Numbers don’t lie—but sometimes they can be made to fib. As anyone in business knows, one of the greatest sources of danger is a large, complex, convoluted collection of data. Massive datasets can be impacted by the preconceptions, even biases, of those who use them.
The situation is even more dangerous when data is presented with no narrative at all. Think about a series of charts and graphs supplied to a decision maker with no explanation or framework. It can only lead to confusion and superficial interpretation.
Without a story well told, and supported by an unequivocal, logical chain of facts, it’s possible for whole organizations to be led down the wrong path. The graveyard of commerce is filled with companies that faltered, and eventually failed, because the information at hand was misread.
A chart or spreadsheet on its own is dangerous because there is no context. Yet words without evidence are just an opinion. Put them together in a collaborative setting, however, and you get the best possible paradigm: data storytelling.
Data storytelling and data visualization are not the same
Data storytelling is not the same thing as data visualization. Visualization does not explain change, describe implications, or remove opportunities for misinterpretation.
Effective data storytelling is the combination of two factors — trusted data and expert narrative. Stories help convey a message and stimulate action. Data, for its part, stimulates thinking—but stories inspire resolve. Together they grab the audience’s attention on important discoveries. They assist in delivering meaning, and persuade people to care about findings.
The business case for data storytelling is strong (see how St. Lukes Health transformed their monthly reporting with interactive data narratives). When facts are communicated compellingly, decision-makers are encouraged to believe both the analytics and the recommendations. A data expert who knows how to tell a good story unlocks the true business value in data. Stories not only justify the discipline of analytics, but, when they are properly constructed, lead the business to better conclusions.
Data storytelling step-by-step
Data storytelling requires collaboration and tools, along with a certain level of skill. It may require practitioners to rethink some of their tasks and processes—but the result is most definitely worth the effort:
- Invite stakeholders and subject matter experts across the enterprise to participate. Different backgrounds provide perspective and help to build the consensus that is so important to those who make the final decisions.
- Look for business intelligence (BI) applications that empower colleagues to contribute to the storytelling process. Solutions should provide opportunities for contributors to insert insights into analytics at the point of relevance, via written accounts. Collaborators should also be able to enhance and refine narratives on the fly. As Keith Wheldon, Business Intelligence Manager at North Tees and Hartlepool NHS Foundation Trust, told us about building data narratives, “Yellowfin Stories enables us to support our data with written narrative - allowing a universal organisational understanding.” That’s the organizational tone BI teams should be shooting for. (See our full use case on the Gateshead NHS Yellowfin BI project).
- Tell the story with balance. Don’t provide too much data, or too little. Stop when the case is made, without embellishment or repetition.
- Make the audience feel something. Help them understand the significance of the information. If the presentation connects with the recipients’ pain points—financial, sales, production, whatever—they will pay attention to what’s being said.
- Make sure the analytics platform supports easy translation of charts and narratives into presentation format (i.e. slides, video, PDF, etc.).
- Give the audience an opportunity to dig deeper into the data if they wish, so they can offer alternative views. This enhances trust and leads to better outcomes.
Data storytelling unleashes the power of analytics
To be effective, data scientists and analysts need to be data journalists. Analysts live to identify causes and solve problems. They should also be able to turn their insights into compelling, digestible stories.
Data storytelling, the combination of trusted data and expert narrative, is the means to not only interrogate data, but also properly unleash its power in an organization. It won’t guarantee that the business will follow the right path—but it certainly can illuminate that path. Many times that alone will make all the difference.
Read more about the future of data storytelling in the Yellowfin Whitepaper.