Glen Rabie is Co-Founder and CEO of Yellowfin, an Analytics and Business Intelligence company that helps businesses understand their data.
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More than 30 years ago, researcher Neil Fleming coined the acronym VARK to describe what he observed about learning, based on his years as an inspector in the New Zealand education system. He perceived that students learned best according to their own individual way of consuming information:
• Visual learners, those who take in facts in graphical and/or symbolical fashion.
• Auditory learners, those who listen carefully during lectures and live presentations.
• Read/write learners, word-oriented people who not only read but also take copious notes.
• Kinesthetic learners, the "hands-on" individuals who learn by doing.
Perhaps surprisingly, that insight applies today to the world of data analytics. Just as VARK has impacted classroom learning, it's become evident that not all businesspeople take in data-driven insight in uniform ways.
Gartner® recently confirmed this observation in its study on what it calls the "augmented consumer." Gartner defines augmented consumers as "an evolution of the augmented analytics market trend that aims to place insights directly into the hands of the decision maker, who would traditionally be thought of as a data 'consumer.'"
Gartner found that analytics dashboards — long the primary means for exploring large volumes of business data — are useful for only a subset of the total universe of data consumers. Dashboards are likely to be just one of many tools for data consumption in the very near future.
"Time spent in predefined dashboards will be complemented — and likely displaced to a degree — by automated, conversational and dynamically generated insights that are customized to user's contexts and delivered to their points of consumption," the report notes. "Dynamic, autogenerated and personalized data stories will offer multiple experiences that leverage a variety of devices and consumption methods, including being embedded in existing business applications and workflows."
Beyond the Dashboard
Data analytics and BI (business intelligence) technology is quickly embracing this new paradigm for data consumption. It addresses the notion that not everyone wants to know the same things or in the same way.
Some want to know what has changed. Automated business monitoring enables users to be notified about, and instantly investigate, what has changed in their critical metrics. If they need to know more, platforms can allow them to drill down into the situation as needed. As a result, users can not only respond to events much faster, but also see patterns and trends without ever exploring manually.
Some want to ask questions. Natural language queries (NLQs) let individuals explore on their terms, using words and terms with which they are familiar. There is no longer the need to stop and consult a data analyst to pose important questions, using arcane terms. Like Alexa or Siri, these tools adapt to the user and facilitate conversational exploration.
Others want insight on demand, as they work. Embedded contextual analytics technology pushes personalized content directly into the user's natural workflows, processes and applications, at the point of daily work. This means the business user doesn't have to stop what they're doing, do some analysis and return to their activity. It's fed to them as they need it.
Some want to be told stories about what's going on. Charts and tables, by themselves, don't provide narrative. They force the user to slice and dice information, working out for themselves what has happened. Dynamic storytelling explains the context behind the numbers and highlights meaningful change in an engaging way. The tool also allows users to create and share data stories themselves in order to inform other like-minded people.
Different Users, Different Strategies
The key takeaway — one that is changing the very nature of analytics — is that traditional one-size-fits-all tools are not effective for every business consumer. It springs from the idea that people shouldn't have to acquire skills in analytics before exploring data. Their area of expertise lies elsewhere — in marketing, logistics, finance, HR, operations or other specialty — and not in data science.
Expecting users to know exactly how to surface a cause or effect, or to even understand what their data is trying to tell them, is inefficient. It doesn't maximize value.
Analysts will always be essential in BI, as will dashboards. But the pace of business, for professional analysts and end-user consumers alike, no longer allows time for translation. In today's nanosecond world, the people who pull the levers of a business need to be able to grasp conditions as they evolve, based on data that's gathered in the moment.
Put another way, if an organization's BI project hasn't lived up to expectations, it's probably because it was built for technical users, not those who ultimately ask the questions and need the answers. If executives and line managers aren't using an analytics dashboard, the problem is with the system, not the individuals.
As Neil Fleming understood, the transfer of knowledge first requires a sizeable dose of awareness and empathy. In this fast-changing world where data, business and the science of analytics evolve constantly, Fleming's notion may be the most transformative insight of all.
* Gartner, "Market Guide for Augmented Analytics Tools," Austin Kronz, David Pidsley, 28 June 2021. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission.
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