Data Storytelling: 4 Common Myths Debunked

March 6, 2017 Mary Grace Glascott

data storytelling

Tom Davenport, the distinguished professor, author and co-founder of the International Institute of Analytics (IIA), has good and bad news for companies investing in Big Data projects: most companies today report that their Big Data initiatives have been successful, yet half cannot measure the benefits from their projects, and a majority struggle in implementing the cultural change necessary to fully operationalize their Big Data initiatives.

Davenport reported those findings, courtesy of the fifth annual Big Data Executive Survey from NewVantage Partners, in a recent article for Data Informed.

According to the survey of business and technology leaders from 50 leading companies, more than 85% of respondents report that their firms have started programs to create data-driven cultures, but only 37% report success thus far. Davenport states, “Big Data technology is not the problem; management understanding, organizational alignment, and general organizational resistance are the culprit. If only people were as malleable as data.”

One of the keys to turning your business’s Big Data project into a success is embracing the idea of ‘telling a story with data’, says Davenport. It’s no surprise why - telling stories with data helps humanize Big Data technologies by making it more accessible to non-technical users.

In an article for Deloitte University Press, Davenport explains the effectiveness of telling the stories in your data:

Stories have always been effective tools to transmit human experience; those that involve data and analysis are just relatively recent versions of them. Narrative is the way we simplify and make sense of a complex world. It supplies context, insight, interpretation—all the things that make data meaningful and analytics more relevant and interesting.

Transforming data into something meaningful, relevant and interesting? Sounds great. So why are individuals and organizations so bad at telling stories with data?

The confusion lies within the definition of data storytelling, understanding how it can be operationalized across the enterprise and eliminating the preconceived barriers that make data storytelling capability a reality.

To help your Big Data project benefit from the power of data storytelling, let’s dispel four common data storytelling myths:

1. “Our dashboard does data storytelling!”

Make no mistake—a visualization can be a powerful way to display information, uncovering anomalies, patterns and other insights not easily seen in data alone. But is a dashboard a story? Return to Davenport’s definition of data storytelling: “supplying context, insight and interpretation.” A dashboard may contain one or two of those elements, but only via a true and complete narrative form can the user gain all three.

As a better approach, what about deploying a dashboard with an accompanying story, written in plain English language? A story that is dynamic, changing as the user continues to drill down in the visualization, and offering explanations and deeper insights with each iteration. Now that’s a story any employee would want to read.

2. “Generating stories from data? No problem. Our IT team can just build them.”

When some technologists see text being generated from data, they may think, “I could do that.” And by building some tools that achieve basic translation driven by pure business logic, they may be able to, although it would take a significant amount of time and resources. Even then, are snippets of text populating pre-defined templates really a story? It’s clear given our definition of data storytelling, the answer is ‘no.’

By comparison, an Advanced Natural Language Generation (Advanced NLG) platform, like Quill, transforms data into Intelligent Narratives that are driven by the purpose of a particular communication. Quill highlights what is most interesting and important in the data, and does so at tremendous scale, generating countless personalized stories, and on-demand. These stories are indistinguishable from what a good human story-teller would write, powered by the scale and automation of artificial intelligence.

Leave the true storytelling capabilities to an intelligent system, so you can focus your efforts on the impact that the story’s outcomes could have on your business.

3. “We have too much data. Big Data! Our data analysts need to centralize the data first before we are ready.”

The “data first” argument is a good one, although quickly becoming irrelevant. In the end, “Big Data” is just, well, data, and the idea that data first needs to be centralized in order to extract insights is becoming obsolete.

Instead of making further infrastructure investments to manage all of your data, and then asking your data, “What secrets can you tell me?,” you need to turn your primary focus -- and investment -- to answering the business question at hand: “What is driving sales performance?”, “Why are we not meeting inventory goals?”, “What is contributing to the uptick in fraudulent activity?”.

After determining the business goal of your analysis, you should then pull in the necessary data to answer that question. Sounds simple, but too many businesses get it backwards. It's truly, first and foremost, about the story. Everything else comes after.

4. “It’s too time-consuming for me to tell every story that needs to be told about our business.”

Tom Davenport speaks to the time-consuming nature of manual analysis:

It takes a lot of analysts’ time to think creatively about how to tell a good story with data. In fact, one senior analyst at a pharmaceutical company told me that he (and most members of his analytics group) spend about half their time thinking about how best to communicate their analytical results.

Many analysts will be reluctant to devote that much time to the issue, even if it would make them more effective.

In the end, a story is only powerful if it is relevant to the person reading it, and that requires personalization. But how do you scale personalization?

To illustrate the scaling power of Advanced NLG, let’s return to one of our questions above: “What is driving sales performance?”

Our Quill platform can automatically personalize the answers to that question, immediately generating custom narratives for different audiences: the director of sales, the district manager, and the individual salesperson. Each story is different, highlighting what is most relevant to that person and their needs. By automatically transforming data into Intelligent Narratives, Quill dramatically reduces the time and energy spent on communicating data insights to others.

Davenport’s observation that humans will never be “as malleable as data” holds true, but by operationalizing data storytelling across the enterprise, data can start to be made more human.

See how Narratives for Business Intelligence helps Qlik, Tableau, SAP, and Power BI users tell stories with their data >>

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