How To Tell Stories with Data: 5 Tips

There are few strategies more influential in a presentation than referencing raw data. Having the ability to share completed research or the analysis of collected statistics gives the subject you are presenting on the necessary context and factual basis that we as humans need in order to accept something as truth.

Today, this type of raw data is available on more subjects than ever before. Social media, smartphones, and technology companies like Google allow organizations to “accumulate massive amounts of behavioral data on the entire world population,” says Thomas Goulding, professor for Northeastern’s Master of Professional Studies in Analytics. “In almost every industry, there are new stories about the advancements of research and functionality that are happening because of research and data analytics.”

The influence of data ranges from DNA and ancestry tracking in crime investigations, to the use of artificial intelligence in warehousing and medical fields, to the analysis of customer and audience trends in business.

This access to relevant, factual information across a variety of industries has led to a reliance on data for decision-making within organizations. However, the continuously gathered information isn’t consumable in the unstructured format it is collected in. Thus, there is an increasing need for data analysts who can interpret and present that data in a way that those groups can use effectively.

Learn More: What Does A Data Analyst Do?

With analytical tools and skills, individuals in these highly technical positions are able to sift through the massive amounts of information being collected in order to determine trends, reflect on findings, and draw conclusions from the data that other business groups can reference when making decisions.

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Presenting Data Effectively

Those who work with data are typically skilled in analysis and interpretation, yet many analytically-minded individuals struggle with the act of sharing their findings with others in a way that is not only informative but engaging. The need for high-level presentation skills, however, is critical in the modern workplace; 70 percent of employees report that advancement in their field is contingent on these abilities, and data analysts are no exception.

“One of the key skill sets that you have to learn as you study data analytics is the distillation and presentation of the data,” Goulding says. “It’s one of the most important skills because if you can’t [communicate data effectively], then the data you’ve analyzed isn’t useful to anybody.”

Analysts mold data to make it more easily digestible to larger audiences through a practice called data visualization. Most often, this practice focuses on the development of graphs and other artistic elements to represent findings and visually highlight trends, outliers, or other conclusions, in an effort to translate statistics and fact-based information into a compelling story.

Data analysts have industry tools and software programs like Tableau at their disposal for creating these artistic representations of data, and these products have come a long way in assisting with bridging the creative gap for analysts. “The tools are much more sophisticated [now],” Goulding says. “They create more artistic ways of visually presenting the results of data, and it’s done automatically so the analyst doesn’t have to be an artist…[They have] tools that will create effective visualizations for [them.]”

Data Storytelling vs. Data Visualization

Data visualization and data storytelling, while intertwined, are two very different practices; the former involves creating a visual representation of collected information, and the latter is about using human communication to help an audience develop a connection to that information.

According to Goulding, effective data analytics “still requires a human being to talk about analysis and visualization,” and calls for high-level abilities in both of these practices. Yet the types of software and programs available for data visualization work don’t exist at the same level for data storytelling. Instead, the task of constructing a compelling narrative and effective presentation using collected data often falls completely to the analyst, and many feel their creative skills fall short.

To bridge this gap, data analysts should lean on many of the effective techniques that storytellers have used to engage audiences and communicate information for centuries. Read on to learn more about these tools and the best practices for applying them in a presentation of data.

4 Storytelling Techniques Data Analysts Should Borrow

1. Structure your story in phases.

Most narratives follow a common format in terms of structure. The arc that has become the backbone for a typical story is known as “Freytag’s Pyramid,” a structure established by German novelist Gustav Freytag and based in his analysis of dramas in the nineteenth century. Through his work, Freytag discovered that most stories have plot points that can be outlined into seven phases.

The Seven Phases of ‘Freytag’s Pyramid’

1. Exposition
2. Inciting Incident
3. Complication
4. Climax
5. Reversal
6. Resolution
7. Denouement

While some of these phases may be more relevant than others in telling a story with data, being able to identify the elements of the plot that best resonate with an audience is helpful in understanding how to construct a presentation of that data that people can invest in. For example, having some background information (Exposition) when identifying findings is a key tactic in allowing an audience to appreciate how the data fits into the larger scope of a project. Similarly, structuring a presentation with a particularly significant or perhaps surprising reveal at the center of your work (Climax) is more powerful than sharing the most substantial piece of information at the beginning without anything else to ramp up to.

These tactics draw on our innate desire as humans to engage with information presented in a narrative format, but it is also important to approach each situation on a case-by-case basis. If you know that the team has already been briefed on the background of your findings prior to your presentation, for example, choose to spend your time exploring what your findings mean for the future (Denouement) instead. Similarly, if you know the group you’re presenting to appreciates facts that are quick and to-the-point, focus your energy on creating a condensed arc that still outlines the data effectively but does so in a concise manner. No matter which aspects of the typical storytelling structure you may choose to focus on, your presentation will benefit from the use of these elements.

2. Create context.

A powerful story is one which allows readers to feel like they are there, living the situation being described themselves. Novels, plays, and similar narratives spend a lot of time creating this type of detailed context within the world of a story in an effort to reach audiences on a deeper, more intimate level.

When it comes to presenting data, the act of developing context comes down to creating a rich framework in which your findings can be best understood. To do this, data analysts should ask themselves who their audience is and what they need to know—including any relevant background information, industry trends, or other details—in order to establish a backdrop against which your data will be best understood.

Real-World Example

If we look at the way different groups may choose to present data on the amount of time children spend looking at screens, for example, we can see how the context the information is placed in can change the narrative. In Time’s article titled “Too Much Screen Time Can Have Lasting Consequences For Young Children’s Brains,” the data is presented in the negative context that the high number of hours reportedly spent on screens can impact a child’s development. In CNN’s article titled “Screen Time For Kids Under 2 More Than Doubles, Study Finds,” the same type of data is used but it is presented in a more objective and fact-driven context. Both approaches are viable, but this dual-use of the same data should exemplify that even something as simple as a title can change the entire story. For this reason, data analysts should take the time to develop the proper context around the data being presented.

Following through with this step in preparing a presentation will also provide insight into how data relates to larger trends, conversations, or goals of the organization to which you are presenting. As these questions are likely at the core of the audience’s investment in the research, addressing them as the framework for your findings has the potential to increase the data’s impact.

3. Stick to a linear timeline.

Although there have been some very well-received tales told through flashbacks or timelines that jump between past, present, and future, the most commonly successful narratives are ones that are told in a clear beginning, middle, and end order. This is because research shows our brains prefer linear storytelling, and data analysts should work to explain their findings in a similarly linear format.

It can be tempting to start a data analysis presentation off by explaining your findings, especially after spending so much time working through unstructured information in order to draw such conclusions. However, diving into a list of facts and figures without the proper background information can be alienating to those who don’t have the necessary context to see its value.

Instead, take some time to lay the groundwork of your ‘story’ by reviewing the problem the organization was trying to solve with this data analysis. Remind your audience of why you’re all here, what they’ll be hearing about, and why it matters.

Then, develop a solid “middle” section of your presentation. This should include a detailed description of what your findings were and how that relates to the original problem, using details, descriptors, and examples to flesh out the information.

End your presentation with an explanation of what those findings mean for the future. Where data analysts place the most value in raw data itself, it’s actually these conclusions and the analysis of what the data means for the future of an organization that is most beneficial to audiences in these situations.

4. Make it relatable by drawing on emotion.

It’s easy to think of data and statistics as the opposite of personal. After all, in business, we’ve often been taught to approach situations and arguments with facts and figures rather than emotions or feelings—a concept that is outdated, according to most research.

However, if the work of writers of the past has taught us anything, it’s that audiences are much more engaged when they can relate to the material on a personal level. The Harvard Business Review’s studies of neurobiology and storytelling discovered that “character-driven stories with emotional content result in a better understanding of the key points a speaker wishes to make.”

This study also uncovered that, scientifically, presenters make the biggest impact when they can get an audience to emotionally invest in a single, focused concept. Successful presenters should have the ability to create that emotional connection with their listeners in less than a minute, as well.

With data, the best way to create this quick, concept-driven emotional connection with an audience is to relate the data you are presenting to a personal story. If you don’t have one that relates to the topic, try developing a fictional persona of the type of person who may be impacted by the information you have uncovered.

Although this strategy may be less effective with more strictly number-driven topics, because humans relate better to other humans than they to do numbers alone, finding a way for audiences to connect to the material emotionally can have very positive results. Most notably, this connection allows audiences to actually recall the information presented with an emotional context for far longer than that which is presented without it.

5. Know your audience.

Another useful tactic for helping translate cold, hard data into something that your audience can invest in is to think through how this data relates to your particular audience. To do this, it is vital that you know who your audience is comprised of.

If you’re presenting on the buying trends for millennials, for instance, you will need to frame your data in a very different way if your audience is comprised of 20- or 30-year-olds than you would if it was made up of older generations. Similarly, if you are reporting on the employment rates of college graduates, you would report the data very differently to a university than you would to a career recruitment organization. Taking the time to relate to your audience in this way will go a long way in making your presentation impactful.

Data Analytics at Northeastern

Those who teach as part of Northeastern University’s Master of Professional Studies in Analytics program understand that the work of an analyst extends beyond the database. In order to prepare students to succeed in this data-driven world, Northeastern has developed experimental classes and a capstone within their graduate analytics programs in which students are able to work with organizations and apply their analytic, presentational, and data-storytelling skills to real-world projects. This type of experiential learning is something Northeastern is known for and allows students to “get their toes in the water with data,” Goulding says.

Depending on where incoming students are in their career and the technical skills they possess, Northeastern also uniquely offers multiple program types as well as “a couple of different ways you can enter [the] analytics program,” Goulding says. “You can come without much background in math, statistics, or analytics in your work experience… [or] we have another avenue for students who do have more technical roots.” These programs are the Align Master of Science in Computer Science and Master of Professional Studies in Analytics, respectively.

No matter which program students choose, they will graduate with a well-rounded understanding of many of the necessary aspects of data analysis and how it relates to the way data is used in the world today.

“Big data is telling stories now that we couldn’t tell about ourselves and our world twenty years ago,” Goulding says. “And data analysts [are the ones who] have the capability…to tell those stories.”

Visit our program pages for more on the Master of Professional Studies in Analytics programs offered at Northeastern, or browse through our data analytics e-book, which offers industry advice and guidance for breaking into the field and advancing your data career.