Storytelling has been an integral part of human culture since ancient times, predating even the invention of writing. It is a pivotal aspect of our social and cultural interactions, involving narrative sharing through improvisation, theatrics, or embellishment. This practice is deeply ingrained in every culture and serves various purposes, including entertainment, education, preserving cultural heritage, and imparting moral values. Each culture has unique stories that have been passed down from generation to generation. The origins of storytelling can be traced back to the Cro-Magnon era when humans utilized it to document their daily experiences. Despite originating during the age of cave paintings, the psychological impact of storytelling remains relevant to this day, after thousands of years.
Data is an indispensable collection of discrete values that provide vital information on quantity, quality, facts, statistics, and other fundamental units of meaning. It is represented as numbers or characters and is commonly gathered through measurement, observation, query, or analysis. It is the smallest unit of information that must be used for calculation, reasoning, or discussion, ranging from abstract ideas to concrete measurements. When thematically connected, data presented in context must be considered information, while contextually related pieces of information must be described as data insights or intelligence.
With the advancement of computing technologies, Big Data has emerged, typically involving vast amounts of data at the petabyte scale. Analysing and working with such large datasets using traditional methods can be challenging. In theory, infinite data generates infinite information, making it difficult to extract insights or intelligence. Nevertheless, Data Science has emerged, utilizing Machine Learning and other Artificial Intelligence methods to apply analytic techniques to Big Data efficiently.
What is data storytelling, and how is it different from traditional storytelling?
The idea of data storytelling involves creating a captivating story line using complex Data and Analytics to convey a message and influence a specific audience. The process is akin to human narration but has the advantage of presenting in-depth analysis and factual evidence using graphs and charts. The information is presented in a simplified manner to engage the audience, enabling them to make critical decisions with greater confidence and efficiency. When communication is effective, it allows for insights to be perceived and remembered by the audience.
The phrase “Data is the new oil” has led to significant changes in the business world. We have seen organizations switch up their models; new companies emerge based on data platforms, and the popularity of data products that have become a regular part of our daily routines. It is crucial to convey our perspectives clearly and efficiently to create significant change. The advancement of digital technology and the importance of data-driven decision-making have made data storytelling a crucial skill, particularly in Data Science and Business Analytics. The aim is to narrow the divide between complex data analysis and decision-makers who may require the ability to comprehend the data. By utilizing deliberate techniques in crafting data stories, the Chief Data Officer/Chief Analytics Officer and their teams can effectively increase business value and enhance their influence and impact on stakeholders.
Creating a Data Story
Creating a narrative based on data that leads to informed decisions and actions can be a highly effective tool. There are several benefits of effectively communicating data through storytelling, including:
- This tool assists in generating valuable insights.
- This tool helps simplify complicated information and emphasizes the essential details for the intended audience.
- Enhancing credibility by establishing oneself as an industry and thought leader.
- Storytelling can be a valuable tool to effectively handle conflicts, address issues, and overcome challenges. Narrative discourse can serve as a useful approach to resolving conflicts when direct action may not be feasible or recommended.
- During a group discussion, a collective storytelling process can effectively inspire and unite group members by connecting past experiences to future goals. This approach can be used to transform problems, requests, and issues into compelling narratives that engage and motivate the team.
- The art of storytelling holds significant value in decision-making and persuasion. In corporate settings, managers and officials often rely on storytelling rather than abstract arguments or statistical data.
To effectively tell a story with data, there are three crucial components.
- Data: To gain a comprehensive understanding, utilizing Descriptive, Diagnostic, Predictive, and Prescriptive Analysis methods is crucial for data analysis.
- Narrative: While sharing insights from data, it is essential to use a plain narrative, whether spoken or written. This narrative should provide context, recommendations, and persuasive reasons for those recommendations to communicate with the audience effectively.
- Visualizations: To effectively convey the story behind the data, visual aids such as charts, graphs, diagrams, pictures, or videos can be incredibly helpful in making it clear and memorable.
We can utilize data storytelling as an internal tool to convey the importance of enhancing our products according to user data. Our products can be presented convincingly, encouraging potential customers to purchase them. It is crucial to consider the intended audience and their comprehension level when crafting a story to ensure effective communication. Selecting appropriate visuals is vital for successful storytelling. Audiences prefer concise and clear messages over complex stories with heavy jargon and complex language. In addition, the purpose of storytelling is to present information in a way that is easily comprehensible for the audience. Properly organizing the flow of the data story is crucial.
Preparation of Narrative
While preparing the narrative, it is essential to take note of the following points:
- Setting: The details provided contain all the necessary information to understand the situation, including the decision, problem, questions, and data. The document includes the organization’s goals, mission, targets, values, and analytical framework related to the inquiry & data.
- Characters: The individuals involved in the data can range from customers to stakeholders and other vital players affiliated with the organization.
- Context: Context refers to the specific point we are trying to convey. It often arises from a question, such as why customers are churning, which is the foundation for our hypothesis and the data we gather to answer the question.
- Conflict: The presented data may impact the characters or setting through issues or their effects. It is important to note that conflicts are only sometimes necessary in data stories.
- Resolution: Proposing a solution to a visible problem or aiding in decision-making is helpful.
Data Visualization to enhance Data Storytelling
The use of data visualization is crucial in facilitating the audience’s ability to comprehend complex information. It simplifies vast amounts of data into easily digestible forms, aided by compelling visuals that support our narrative. Given the decreasing attention span of humans, data visualization is especially useful in conveying essential information. The human brain is better equipped to extract information from images than textual representations of the same data. As such, AI/Data practitioners should be able to communicate their findings in multiple formats. Utilizing data visualizations can be beneficial in aiding comprehension and analysis:
- Discover unbiased insights by identifying patterns, trends, and key findings.
- Provide context, analyse the outcome, and express the insights.
- Present information clearly and concisely, making it easy for the audience to comprehend. Improve audience engagement.
Creating Dynamic Visualization of Stories
Finance analysts and decision-makers are currently facing an overwhelming amount of complex data. According to Gartner, by 2025, the most prevalent method of consuming analytics will be through Data Stories. 75% of these stories are expected to be automatically generated using Augmented Analytics techniques. Organizations will increasingly incorporate dynamic storytelling tools powered by Artificial Intelligence and Machine Learning into their traditional dashboards to keep up with this trend. These dynamic data stories will provide insights in the form of Narratives, highlighting the most meaningful business changes for each user and providing root causes, predictions, and prescriptions tailored to their roles and contexts. This approach dramatically reduces the risk of misinterpreting financial analysis, creating contextualized Narratives that drive data-driven decision-making. Automated data storytelling will also save time by reducing the need for manual authoring and reconfiguring of dashboards while still allowing users to generate personalized data stories automatically.
When presenting decision-ready data, it is essential to use a classic three-act narrative structure and story arc. How the information is conveyed can significantly affect how it is perceived, comprehended, and necessary action is initiated.
Key components of the story arc
Identifying a fascinating pattern or trend through the available data can open doors to new opportunities and benefits. For example, a Data Scientist on the team creates a data model that surfaces a previously unidentified sub segment of highly profitable customers at risk of churning. This finding is the stimulus that prompts us to recognize that significant changes are required in the sales strategy and marketing campaigns that have been running unchanged for a period.
To effectively manage a business scenario, it is crucial to measure progress. Continual measurement is necessary to gauge any impact that may be occurring. The Data Scientist utilizes a churn analysis model to evaluate the effectiveness of changes to the sales plan and marketing campaign.
The data gathered during the final measurement stage offers valuable insight and feedback that can be used to improve future strategies. Depending on the feedback & insights gained from the churn model, adjustments and refinements can be made to the business plan to optimize the approach further. This creates a positive cycle of constant improvement.
Robinhood Recap: An Example of a Successful Data Story
Robinhood Financial is an online brokerage service enabling investors to trade stocks and securities without paying commissions. In late 2020, the company launched a unique personalized experience for its users to revisit their investing journey, complete with their most significant trades, key investing moments, and other market milestones. This unique feature has helped users understand their activity and encouraged them to look closely at their investment strategies. The data-driven customer experience highlights the users’ earned interest, dividends, and trade returns and reinforces moments of explicit value. Additionally, it promotes desired user behaviours. Robinhood is eager to drive brand awareness and new client acquisition through referrals and app adoption, ensuring brand loyalty.
Dynamic data storytelling in the Indian Banking sector
Some examples of using interactive data visualization are:
- Visualizing loan growth: It is helpful to see how loans have grown over time across various categories like personal, home, and business loans to understand the Bank’s loan offerings better and make informed decisions.
- Analysing credit risk: Credit risk analysis across various regions and industries is crucial. This information aids the Bank in effectively managing its loan portfolio and making well-informed lending decisions.
- Exploring customer behaviour: One way for the Bank to gain insights into customer behaviour is by analysing their account activity, including transaction frequency and type. This data can enable the Bank to identify patterns and trends, which can then be used to create targeted marketing campaigns.
- Mapping branch locations: To assist customers in locating nearby branches and provide insight into the services offered, the Bank can implement a feature that displays the locations of its branches throughout the country.
The use of data-driven storytelling has the power to significantly transform how we consume and analyse data, and revolutionize the field of Analytics. Interpreting and explaining data makes Business Intelligence accessible to all users, not just those with data analysis training. In the future, new data tools can provide data stories. Additionally, combining data storytelling with Artificial Intelligence predictions can lead to accurate predictions without extensive configuration. Critical actions for the Financial Sector are evaluating vendors with augmented user experiences and implementing or enhancing data literacy programs.