Enhance Your Data Analytics With Storytelling

Data is a powerful tool. Therefore the ability to enhance your data analytics with storytelling can help uncover insights from your data that you might have missed before.

With the correct data, you can learn what is happening inside and outside your business. Data can show you if your customers are fleeing to your competitors. If you’re wasting a high percentage of materials each month and if your latest marketing campaign is failing. But although datasets and spreadsheets can tell you what is happening, they don’t do a very good job explaining why.

Sure, your material waste is through the roof. But why is that? What is the cause behind the increased waste? Is there a way to fix it so you can be more efficient? In order to understand the data and make decisions from it, you need a way of telling the why behind the what. Numbers and charts won’t do this alone. It would definitely help if you had more, which is where the storytelling part comes in.

What is Data Storytelling?

Data storytelling is a way of communicating information clearly and concisely using a compelling narrative. It’s one of the most commonly skipped parts of the data analysis process, but it’s the secret key to unlocking the insights hidden within the data. The need to tell stories is hardwired within us, as is the need to hear stories. When we listen to insights told in the form of a story, it makes it easier for us to connect the dots and get the necessary understanding to know what to do next.

Data storytelling combines three significant facets of data analysis:

The Science of Data

Data science is collecting data and extracting knowledge and insights from within it. A data scientist, therefore, has to sift through the conglomeration of data from disparate sources and present the key insights in a readily available format. While data science and analysis is relatively new (growing exponentially in the last few decades and even more so with business intelligence), it has transformed our ability to run a business and make accurate predictions for the future.

Visualisations

These refer to the technology solutions we use to present the key insights extracted through data science. Dashboards are an excellent way of presenting data, giving us graphs and pie charts that are easy to read and interesting to view. Data visualisation can provide you with a comprehensive snapshot of what is going on, but again, it cannot tell you the why behind it.

Storytelling

This is the missing piece but arguably an essential part of the presentation. We can comprehend key insights and fully understand the visualisations by giving data a narrative. The ability to use the data to tell a story will turn a bunch of dry graphs and charts into an engaging map full of twists and turns.

How to Tell a Story With Data

In order to tell a story with data, you need all three of the above components. Although most data scientists are not naturally the best storytellers, adding a narrative to the data and telling a story is relatively simple. There’s no story without data to guide the way, and visualisations are key to help prove the point. Once you have accurate data and a comprehensive visualisation, you’re ready to give your data a voice.

Think of the data points as characters within the story. Each data point has a meaning, playing a role within the larger narrative of your company to tell you something important. Companies have used data storytelling in different ways, often using these compelling narratives to reach out to customers. Spotify does a yearly recap story for its customers called “Spotify Wrapped” that uses data on their listening habits to tell them who their most popular artists are and how many minutes they’ve listened to each. This tells a short little story about who the customer is, giving them an engaging view into their behaviour, likes, and dislikes. Compared to a spreadsheet or pie chart, this form of data storytelling does more to reach out to customers.

Why Data Storytelling Works

Consider an eCommerce cart with a high level of abandonment and a high level of customer growth. It also shows a difference in checkout times between your two sites with a variance of 10 minutes. Sure, these data points can tell you that you’re losing customers, but what else do they tell you?

Now consider the same data told through a story. A customer logs onto your site to make a purchase. They’re met with three different forms to fill out and multiple web pages to click through. The entire checkout process takes an average of 15 minutes, and they have a busy life. Therefore, they give up halfway through and go to your competitor’s website. Your competitor’s checkout process can be done on one page in under 5 minutes. The customer checks out there and, happy with the process, buy from your competitor the next time they need the product. While your customer retention rate slips, your competitor stays strong.

These same data points told as a story clearly show you what you’re doing wrong and what your competition is doing right. This allows you to make the changes you need to create and prevents you from making the same mistake in the future. The next time you’re making tweaks to your website, you’ll remember the story about the busy customer and keep your navigation process as simple as possible.

Conclusion

One of the greatest assets of a quality business intelligence platform is its ability to weave data storytelling into its visualisations. Data storytelling works because it provides data with context that enhances your understanding. Without the context surrounding the disparate pieces of data, you’re left wondering how the dots connect. You can easily turn a dry data report into a clear and compelling narrative using BI tools.