Telling data stories, and why we find it important.

Oct 18, 2021 • 3 MIN READ

Every business wants to make good decisions, and good decisions rely on good information. To us at Ayazona, understanding and translating data into meaningful insights is crucial. We have crafted data stories to help us in communicating key insights compellingly and clearly, thus inspiring action and driving change in the business.

We understand that data storytelling is not just about making great data presentations and graphs, but communicating insights that deliver real value. We use these narratives to explain how and why the data we collect changes over time, and often through visuals.

Why do we find this important?

Understanding context and encouraging change or action are at the heart of data storytelling. A story can help us express complicated ideas and simplify and accelerate the decision-making process for our stakeholders when we examine and present our facts.

Tom Davenport, a thought leader in analytics and a professor of Information Technology and Management at Babson College, emphasizes the role and power of narrative in data analytics:

In other words, a story ensures our data is memorable, persuasive, and engaging.

What are our story elements?

To ensure we deliver an engaging narrative, we consider the following three as our core data story elements.

  • Data
  • Visuals
  • Narrative

Together, these elements put our data into context and pull the most valuable information into focus for our key decision-makers.

How then do we tell our story with data and analytics?

To tell a good story, we ask these two questions: how do we determine a good story? And more importantly, how do we tell it effectively? To achieve these two, we follow these steps:

  1. Identifying our story.
  2. Being aware of our audience.
  3. Building our narrative.
  4. Using visuals to present and clarify our message.

How do we identify our story?

To us, the first step to telling a great data story is uncovering a story worth telling. We start by asking forming hypotheses or asking questions, then compiling and digging into relevant data to find answers.

As we collect and analyze our data, we consider using approaches like looking for correlations, trend identification, drawing comparisons, looking for outliers, and paying attention to data that are counterintuitive. These approaches help us in coming up with themes and develop structures for our story.

The audience?

As we develop and share our data story, we always ensure we remain aware of who our audience is. To remain relevant and ensure we impact our audience, we look into responding to the question: Does the story we are telling solve a problem they care about or provide needed insight?

We also ensure that we customize our story and approach it from different angles depending on the audience we will be sharing it with.

The narrative?

With our audience in mind and the data at hand, we can start developing our narrative. Here, we consider: who we are talking to, what we want our audience to do or know, and how we can use our data to make our point.

We ensure that our narrative is not just an explanation of our data, but a story that tasks our audience on a journey. To do this, our data story takes this shape:

  1. context - here we look for a hook to engage our audience.
  2. characters - who are the key players?
  3. problem - what is the conflict?
  4. solutions - how can the problem be solved? Or what key insights or actionable steps should be taken, emphasizing the value.

The visuals?

A good data narrative needs visuals. When communicating to non-technical audiences, visuals become a powerful way to engage and improve their retention.

To enhance our audience's comprehension at every level, we ensure that our data story is visualized. Telling data stories with visualizations help us in simplifying the information, highlighting the most important data, and also communicating key points quickly.

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