Making the Case for Data Storytelling

If you’re interested in data, it is likely that you have heard of the new field of work called data storytelling. It seems like everyone is talking about data storytelling, and everyone wants to become a data storyteller. But what does that really mean? And why do people in the data world view this as a critical role to fill??

I like to think of data storytelling as the practice of taking analytical results, showcasing them in a dashboard (usually, not always) with visualizations, and presenting the results with a defined communication (or narrative) to divulge the meaning of the results in an easily digestible format.?

This seems like a lot of extra steps from the traditional format of presenting data in excel-like tables with populated KPIs, OKRs, and other predetermined metrics - and it is - but there is a method to the madness that is enabling companies to capture additional data points and insights. Through traditional reporting methods, also known as descriptive analytics, the business is defining what is important in advance and they are making assumptions for the cause of the results based on inherent knowledge. This type of data communication has worked for a long time - and it is still working - however, data storytelling helps to bring that level of communication and insights to the next level.?

Data storytelling enables individuals to both dive deeper into the data and discover different insights that may not have been obvious through traditional reporting methods. The reason additional/different insights are discoverable is because one of the keys to data storytelling is found within the presentation process: when communicating the narrative, an important piece to this is divulging the WHY - why something happened, why a result was the way it was. Said a different way: An individual breaks down the data, digs in further, and comes up with why something - not just that it happened but why it happened. This form of analysis, and therefore, communication of data, is referred to as diagnostic analytics.?

Data storytelling is not just a great skill for the traditional analyst to display results, but it is also a highly necessary skill for data scientists, AI/ML experts, and other individuals who are in advanced data roles. It is no secret that most insights are lost in translation from being ‘discovered’ by a data science or analytics team to being presented and incorporated into future strategy. The fault is not in the data science or analytics team for not being able to discover ‘good’ results, but the issue lies in the communication of those results. If a data scientist (or someone in a similar role) can gain this skill, the adoption of results into data-driven-decision-making (aka strategy) will likely increase.?

A dashboard can be understood with limited context. Most people have seen bar charts, pie charts, maps, etc. several times throughout their life and already know how to interpret them. An individual may not know the data behind a dashboard, but because of their existing knowledge, can figure out the meaning behind it all. When context is added to the vizzes, it enhances the user's experience even further. This is part of data storytelling and why it is so useful. Few people grew up and entered their career knowing how to interpret complex data models or how to read massive tables of data. Most people already know how to interpret a data visualization.?

So how do you learn data storytelling? The basics to data storytelling are not complex; however, the author needs to be thoughtful around how they craft their story.

> Contextualize your visualization - make sure there is enough background information for the audience to understand.

• Know your audience: there will likely be a different story to tell a C-Suite Executive vs. a Product Owner vs. a Sales team. A big part of storytelling is identifying what is important to the audience you’re working with.?

> Create a Narrative that will resonate: When a storyteller understands what is important from step #1, they can craft a narrative that will be important to the audience. And it is not all about what is important but why it should be important to them. Think through the following questions when trying to craft a narrative:

• Why did you analyze this data? What was the purpose?
• What is your message?
• What problem does this solve (or identify)? And do you have a proposed solution?
• What value is this analysis adding to the organization?
• Why should it be important to your audience?

> Summarize your insights: This can be done both through the narrative as well as the content that is displayed on the dashboard.

• Make sure that you are displaying only information that is relevant to your story and analysis and make sure to leave out additional information. Data story’s fail when there is information overload. Make sure that the dashboard is succinct, easy to read, and the visuals make sense for the information that is being displayed.?

Data storytelling is becoming a critical part of most organizations' data and analytics departments. It is a continuously growing field that anyone and everyone can enter. The first step is to grow your curiosity around data, strive to become a better communicator, and have a passion for helping your company realize the benefit of their data science and analytics teams.