You’re sitting in your fifth meeting of the day, and an excited analyst is peppering your team with the results of her analysis. She is clearly fascinated by all of the findings she was able to unearth, but you can’t muster the same enthusiasm. While you were initially curious, an endless barrage of overly detailed data tables and complex charts has smothered out any interest you originally had. Now, you’re distracted by your smartphone and what is next on your schedule. Once the onslaught of information comes to a merciful conclusion, you leave the presentation confused and frustrated—unclear of what actions need to be taken. You hope your next meeting will be more productive.
We’ve all sat through one of these “data dumps”—a data presentation packed with more facts and figures than a human being can absorb in a single sitting. You might have been the person who unknowingly delivered such a data dump, or you’ve just been on the receiving end of one of these painful presentations. A business manager once told me it’s like watching a slow train crash occur. Everyone sees it happening…but no one knows how to stop it. Each railway car (think slide) derails after the next, and there’s no end to the carnage.
As organizations gather more and more data, you’re likely going to have to suffer through more data dumps. Not even the mighty executive summary is a perfect solution. They too can be prone to data dumping, which leaves leaders with a shallow or incomplete understanding of the problems, risks or opportunities facing their businesses. Enter the data story—a more effective communication approach that combines key insights with engaging narrative and enlightening visuals. Data stories provide just the right amount of detail and help the audience to understand the meaning behind the numbers. If data dumps lead to confusion, frustration and in-action, data stories can generate clarity, understanding and action.
If you want to see fewer data dumps and more data stories in your organization, start with these three key lessons that can help your company in its transition to more effective data storytelling.
Lesson #1: The data won’t speak for itself; you must give it a voice
Whenever you have spent a fair amount of time analyzing a dataset, the noise in the data will begin to fade away and the signal will come into sharper focus. Eventually, key patterns, trends and anomalies will stand up and “speak” to you. However, if other people look at the data without the same context and background, they won’t hear a whisper. They will struggle to grasp and interpret the data the same way you did. In other words, the data tables and charts used in the exploratory phase to help you find insights may not work equally well for the explanatory phase when you share your insights with others. Your audience may end up perceiving it as an overwhelming, jumbled collection of facts and figures—a data dump.
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In contrast, data stories are designed with an explanatory focus in mind. Data storytellers recognize the data can’t speak for itself so it’s up to them to provide the meaning behind the numbers by crafting a concise, compelling narrative. In order for an audience to more easily understand and follow your key points, the original findings may need to be filtered, re-organized and curated to enhance the flow or structure of your message. By skillfully combining narrative and visual elements, you ensure your insights have a clear and concise voice so they can resonate with your audience.
Lesson #2: You must have a main point, not just information
One of the main reasons why data dumps fail is because they are packed with too much information for the audience to process. A data dump may contain several valid insights, but they are often delivered in a scattershot approach that is confusing and directionless. Whenever an audience is forced to sift through the facts and figures to find something meaningful, they will quickly lose interest and patience. In most cases, the presenters are excited to share their key findings with other people to help address potential problems or opportunities. Unfortunately, their good intentions often lead to information overload and disengagement rather than enlightenment.
On the other hand, a good data story will always have a clear destination—a main point or central insight. It serves as the “true north” of the data story and gives it a well-defined purpose. A main point clarifies what decisions must be made or what actions are required. It also influences the structure and flow of the content as well as what supporting details are necessary to provide adequate context and color. Equally important, it helps determine which data points should be left out of the narrative or formed into a separate data story. If a data dump is susceptible to being overwhelming and unclear, a data story will be streamlined and focused on delivering a main point that can inform a key decision and lead to action.
Lesson #3 – Not all insights merit a data story
Because it can take a decent investment of time and effort to prepare a data story, it is important to determine whether an insight merits its own data story in the first place. In my research, I’ve developed a two-by-two matrix that highlights when it’s a good idea to turn your insights into data stories. One key factor in this matrix is the insight’s potential value, and the other factor is how receptive or open-minded the audience will be to the new insight. If an insight only offers limited value to the audience, it may not necessarily warrant being turned into a data story. If you have a valuable insight that could be difficult for the audience to embrace, then you will definitely need craft a data story for it. An insight may be “hard” for an audience to accept for any number of reasons:
- Unpleasant: “Our sales strategy is broken.”
- Disruptive: “We need to switch supply channels.”
- Unexpected: “Nothing indicated this production issue would ever occur.”
- Complex: “Our new marketing campaign features 10 different offers.”
- Risky: “We could lose our funding if we can’t fix this problem.”
- Costly: “This workaround solution will cost $25 million.”
- Counterintuitive: “Our prospects don’t see our products the same way we do.”
In this matrix, I’ve labeled the “sweet spot” for data storytelling as the Story Zone, which is medium-to-high in potential value and falls into the “hard” insight type category. If your insight falls outside of this zone, you may not need to invest the extra time and effort to build a data story or necessarily require a data story for your insight to be understood and embraced. Knowing when it’s advantageous or important to craft data stories for your insights can help you communicate them more effectively and efficiently.
If your organization is still suffering from mind-numbing data dumps, you must focus on eliminating them today. For far too long, leaders and their companies have tolerated bad data communications. Unfortunately, valuable insights are being lost because your teams struggle to effectively communicate them. Without any intervention, data dumps will continue confusing and paralyzing your people rather than inspiring and mobilizing them. If you want to close the last mile of analytics and achieve a greater return on your investments, data storytelling will be a key skillset for both your analytics and business teams. The fate and fortune of your insights rests in your hands—everyone within your organization benefits when more data stories are being shared than data dumps.