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Learning from Data: Why These Three Styles Matter

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 Business Intelligence 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 organisation 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 this year, 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. 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, organisations 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.

8 Comments

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  1. We’ll written , well explained and just the right examples to prove the concept.
    Actually industries are shifting to these standards of learning via human senses to make products better both for fit and impaired personnel.

  2. As discussed in the article, it is quite true that people have different approaches towards things. Few people are inclined to learn with the help of audio and interact with the machines vocally, while few others have a strong preference for the kinaesthetic reception and some people are keen on visual learning and interaction. So the challenge is to adapt and cater to the preferences of the customers based on their need rather than enforcing a single mode of interaction to all the customers alike. Diversifying and letting the user chose his mode of engagement would help the organisation spread its reach and improve the trust of the customer and thus the companies have to adapt to learn from data in all three forms.

  3. Well said by Mr. James.

    Institutional learning is the most important part of emerging economy.

    Companies can make some test or system by which they can differentiate individuals based upon their learning styles.
    Then they can be trained by particular styles, for effective learning.

  4. The business benefits are always derived Business Intelligence. People are getting business decision relevant data quickly. People are able to learn and gain insights about their organisation and its situation, which ultimately helps in improving decision making. The author had expressed his support that the real world, benefits of BI are largely derived from assisting institutional learning. In turn, institutional learning is built on individual learning. The visual representation of data is dominant this year, 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 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 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.

  5. These 3 styles matters because this learning style uses the three main sensory receivers: Visual, Auditory, and Kinesthetic (movement) to determine the dominant learning style. It is sometimes known as VAKT (Visual, Auditory, Kinesthetic, & Tactile). It is based on modalities—channels by which human expression can take place and is composed of a combination of perception and memory.

  6. So until now there were ways which could be used to visualize the data for those people who doesn’t understand the pattern in data, doesn’t know about what is actually going behind these insights, obviously not everybody does not need to know , but as a organisation providing solution they should be able to make understand their clients about the patterns, insights of data either visually OR by these three styles of data.
    It is imperative for the clients to learn from the data and in future there will be a way where data iletrate people, people with disabilities should be able to learn from Data.

  7. The idea of using the “The Three Styles” together is really very appreciable.With the help of this data can be made accessible to people with different abilities,specially those who are physically challenged,and can help them acquire knowledge from the data and improve BI.But what if we employ BI in a much lucid form using “The Three Styles” to educate and empower the data illiterates and bring them into the population of data literates,this can help in building an intelligent community.

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