What is data storytelling?
Pure, raw data speaks a very precise language that only a few specialists can understand. Data storytelling is simply a means of taking that flat, numerical information and building a contextual narrative around it that people can grasp and relate to. A report that statistically correlates porridge temperature to bear size is unlikely to get many views. Whereas a story showing a picture of Goldilocks with comparative images of different sized bears, beds, and chairs has a much better chance of being read and more thoroughly understood. And isn’t that the whole point?
The elements of data-driven storytelling
Every data story must include three elements:
- Data
Data-driven storytelling should be based (as much as possible) on clean and complete data. It may seem obvious, but it’s challenging because data exists across multiple countries, business units, and departments. The advent of new data sources, such as IIoT, is only turning up the data volume. For companies drowning in data (most of them), data management solutions help address these issues by collecting data from all sources and delivering trusted actionable insights. Here is where we begin.
- Narrative
Throughout history, humans have effectively conveyed information through storytelling. Data-driven storytelling, too, follows a traditional narrative storyline (aka “story arc”) with a beginning, middle, and end. The narrative should tell the story of what the data reveals, highlight its context, and suggest potential actions. Data storytelling software works with ERP platforms, incorporating multiple types of data analytics (descriptive, diagnostic, predictive, prescriptive) to help reveal which data is the most relevant or compelling to the story.
- Visuals
A good visualisation illustrates data connections in a way that the reader can quickly understand, then use to consider potential outcomes. Although existing spreadsheet and data visualisation software can generate charts, maps, graphs, and diagrams, combining the graphics with narrative is what gives them the all-important context and meaning. A picture is worth more than a thousand words: It’s worth thousands of Excel rows.
Data storytelling is about taking complex data and analytics and building a compelling narrative – one that grabs audience interest, conveys a clear message, and influences an action or next step.
Why data-driven storytelling is so effective
The human brain prefers stories to pure data. In a nutshell, they’re more appealing to the eye and easier to understand. This gives modern, data-driven storytelling numerous advantages over spreadsheets, data whiteboards, and digital dashboards. Data-driven stories are effective because they:
- Simplify the overwhelming amount of data that prevents decision-making. According to Gartner, 80% of traditional analytics insights will not deliver business outcomes through 2022.
- Engage more parts of the brain than pure data, increasing comprehension and retention
- Can illustrate why something is happening
- Are supported by evidence
- Use data to uncover new patterns
- Can tap into readers’ emotions as well as intellect
- Generate actionable insights
- Can connect to live data for always up-to-date analysis
- Can be personalised at scale
- Offer value to the audience
- Perform well on metrics such as click-through-rates and conversions
- Provide credibility for one’s company or industry
As we’ll see, data-driven storytelling is effective internally and externally, boosting its adoption.
Uncovering insights and extracting value: Data storytelling examples
Chances are, you’ve seen examples of data storytelling without realising it. That’s part of its beauty: Because it’s engaging visually, you don’t feel like you’re slogging through data.
- Healthcare: The World Health Organisation tracks COVID across the globe in an interactive dashboard. Readers can choose to view the number of COVID cases, deaths, or vaccinations; the various measures taken by each country; and dozens of easy-to-understand visualisations that governments and medical staff can use to help “tell” their own local stories. Healthcare organisations can also combine historical data with clinical trial results to explain the benefits and risks of new treatments to patients.
- Supply chains: End-to-end supply chain management helps to centralise multiple data sources. A case for data storytelling in supply chains could relate to sourcing and provenance. Data can come in from raw materials suppliers, RFID tags, manufacturing assets, and shipping and logistics partners. This can help tell a very dynamic story of where a product comes from and illustrate why the best labour and manufacturing practices add value and sustainability.
- Human resources: In today’s complex world of work, companies are more focused than ever on not just hiring the best teams but getting them to stick around. Modern HR software systems gather and analyse data from “hire to retire”. This presents the opportunity to create compelling data stories – showing business and HR leaders examples of how various initiatives improve employee recruitment, experience, and retention.
- Retail: With its B2C focus, the retail sector has enormous potential to connect with customers in new and interactive ways. The digital technologies that support omnichannel retail basically all result in data generation or curation of some kind. By leveraging data from smart shelves, e-commerce, and online shopping in general – retailers can generate highly precise and actionable data stories about exactly where, how, and why their customers are engaging with their products. This can help to inform business development, inventory management, marketing and much, much more.
The growing trend of data stories and augmented analytics
Data storytelling isn’t new to the enterprise. Still, until recently, it was largely a time-consuming, manual process: employees had to pull data, design and develop visualisations, draft a narrative, and generate insights. Companies typically relied upon data scientists or IT, who handle large volumes of data, to create analytics dashboards. These employees often lacked the soft skills to develop clear, compelling stories. Often, the resulting dashboards couldn’t convey the most relevant information in a way that leadership (or anyone else) easily understood.
Today, storytelling platforms linked to ERP technology systems have democratized data, so business analysts, marketers, artists, and editorial staff can collaborate with data scientists to create compelling stories. Bringing in employees from other departments not only adds soft skills but also generates insights from multiple points of view.
With the right tools, anyone within an organisation can potentially create a data story. No- and low-code software and augmented analytics tools – incorporating artificial intelligence, machine learning, and natural language processing – can automate many functions that previously took so much time. The entire process is faster, more efficient, and more effective.
Thus, according to Gartner, data stories (as opposed to dashboards) will become the most widespread way of consuming analytics by 2025, and augmented analytics techniques will automatically generate 75% of these stories.
Creating effective data narratives: The five “W’s”
Corporate employees learning to generate data stories benefit from adopting journalism’s traditional “Five W’s”: who, what, when, where, and why. So too, a good data story designer tries to answer these fundamental questions.
Before beginning their process, a story designer (or team) might ask:
- Who is the story’s audience?
- What is the goal?
- What KPIs should we use?
- What data and visuals best convey this information?
- When (over which period) should the data range?
- Where does the data reside?
- What trends does the data reveal?
- Why is this happening (context)?
Like quality journalism, data narratives should attempt to be objective, non-judgmental, and empathetic to their audiences. Companies need to guard against human bias as well as bias introduced by artificial intelligence. This thoughtful approach is critical to credibility.
Incorporating data storytelling successfully within the enterprise is more than a software upgrade. The process requires buy-in from the top down and departmental leadership’s willingness to break down data and staff silos to collaborate. While some companies hire “data storytellers” for a specific role, others make the skill a requirement for analyst positions. With training, current employees outside data-centric departments can bring invaluable institutional and situational knowledge in creating stories that drive the business forward.
To create the smoothest evolution to this new communication form, companies should consider involving experts who can guide technology installation, roadmap planning, training, and communication. Transitioning corporate data from spreadsheets and dashboards to context-driven visual stories can benefit numerous areas of an enterprise in any industry.
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