You’ve Got Data… Now What? Everything You Need to Know About Data Storytelling (and how to start)

Data is only useful if you can get value out of it. Numbers have stories, translating to incredible amounts of potential value — but only if you have a way to uncover these insights. This is the basis of data storytelling.

It’s very 21st-century but it’s really nothing new. Way back in January 2009, Dr. Hal R. Varian, then Chief Economist at Google remarked on the importance of storytelling with data:

The ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it – that’s going to be a hugely important skill in the next decades.

More than a decade later, Varian’s statement has never been more perceptive. Business Intelligence and Analytics expected to grow into a $26.50 billion industry by 2021.

Now that businesses have access to more and more data, they’re desperately looking for skilled data analysts. According to LinkedIn, data science is one of the skills with the highest demand among recruiters.

This article goes into the heart of data storytelling, including definition, importance, and why we’re not good at it. We ice the cake with data storytelling examples to learn from.

What is Data Storytelling?

Data storytelling is a simple name for a process that’s a lot more complex than it sounds. In a nutshell, it is the translation of data analysis to easily-understood insights to influence action or business decision-making.

For nearly a decade, data-driven decision-making and digital business have been on an upward trajectory. By 2018, the US alone was short of 1.5 million analytics-savvy managers and over 140,000 big data analysts. Data storytelling is a skill of data science and data analytics, working to connect decision-makers with the sophisticated analytics of big data.

Storytelling with data is still relatively new – ‘dark’ data, i.e. unstructured/unutilized data, will represent 93 percent of all data by 2020. So new, in fact, that there still isn’t a set of best practices related to compelling data storytelling.

Some experts simply use traditional storytelling analogies:

  • Creating a hook to draw the user
  • Building up to a climax
  • Including themes and eliciting emotions, and finally
  • Winding up with a set of conclusions

Other experts assert the importance of the narrative – the way to make sense of a complex world. Data storytelling should include the things that give data meaning and relevance while piquing the user’s interest. Ideally, you should be able to send someone a presentation, and the recipient should understand your point just by going through it.

Data storytellers have made their way into the workplace, and they come in the form of economists, statisticians, and even journalists, with advanced degrees in data manipulation. People in these professions already know how to either tell stories or derive meanings from numbers. In some fields, they are able to take data and convert them into actual news stories – pretty neat, huh?

Why Is Data Storytelling So Important?

Most data analysts and business decision-makers don’t glean out the full potential of data at their disposal. Storytelling with data is still a little-used skill in the corporate environment, to many organizations’ detriment.

Here are five reasons to take data storytelling seriously from now on:

1. Stories Provide Meaning

Since time immemorial, we have used stories as tools to transmit various aspects of the human experience. Data and analytic storytelling are no different, just more recent.

We use stories and narratives to give data and numbers context, helping us to interpret better and gain better insights. These are the things that make data meaningful, relevant and interesting.

People want insights that solve their problems, expand their knowledge and help them navigate various aspects of their lives. By using data storytelling, you can offer real service to your clients by giving them answers they relate to.

2. Stories Are Easy to Understand

The goal of data analysis is to change the way someone takes action or makes a decision. The analyst attempts to inspire trust, persuade, and initiate change with their analytics. For this to happen, you don’t need impressive or high-level analytics; you need understandable insights.

The impressive analytics are supposed to help the data professional to make sense of the data. Having done this, they must find ways to communicate the narrative or data story to stakeholders.

3. Data Analytics Is Complex

The average professional, even a high-level manager, has trouble understanding the details of data analytics. However, numbers can be very compelling when served properly. Stories incorporating analytics and data are more compelling than the ones appealing to emotion or personal experience.

The most effective stories, however, must combine the two aspects. They use data and analytics to build the story without losing the humanity/reality of the people or organizations behind the numbers.

Data storytelling is the last – but possibly most important – mile in the analysis journey. It explains the why of data and enables stakeholders to understand data in the language they speak. It is the reason why companies that use business intelligence make better, more informed decisions five times faster.

4. Shorthand Data Presentation

There is so much data available today, which means analysis and preparation can take up loads of time. However, business decision-makers seldom have the time to comb through mountains of data – this would be boring, tedious, and time-consuming. Instead, they need shorthand representations of insights.

Data storytelling also helps you build summaries that are backed by numbers for presentation to beneficiaries or decision-makers. With a story summary, you can state the problem and build up to the solution without losing a stakeholder’s interest.

5. Stories Improve Message Retention

Blending the narrative with visuals and data targets both sides of the brain, which cements your message in the receiver’s mind. According to research, you use 50 percent of your brain to visualize data.

The narrative/story explains the data, and the data validates the narrative. This gives you the best of logical and emotional communication.

Using data visualizations tools, specifically, helps receivers to process the data faster and makes data easier to understand. Such visuals are more attractive and appealing, which improves the receiver’s ability to recall your message.

6. Stories are Flexible

Data can tell more than just “reporting” stories, which are the most common type of story told. Data expert Tom Davenport argued that there are ten kinds of stories you can tell with data.

You can tell different stories depending on your audience and objectives, and organizations often explore the different kinds in time. The most important thing is for data analysts to move from reporting stories to correlation, causation, explanations, predictions, and depth stories/, among others.

Why Aren’t We Telling Data Stories Well?

Despite the many convincing reasons for storytelling with data, most quantitative data analysts don’t tell good data stories. This has profound implications: for one thing, analytics ends up not having the effect on action and decisions that it should. For another, the time and money that went into data acquisition and analysis effectively goes to waste.

But why does this happen? Why are organizations and individuals not enjoying the full potential of data and analytic storytelling?

1. Analysts Don’t Understand People

People in data analysis aren’t very conversant with, for want of better terms, human interactions. They understand the structure, unambiguity, and invariance of data – human beings are none of these things and much more complex. Data scientists gravitated towards structured study fields like math, statistics or computer science – and then made careers out of working with numbers.

Of course, this isn’t true of all data analysts; there are those with social science or human interest backgrounds like communications and economics. They are able to understand the more human or real implications of numbers they are working with. With deliberate effort, however, any quantitative analyst can learn how to tell compelling stories depending on their audience.

2. Schools of Analysis Don’t Teach Data Storytelling

Part of the reason that quantitative analysts don’t gravitate towards storytelling with data is that they weren’t taught much of it in school. Perhaps it’s because many faculty members teaching statistics or quantitative analytics aren’t strong storytellers themselves.

Without understanding the humanity of data, they may dismiss the importance of teaching data storytelling approaches. This is probably why one survey among recruiters showed that the number one skill required of analysts was communication skills. It came before SQL, Query and Basic Analytical skills.

3. Analysts Think Storytelling Is Beneath Them

Some analysts erroneously believe that data storytelling isn’t a valuable use of their time – given their level of technical data knowledge. They may, justifiably so, argue that there are many other people who can tell stories, but few can do what they do. Therefore the best use of their time would be to run the complex manipulations through quantitative analysis and have others do the storytelling.

While they may have a point, allowing other people to translate their analytics results into stories has disadvantages – the obvious one being that such people don’t have the same grasp of the bigger picture as someone that worked on the data from scratch. Additionally, it is more labor-intensive for someone else to go through the data again and derive stories from it.

4. Analysts Struggle with Communication

As stated previously, analysts with a grasp of the world of data often struggle with human interactions. It isn’t uncommon for an analyst to say that while they understand the data, they struggle with ways to communicate their insights. This ends up taking a lot of their time, which is why many of them would rather not get into it at all.

Successful Data Storytelling Examples

Having data isn’t nearly as important as what you choose to do with it. By 2013, only 4 percent of companies had the resources to utilize their data properly. Thankfully, the last six years have seen an explosion in the business intelligence industry, with many successful data storytelling examples. Below are a few of them to inspire you:

1. Marie Curie

Marie Curie offers support for over 40,000 people with terminal illnesses in the UK. In 2015, they launched The Great Daffodil Appeal, using data-driven personalization to motivate the public to collect money for them.

They sent out a launch email using a supporter’s postal code data or current geolocation data and matched the supporter with the closest collection site. In the email was a personalized dynamic map detailing how the supporter can reach the closest collection center in real-time.

Using data modeling, they derived their target population and sent persona-driven messages to all supporters. These messages were crafted according to the supporter’s previous interactions with Marie Curie and collection history. They included live videos to elicit emotion and encourage supporters to collect.

As a result, they had considerably greater signups with a leaning in favor of online signups. This clearly demonstrates the efficacy of digital data storytelling.

2. Uber and Spotify

Uber uses data analytic storytelling when communicating with their customers annually. They had been previously using the financial angle to tell their riders how much money they’d spent on Uber per year. Now, they’ve shifted to showing the value that the taxi-hailing service has given the clients.

For example, they wrote, “You have driven X miles with Uber this year, the equivalent of Y journeys across the world.” While a seemingly simple statement, generating such an insight leveraged knowledge from multiple teams and skillsets.

In this statistic, they feature personalized statistics displaying each rider’s experience with the app. As a result, the rider immediately sees the impact of the app to their daily lives.

The themes featured the three important elements of data storytelling:

  1. Narrative
  2. Accurate data
  3. Useful visuals

Similarly, Spotify sends annual recap stories to their customers through email. They use short stories that curate interesting stats for every user, e.g. the number of minutes listeners used the app for music. It is more impactful than sharing financial data or just saying thanks – it communicates the impact of their service.

3. Facebook

Perhaps the master of data storytelling is the social media giant, Facebook. You’ve seen storytelling with data in action countless times on your timeline:

  • A message on your birthday that includes memorable moments from the previous year
  • Happy new year message with similar insights
  • Friend-versary videos highlighting the depth of your interactions
  • Number of times friends have liked, loved or ha-ha’d your posts
  • “This day in history” memories
  • Most viewed photos or posts

All of these personalized insights make you think that they’re actually watching your interactions, and not in the spooky, Big Brother way. Instead, they elicit warm emotions and show the positive impact of the platform on your social life. As a result, you’re encouraged to keep posting and sharing details of your offline life with your online “family”.

How to Tell the Best Data Stories

The first step of telling a great and memorable data story is to find great data. The best stories should come from your company/business’ stores of data. Proprietary data is useful if you’re aiming for a content marketing strategy that boosts visibility and builds your brand.

Step 1: Find Good Data

Luckily, even without buying any data, you have plenty of data within reach for your business. If you keep good records, you have online and offline analytics, customer data, surveys, reports, and case studies, among others.

If you have limited resources to build your own data, the Internet is packed with useful data from credible organizations. Such organizations may offer data collection as their core business or they may be in your vertical, but with more resources. Use these sources to build good data stories or support your internal data.

The best sources include research firms, industry publications, and government agencies. You can also find more free data sources to supplement your own data to create balanced stories.

Step 2: Determine Who Tells the Story

If you haven’t done it before, knowing how to tell a good data story can be intimidating. This is especially true if you don’t have a background in mathematics, statistics or data science and analytics. This shouldn’t intimidate you, however; some of the best data stories come from marketers and even journalists.

In fact, statisticians and analysis have the technical skill, but they struggle with emotional or human elements. The best team is comprised of people that can interpret both the technical and human aspects.

The marketer knows the audience and what the audience needs; they’ll be able to translate the story into their language. The analyst knows the data, and they can glean insights that a non-professional would miss.

Step 3: Choose Your Subject

An interesting story should be at the heart of any data storytelling attempt. It is possible to use data to give an otherwise boring story an interesting spin. But you still have to ensure that the subject is interesting or relevant for the audience for whom the story is being written.

Remember that data storytelling is more about the effect of numbers on the people than about the data. Often, you start by creating the story idea and then assessing the data to see what it shows you. Sometimes, the data may point to an interesting insight or angle that allows you to build a story.

Step 4: Support with Credible Data

The aim of data storytelling is to build trust and communicate insights in a way your audience can understand. Therefore, you need credible data from unbiased sources to help make your case.

Think about the data you want, and why you want it. Talk to your data management team if you have one or find credible resources according to your story idea. Even though you have a query, remember that the data may lead you in a different direction from the one you thought.

Make sure to use the latest insights, especially if you’re in a dynamic field of business. Be responsible when using sensitive data, and always cite the sources of external data you use.

Step 5: Craft the Narrative and Visualizations

A good data story is made up of a good narrative backed by credible data. Your narrative can have different goals: offering new information, changing perspectives, or inspiring action, among others.

Building your narrative includes using visualization tools to guide your audience through the story. Ensure your mode of delivery provides background and context. It should also help your audience to understand the message effectively.

The brain processes visual data in as few as 13 milliseconds – such is the power of data visualization. Ill-designed or incorrect data visualizations will do more harm than good to your story. This is why it’s important to use a designer or analysts who know data visualization best practices.

Final Thoughts: Data Is NOT Scary

Data storytelling is the future and all businesses need to embrace it sooner rather than later. Businesses today have more access to data than ever before but all this is useless if you can’t draw better insights and make better decisions.

Buyers are exposed to the same content formats, so businesses that present unique stories or unique experiences will have the edge in the consumer’s mind.  Start learning how to analyze data and how storytelling with data works. Use it to mold interactive and visual experiences that are meaningful to your audience.

Don’t leave your best assets to collect dust in your servers, logs, cabinets or anywhere else you store your data.