From data to impact: Elevate your analytics with the power of data storytelling
Some believe that data analytics consists of just two pillars: data analysis and data visualization. However, while these are certainly essential in data research, there’s a third pillar which is arguably the most important one — data storytelling.
Data storytelling is the practice of turning data observations into a meaningful narrative that’s easy to understand. While it is crucial to analyze and visualize the data adequately, it’s simply not enough, as even the greatest data research will not create an impact if stakeholders fail to comprehend it. Thus, the key to delivering an impactful analysis that drives change is how it’s communicated with stakeholders and business leaders.
Why is it important to tell a story?
Storytelling has been a fundamental part of humanity for thousands of years, and our brains are evolutionarily wired to respond to stories in a way that is different from how we respond to facts. We react to stories emotionally, and this emotional reaction can motivate action, creating change in behavior or beliefs. In contrast, when we hear a list of facts or findings, we may be able to understand the information intellectually, but it is less likely to inspire us to take action.
Therefore, when presenting data analytics, incorporating storytelling techniques can significantly engage and motivate the audience in a way that presenting findings alone cannot. Remember, the underlying role of a data professional is not only to find interesting observations, but to turn them into actionable insights that shape decision-making. The most effective way to do so is by transforming data insights into a compelling data story.
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How to tell a compelling data story?
The ability to narrow down vast amounts of data points into clear and concise stories can be extremely challenging, especially in the context of big data environments that are inherently noisy and cluttered. Each of the three pillars of data analytics — data, visuals, and a narrative — could all be described separately — but in practice they are commonly intertwined.
Here are some fundamental data storytelling techniques to help highlight the core narrative of your data analysis:
- Know your audience
Understand your stakeholders’ needs, preferences, and level of familiarity with the data. Presenting data in a manner that aligns with your audience’s perspective enhances engagement and comprehension. For example, executives might seek concise, high-level insights, while technical teams might appreciate a deeper dive into methodology. By recognizing the nuances of your audience, you not only ensure that your data narrative is relevant and impactful, but also foster a more collaborative and informed decision-making process.
2. Less is (sometimes) more
Equally important to how your data is presented, is choosing what data to present. When conducting data research, many data points, observations, and charts are produced, leading to a natural desire to share all of the hard work that was put into discovering these findings. However, not all observations are necessary to tell the story, and too many observations may lead to cognitive overload for your audience. Remember that the time and attention of your stakeholders are always limited, so utilize these resources wisely — only present findings that are essential to the story, at the level that suits your audience.
3. Start with the context
Before diving into the details of your analysis, provide your audience with some context: Why are we looking into this? What do we know so far? What is our goal? Keep in mind that not everyone is fully acquainted with the topic. This is your opportunity to bridge the gap and provide essential background information, allowing them to fully engage with your data story.
4. What’s in it for me?
Tech professionals are highly impact-driven. Leverage this by expressing the untapped potential your analysis aims to uncover. For example, you can capture the attention of your audience with a small “what if” illustration — “Increasing the adoption rate of this feature by 15% will in turn increase revenue by 10%, amounting to an annual increase of $100M”. Depicting the opportunity to impact top-line business KPIs will make it easier to bring decision-makers on board.
5. Use explanatory headlines
Most people, especially non-technical stakeholders, don’t interpret charts easily. Thus, people might not make any sense of your visualization, or worse, they might misinterpret it. To avoid this, use chart headlines to express the most important takeaways, rather than describe their technical properties.
6. Explain your data story to a 10-year-old
Before delivering your data analysis, test yourself by trying to explain the outlines of your data story in a few clear sentences like you would to a 10-year-old. Articulate the story in the most simple yet comprehensive way possible, to see if all the dots connect while keeping the narrative coherent and robust.
7. Leverage visualization techniques
Utilize the following data visualization techniques to guide your audience’s attention towards specific elements:
- Focus areas: Shapes, boxes, ellipses, or other visual cues can be used to draw attention to critical zones in your chart.
- Use of color: Use colors effectively to emphasize important areas, bars, trend lines, or other elements.
- Arrows: Utilize arrows to pinpoint spikes, drops, or noteworthy events such as holidays, product launches, or bugs that might have influenced trends and metrics.
- Benchmarking: Reference lines can be used to facilitate comparison against benchmarks like averages, company goals, or industry standards, to provide context for your data.
- Symbolic icons: Use icons, emojis, or symbols to underscore winning and losing groups in A/B tests, highlight desired and undesired outcomes, and provide instant visual context to enhance your narrative.
Summary
In the dynamic landscape of data analytics, three pillars hold principal importance: Data analysis, data visualization, and data storytelling. While data and visuals are essential, it’s the craft of transforming data insights into meaningful narratives that truly empowers decision-makers. By leveraging these storytelling techniques you will be able to effectively communicate complex analyses, foster collaboration, and ultimately drive impact in your organization.
