Business and reporting have been inseparable since the birth of human civilization. More than 5,000 years ago, the Sumerians of Mesopotamia are credited with developing Cuneiform to record transactions on clay tablets. Throughout history, reporting continued to be an integral part of trade and governance. For example, in 1086, William the Conqueror commissioned the Domesday Book to record a comprehensive survey of taxable resources and assets across England. In the fifteenth century, the powerful Medici Family pioneered a double-entry system with credits and debits to secure its dominance during the Renaissance.
In today's data-intensive environment, business reporting continues to play a crucial role in helping leaders monitor and manage their team performance. One of the main functions of most data-related teams—accounting, finance, analytics, data science, and research—is to produce these reports periodically for internal or external stakeholders. While much of the routine reporting has been automated via dashboards and scheduled reports, data and business professionals are still tasked with manually assembling these reports as presentations or written documents.
If there's one criticism of traditional reporting, it is these reports often lack adequate context or interpretation to help decision-making. In response to this issue, there has been an increased focus on “narrative reporting” where more context and interpretation is overlaid or inserted into the standard reports by either human beings or artificial intelligence. With the addition of a narrative component to these reports, some people may view them as data stories. While on the surface they may appear to be the same, they are fundamentally different things.
While business reports can be generated for ad-hoc purposes, most are recurring (daily, weekly, monthly, etc.). They provide high-level summarized information with a series of breakdowns across a set of key areas. Depending on the report's audience, it will contain comprehensive data on a series of business units, regions, teams, products, functions, partners, etc. The reports are designed to be informative and exploratory, enabling users to pick and choose what is interesting or important to them.
For example, a monthly marketing report may cover the performance of different channels or campaigns. Marketers can use the report to examine how various aspects of their marketing efforts performed each week or month. A narrative version of report would provide additional contextual details behind the channel and campaign performance and share observations based on key anomalies, patterns, or trends.
However, unlike a data story that is structured around a storyline that explains a specific insight, a narrative report follows a reporting structure rather than a narrative structure. Regardless of what is highlighted in each section, a narrative report will still be consistent and comprehensive in its approach. While the added context and interpretation will vary, the underlying structure of the report isn't going to shift each period. Even though a narrative report provides more explanation than a regular report, it is still primarily a report.
If you look at a narrative report and a data story side by side, you may not notice that many differences on the surface. Both will feature key takeaways and annotated charts; however, the focus of the narrative is very distinct. Rather than providing a cohesive story, a narrative report simply highlights the main takeaways of each section or focus area. Frequently, a section may not feature anything notable. Still, for consistency reasons, each slide will require a takeaway, even if it just states there’s nothing material happening. Whenever there are meaningful observations to share, the narrative report cannot fully explore each potential "story thread." All it can offer is a variety of narrative fragments that may or may not be related.
On the other hand, a data story singularly focuses on explaining a specific insight discovered in the data and uses a narrative arc to communicate it. A data story tells the whole story rather than just bits and pieces of it. While a data story will not be as broadly focused as a narrative report, it will go deeper into a specific topic. Each slide in a data story performs a specific role in advancing the audience's understanding. A good data storyteller will remove extraneous information that doesn't support or detracts from the intended storyline. Each data story will be unique and doesn’t need to conform to reporting conventions.
Even though both forms of communication share similar narrative techniques, there are subtle but important differences in how they’re utilized. For example, in a narrative report, annotations will be used to add context to major anomalies, trends, and patterns that emerge in each period. However, in a data story, only the observations that tie into the overall story will be annotated. In a data story, you would consider using different chart types and editing them if it helped communicate your key points more clearly. However, in a recurring narrative report, you would value consistency over the need to align the charts to a particular narrative.
Many data teams want to deliver more data stories within their organizations. If your analytics team is already accustomed to delivering narrative reports, you may feel the gap between the two areas is relatively small. However, the chasm is wider than many anticipate, as analysis is the missing link between narrative reporting and data storytelling. Without exploration of the potential story threads, you won't discover meaningful insights to share as curated data stories.
Unfortunately, many data professionals haven't been given the opportunity to develop strong analysis skills because they're mainly tasked with wrangling data, building dashboards, and preparing recurring reports. When interviewing candidates for an analyst position, I was shocked by the number of seasoned analysts who lacked basic analysis experience. Many had impressive technical experience working with leading business intelligence and data visualization platforms, but they couldn't point to any analysis projects other than handling occasional ad-hoc requests.
First, analysts must be given time to explore the data to find insights. Without freeing up their time to perform analysis, analysts will not develop any expertise in this area. They will subsequently lack the building blocks—observations and insights—they need to craft data stories. Increasingly, technology will reduce the burden on analysts to perform repetitive, manual data work, freeing them to focus on more strategic analysis. More and more, artificial intelligence will also be useful in augmenting analysts' analysis capabilities.
Second, data teams must create venues or opportunities for sharing data stories. For example, many organizations have recurring meetings to review periodic performance. Rather than allocating the entire hour to reviewing and discussing these reports, carve out 15-20 minutes for someone to share a 'deep dive' on a particular insight. Initially, business teams won't be asking for data stories, so it's essential to give them a sample of what's possible and get them excited about the valuable insights that your data can offer.
Third, while narrative reporting is related to data storytelling, it requires a distinct set of narrative and visualization techniques that are unique to data stories. Many people are comfortable visualizing and annotating data but lack the ability to craft narratives around key analysis findings. Data storytelling education becomes important because it teaches data professionals how to turn the results of their analyses into engaging narratives that can drive change within their organizations.
Reporting—traditional or narrative—will never be replaced by data storytelling. They serve very different but complementary purposes. Well-designed reports and dashboards will "frame" where potential data stories will emerge from the data. A good narrative report can help business users identify meaningful observations, resulting in potential story threads that require further exploration. If one of the story threads yields an insight, a data story can be used to explain the opportunity or problem to key stakeholders.
Essentially, narrative reporting happens before analysis, and data storytelling happens after analysis. Narrative reports use narrative on a broader basis to answer questions and help prioritize and trigger data exploration. Data stories use narrative on a narrower basis to explain an insight that was discovered and inspire action with it. While both rely on narrative elements, they demand tailored approaches and specialized skills. High-performing data teams will have expertise in both areas.
If your team would like to improve its narrative reporting or data storytelling skills, let's meet to discuss how we can help your team with its data communication needs.
Effective Data Storytelling teaches you how to communicate insights that influence decisions, inspire action, and drive change.