that complex. Our own experience and research on multi-touch UIs points found that people retain and ascribe more importance to data if they touch it, even where all they're actually touching is a piece of cold glass (for more, see Touching is Trusting). This is why designing BI software products specifically for the touch screen experience is about more than just getting on mobile devices.
Finally, for the visually led learners, the options will grow both in terms of new visual forms and in the output devices visuals are rendered on. This could mean taking advantage of very large, ever-higher resolution displays to enable the rendering of massive data sets and perhaps practical VR experiences that allow analysts to work within and explore immersive data spaces.
Overall, more support for a range of learning types is critical if BI is to deliver as much value to decision makers as it can from the data-driven possibilities open to them. A caveat though: delivering information in more forms is only useful if people can make sense of the data -- that is, if they know how to read it, if they are data literate.
Perhaps just like with today's "code of conduct" training programs, organizations should be mandating training in data literacy. After all, data literate employees are a driver of competitive advantage. As data literacy becomes more prevalent, we'll see more demands for and of data, which can only result in reaching better, faster decisions with BI when it caters to a full range of learning styles.
James Richardson is business analytics strategist at Qlik. Prior to joining Qlik, James spent six years as a Gartner analyst covering business intelligence and analytics. During his tenure, as well as advising hundreds of organizations on BI topics, James was the lead author of the Magic Quadrant for BI Platforms report and chaired and keynoted Gartner’s European BI summit. Before Gartner, James spent 13 years at BI and performance management software vendor IMRS/Arbor/Hyperion in various roles. James was named one of the UK’s “Top 50 Data Leaders” by Information Age magazine in 2015. You can contact the author at firstname.lastname@example.org.