Throughout this blog series we’ve emphasized the value in proactively seeking opportunities as the world around us changes. New market spaces emerge when consumers evolve and conditions shift. The key to unlocking growth potential during these times of uncertainty is understanding how external factors impact your business – and leveraging this understanding to guide ongoing decision making.
The previous three parts of this series offer an in-depth look at the potential value hidden within your existing data inventory, the importance of taking a strategic approach to brand positioning, and the power of actively listening to customers. Now it’s time we pull all these pieces together and convert the collective insights into a cohesive data story.
Data storytelling is how we clearly and effectively communicate the actionable insights pulled from complex analytics. By shaping a memorable and impactful narrative using numbers, language, and data visualization, we’re able to illustrate key points in a way that is tailored to a specific audience. Data storytelling goes beyond charts and graphs; its ultimate goal is to answer the most pressing question – what is all this data telling us?
We’ve spent the past 35 years partnering with brands to organize, analyze, understand and apply their data-driven customer insights. The most common challenge we see is when it comes time to apply the insights, usually because brands are unsure of what the data means within the context of their business goals. Data storytelling is how we overcome that challenge, so here are the elements of a good data story:
From market conditions to consumer behaviors, accurate and reliable data is the foundation on which the cohesive story is built.
Obviously, data is the central component of a data story. The important thing to remember is that it must be the RIGHT data; strategically organized and analyzed in a way that yields actionable insights. Simply amassing high volumes of data is not a recommended approach because this just consumes resources and overloads the system.
Start by digging into your existing data inventory to identify the missing pieces. Then, systematically bridge the data gaps through secondary data sources, targeted market research, and customer feedback initiatives.
When language adds context to data it helps explain to the audience why particular insights are important.
It’s one thing to identify a statistically significant data point, but it’s quite another to translate those statistics into broadly relatable terms. Our job as data professionals is to present analytic findings in a way that is consumable to a wide audience. We want to answer questions, not present information that raises more questions. This is where merging data with narrative plays an important role.
It’s easy to understand that 60% is more than 40%. But what implications does that have in relation to a company’s business objectives? Providing observational statistics may be interesting, but just because they’re interesting doesn’t mean they’re actionable. At the end of the day, the power of data-driven insights is in their application to guide decision making. Adding narrative detail to these data points will provide relevant context and empower the reader to understand why those statistics are worth mentioning; and how they can be applied to real-world scenarios.
Creatively illustrating key data points can enlighten readers and reveal comparative insights that are less prominent when presented in a static data table.
Engaging content is more likely to resonate with the audience, so data visualization helps pull and keep the reader’s attention. Thinking outside the box of traditional charts and graphs, finding ways to visually convey a point is conceptually simple yet difficult in practice. Including images without a purpose will only be a distraction, as will overly complicated infographics. We want to add layers and depth to the story, not fluff or clutter.
To effectively apply visual design, the data story itself must be clear. With the analytics and narrative in place, highlight points of the story that warrant added emphasis. These are the areas where visuals can add value and enhance the reader’s experience.
Every good story has an ending that ties all the pieces together and leads the audience toward valid conclusions.
Data story endings are commonly more “to be continued” than they are “happily ever after.” This is due to the multi-layered nature of data where new story lines open as others close. What’s most important at this point is establishing solutions for the initial objectives and addressing the questions that started this entire process. Without a complete, conclusive ending to the primary storyline (i.e. the most pressing business questions), audiences will be left uncertain and unsure of how to move forward.
Data storytelling is where we get to enjoy the fruits of our labor, so it’s important to invest in the labor; otherwise the fruit will be dry and tasteless.
At MacKenzie, we bring passion and curiosity together to create remarkable customer experiences and drive growth opportunities for our clients. Our focus is on giving you the clarity and confidence to make informed business decisions. For 35 years we’ve honed our data storytelling skills; driving brand agility, innovation, and evolution.
Want to learn more about the topics from this blog series? Give us a shout!
PART 1 – Data Inventory (Read Here)
PART 2 – Brand Positioning (Read Here)
PART 3 – Active Listening (Read Here)
PART 4 – Data Storytelling