Q7 - Generating Engagement

Q7 - What is necessary for a data-driven story to be able to generate engagement with the audience?

Answer to the question:

Stories improve understanding by helping us associate information and engage interest more than isolated figures and facts.

Various techniques are used to tell stories in different segments such as cinema, theater, and literature to obtain engagement.

These techniques can be used for data narratives, but we suggest others that are considered important.

Some ways to generate audience engagement through data stories are:

1. Orient the data stories to a central point

2. Be selective with the data

3. Promote empathy and emotional connection

4. Help the interlocutor gain insights from the data

5. Be cautious with bias and false narratives

6. Relate the story to a person

7. Find the compelling narrative based on data analysis

Let’s describe them below.

1. Orient the data stories to a central point

With the flood of data that comes to us every day, data stories help convey significant aspects of the data clearly and succinctly.

Data analysis is oriented towards specialist users and requires those interested to be data literate.

Data stories, on the other hand, promote data discoveries to ordinary people or a specific target audience, whether data literate or not.

Data narratives should direct the audience’s attention to a central point or main message that brings the good news about the knowledge generated from the data.

The central point is the focus of what needs to be shown to make the interlocutor interested in the data story.

2. Be selective with the data

Today, people's time and attention are scarce, so the story must be clear, concise, and precise to attract interest.

Be highly selective in your choices about what to include and what to leave out of your data stories.

Do not include winding and insignificant details that generate doubts.

Be careful with the provenance of the data, where it came from, whether it is valid or not, whether it can cause errors in data analysis, and the result becomes a false narrative.

Talk about the data in the least technical and clearest possible way, using only what is necessary, useful, and relevant.

3. Promote empathy and emotional connection

Most data stories present facts and numbers about data analysis, and this does not bring an emotional connection with the interlocutor.

It is possible to establish emotional connections with data stories by relating data to people's lives.

Try to promote some form of emotional connection between the data story and the interlocutor.

It is the best way for this reader to be interested in and connect with the data story.

4. Help the interlocutor gain insights from the data

Few forms of communication are as persuasive as a compelling narrative.

Data narratives are based on discoveries that show the meaning of the data, transforming it into knowledge.

It is necessary to help the interlocutor understand the discoveries that this data offers so that he is interested and participates in the story, and for this, we can resort to a graphical visualization of the data.

A graphical tool is infographics, which summarize and represent information in an attractive visual format with elements of images, graphics, icons, texts, and diagrams.

Another is dashboards or interactive digital panels that allow simulations and interaction with the data, enabling the interlocutor to gain his own insights from the data.

5. Be cautious with bias and false narratives

Cognitive biases are tendencies to think and judge based on our own perspective, experience, knowledge, and filters.

Data stories are where truth or fiction can be brought to life and disseminated to others.

As a data storyteller, you have power and responsibility, because the way you select, organize, and present facts can change the way people see the world.

In the process of assembling data stories, it is necessary to be careful to avoid creating false narratives and you need to check your own cognitive biases and consider potential biases in your audience.

Often, even when striving for data stories to reflect reality, it is possible to make unintentional mistakes.

A big problem in data narratives is that they are told from data collected from various places and in large quantities, and if this data is not reliable, it can generate false narratives.

Common situations for lack of data veracity are inaccurate sources, software errors, statistical biases, lack of security, unreliable sources, falsifications, uncertainties, lack of updates, human errors, inaccuracies, among others.

Even if data analysis has made significant discoveries about the data, the lack of veracity will generate false narratives.

Therefore, for any data story you create, make sure to consider other possibilities, such as validating the data provenance, validating ideas opposite to yours, validating stories outside the target audience.

6. Relate the story to a person

Data stories are mostly told from data visualization, an area of computer graphics.

Designers and specialists in graphical environments who produce content, develop software, and graphical ways to present information via computer work in this area.

This area has a great impact on Big Data as it is the basis of Stage 5 when discoveries about large volumes of data are presented in a graphical and visual way to be easily understood.

There are several techniques to increase the emotional quality of a visualization, ranging from structuring the story, choosing colors, words, using interesting graphics, and other design elements.

The smartest way to relate the narrative to the interlocutor is to find the story of a person related to the data to exemplify the narrative instead of starting with general facts and figures.

Make the interlocutor identify with something within the story so that your effort is not in vain.

An example

Which is better?

You say that mortgage foreclosures increased by 20% in Portugal this year, or say that Felipa is worried about losing her house?

In this case, it is better to tell the data story through the perspective of Felipa, our character.

In this data narrative, there could be simple graphics showing Felipa's losses and gains, and in the end, conclude that it is possible to resolve this because she managed to finance the debts and resumed her life.

From the moment the human element entered the data story, it was possible to build a narrative that attracted different audiences, both those in Felipa's situation and those moved by the character's situation.

That is, the data story took on a life of its own.

7. Find compelling narratives based on data analysis

Most insights about what the data wants to tell us almost always happen in Stage 3 of Big Data when specialists begin exploring the data called EDA (Exploratory Data Analysis).

In EDA, analysts or data scientists start playing with the data, comparing, relating, establishing connections, trying to answer previously elaborated questions or new ones that arise.

It is the process of discovering what the data wants to tell us.

The data narrative should explore the final results of the EDA that have been proven to produce an impact on the interlocutors.

It is difficult to find compelling narratives to explain the discoveries in EDA because they are very technical and scientific, but it is always possible to find a hook, impetus, and purpose to awaken and captivate the interlocutor on the subject.