Beyond Analytics: Telling Stories With Data

• Managers will need soft skills such as communication and leadership to turn massive amounts of raw information into compelling stories that drive strategic decisions.
• Students can hone their data storytelling skills by completing projects involving real-world companies and participating in competitions where they must solve problems through data.
• Students should be wary about narratives created by GenAI, because these narratives sometimes lack transparency and therefore are unsuitable for certain situations and industries.

 
We live and work in an era of overwhelming data abundance, and the digital universe is expanding exponentially. Our daily activities both generate and rely upon trillions of data bytes flowing through social media, the internet, devices connected to the Internet of Things, and now generative artificial intelligence (GenAI).

Experts estimate that the data generated by machines and humans in 2024 will exceed 140 zettabytes. That’s 10²¹ bytes or 1 trillion gigabytes—an unfathomable amount. This data explosion has transformed data into the “new gold” of the 21st century.

However, the true “gold” of data lies not in its quantity, but in its potential to help us make sense of the present and anticipate the future. Today’s managers cannot be content to simply clean, manage, govern, and analyze data. They must be able to extract, interpret, and effectively communicate the insights they extract from data. Their task is to turn raw information into a compelling narrative that drives understanding and informs strategic decision-making.

How can business schools ensure that our students go beyond being data analysts to become translators who can communicate their findings in ways that lead to impactful change? We must teach them how to tell stories with data. To do that, we must provide opportunities for them to develop the soft skills that enable them to inspire and motivate others.

The Importance of Soft Skills

For decades, MBA, masters, and undergraduate programs have emphasized soft skills such as communication, collaboration, problem-solving, adaptability, influence, and leadership. Until recently, technical fields have tended to overlook the value of these competencies. However, that is changing as more and more hard skills are enhanced or partially replaced by automation and the capabilities of AI.

Today, managers know they must be able to pair technical expertise with the ability to motivate employees and communicate with stakeholders. Graduates who possess both kinds of skills will be successful across roles, industries, and career paths.

Because soft skills are honed through experiences and self-reflection, students need to practice these skills in real-world situations. Thus, business schools should provide students with rich and diverse opportunities to work in teams, learn about other cultures, and collaboratively dive into difficult open-ended problem-solving activities.

Managers must be able to pair technical expertise with the ability to motivate employees and communicate with stakeholders.

We are working to merge soft skills with technical skills in the curriculum at EDHEC Business School in France, where I am a professor and the head of the school’s AI initiative. For example, students completing their capstone projects in finance present to an audience of coaches who are not necessarily well-versed in the intricacies of the data models used in the presentations. Students are forced to zoom out from the analytical details and reflect on these questions: What problem are we addressing? In lay terms, how do we address it, with what data, and why? What conclusions can we draw? How will our insights impact practice?

When students simultaneously develop hard and soft skills, they can bridge the gap between finding data-driven insights and implementing actions based on those insights. They can ensure that their findings are translated into real-life initiatives that can improve businesses and governments. And they can do this most easily when they understand how to use data to tell stories.

The Significance of Storytelling

Storytelling is a trait that is specific to human development and culture. Stories create emotional connections, foster understanding, and lead to greater cognitive and social development. While today’s GenAI is capable, at some level, of creating stories, human storytelling will always remain a powerful component of culture, and data storytelling will be crucial for leading businesses and governments.

Well-crafted narratives help people make sense of overwhelming amounts of information. At its core, data storytelling is about asking good questions, displaying curiosity, developing critical thinking, and guiding an audience to interact with data at a high level. One way that students can develop their storytelling skills is by participating in competitions in which they solve problems with data and then present solutions that can be grasped by nontechnical audiences.

At EDHEC Business School, our DataViz competition, which we run in partnership with Tableau and UNICEF, is open to students at schools across Europe. Participants choose the stories they wish to tell about the ways UNICEF helps children around the world face specific challenges. Students choose which datasets to use and develop dashboards designed to help the public better understand UNICEF’s work.

Another competition is the Global Business Analytics Challenge (GBAC) offered through the Quantitative Techniques for Economics and Management Masters (QTEM) network. (EDHEC is a member of the network, and I serve on its board.) QTEM brings together students, academic partners, and international corporations to help students develop analytical and quantitative skills, as well as soft skills.

For GBAC, students develop a data analytics solution and narrative based on a corporate partner’s real dataset and business issue. In recent years, students have designed solutions for companies in the banking, luxury, and nongovernmental sectors.

At its core, data storytelling is about asking good questions, displaying curiosity, developing critical thinking, and guiding an audience to interact with data at a high level.

In the early years of GBAC, students over-focused on creating models and offering analyses; they spent insufficient effort on telling stories and engaging the audience. Current students don’t just take courses on programming and data science; they also attend modules that develop their skills in public speaking, multicultural teamwork, presentation skills, communication, and pitching. Today’s winning teams combine rigorous data analysis with the art of storytelling to make complex data more understandable, engaging, and actionable for their clients.

Two years ago, the winning QTEM team—which included students from France, Portugal, and Germany—worked with data supplied by the British Red Cross. The organization wanted ideas on how to maximize its impact in providing emotional and psychological support to vulnerable groups, such as people with mental health issues.

The team opened its presentation with an emotional reminder of Queen Elizabeth’s passing and linked her death to statistics indicating that 40 percent of adults in the U.K. have experienced feelings of loneliness. After this impactful and sensitive opening, the students presented an analysis of the needs of different vulnerable populations and offered recommendations for identifying and assisting at-risk groups. Their proposed “Lilibet Aid Package” not only made an obvious reference to the queen but also focused on loneliness, anxiety, and positivity.

The International Element

An essential component of GBAC is that each three-person team is made up of students from different schools and countries. They work remotely and meet in person for the first time when they gather to present their findings.

This format enhances students’ soft skills because it provides them with opportunities to manage projects across disparate, multicultural, and remote teams. That’s an ability that has been in particularly high demand among employers since increasing numbers of employees now work from home in the wake of COVID-19.

Students also build their soft skills through immersive international experiences. When students live and study in other countries, they not only develop a greater awareness of other cultures, but they also engage in intense reflection on their own backgrounds and biases. This leads them to develop resilience, adaptability, and global perspectives—all of which are crucial in today’s interconnected world.

The Impact of GenAI

Storytelling entered a new era when ChatGPT became publicly available in the fall of 2022, and business professors quickly began looking for ways to incorporate it into their teaching and research activities. For instance, many have experimented with generating content in styles ranging from Shakespeare to Kant. Others are using GenAI to create content for audiences at various technical levels or to try to analyze vast datasets to uncover patterns that elude humans.

However, there’s a need for caution. AI’s algorithmic process is frequently referred to as a “black box” because we cannot explicitly explain how the software learns or makes its decisions. This means it creates narratives from models that lack transparency or interpretability. If results cannot be validated, leaders need to be skeptical about using some analyses and stories in their strategic decision-making.

We must integrate principles of ethical AI usage into the curriculum and teach students how to incorporate complex models into the storytelling process.

To address this problem, QTEM instructors train students in a variety of Explainable AI (XAI) methods and examine why decision trees and linear models might sometimes be preferable to complex black box methods. For example, in industries such as finance and healthcare, regulatory compliance calls for transparency in decision-making processes.

At QTEM schools, students gain an understanding of the trade-offs in model performance and levels of transparency. Through quizzes, coding exercises, real-life examples, and hands-on projects, students learn how to work with model decisions, make hypotheses on the available data, and validate these hypotheses against empirical findings. Through presentations, workshops, collaborative projects, and case studies, students develop their ability to convey complex concepts to diverse audiences.

Across higher education, we must integrate principles of ethical AI usage into the curriculum and teach students how to incorporate complex models into the storytelling process. As instructors, when we experiment with these rapidly advancing tools, we must display transparency and hold open dialogues with students if we hope to positively influence their behaviors and mindsets.

The Need for Evolution

The business landscape is continuously evolving, and the skills required to succeed are evolving as well. To navigate a world often described as volatile, uncertain, complex, and ambiguous (VUCA), executives must possess both technical prowess and soft skills.

At business schools, we must take a holistic approach to education. Our goal is to ensure our graduates will have both technical and AI abilities so they can be innovative leaders who are prepared and able to drive meaningful change in their organizations and industries.