The principles of effective data storytelling

Much has been said about the proliferation of data and how this has led to various emerging specialisms, such as data strategy, data engineering, data management, automation and data sciences. Each skill has a vital role in the emerging paradigm of data-driven decision-making. One of these skills is data storytelling - the ability to construct a compelling narrative backed by data and enabled by data sciences. As a result, a data storyteller is increasingly becoming a critical team member - to make sure what the data can deliver is relatable, relevant and human.

Why is data storytelling important? Decision-makers in business, as in many other sectors, are conscious of the power of being informed by relevant and current data. However, given recent advancements in data techniques and the widespread use of terms like artificial intelligence and machine learning, it is hard for business leaders to fully comprehend the possibilities and nuances of the data and processes they should be using. This is the gap that the data storyteller fills - with an appreciation of the sector and business processes and a deep understanding of the data sources and techniques that can be applied. In other words, the data storyteller tells the findings from data in plain speak, unencumbered by technical jargon that comes with specialisms.

And what is the difference between insights and storytelling? Business analytics and insights, in their very nature, are confirmatory. Analysts sift through data, find ways to validate hypotheses and predict a set of finite possible outcomes. On the other hand, data storytelling looks at a more blue-sky approach to business challenges - exploring open-ended problems, which may not have historical precedent, and crafting data approaches to track and measure going forward. In other words, data storytelling focuses on the story arc to solve the business challenge and unlock growth and then enables the development of data systems to craft the story.

Data storytelling focuses on a straightforward narrative - a vehicle that strings together the data, visuals, insights and models to connect with the audience - the decision-makers for business. Thus, the storyteller needs to focus on a few fundamental principles in the story:

• Meeting the business where it is. Business decisions, and therefore the data systems that enable business, need to evolve. The data narrative needs to consider where the company is now - its access to data sources, ability to invest in data resources, competitive pressures on the business and capacity to cascade change within the system. Companies resonate with data approaches that feel achievable and address their needs rather than something that feels too futuristic.

• Putting needs before wants. As a data practitioner, it is tempting to implement the latest techniques and methods for the business. While innovation is a great objective to drive, the narrative must cover the immediate burning issues in the company. Innovation tends to be risky, and while it lends an edge to the narrative, the narrative must be grounded in the day-to-day business.

• Ensuring data safety. As with any other asset, it is imperative to ensure the safety of the data in every aspect. This includes compliance with privacy laws and practices and making sure that relevant and essential data is tagged and archived for future reference if needed.

• Showing empathy. The data narrative needs to be empathetic to the business landscape and the challenges faced by the business. For instance, any change in approach to data collation or implementation would not be feasible during a seasonal peak for the company. Instead, the process needs to be paced and scheduled to ramp up in time for mountains and for teams to be able to use the system effectively.

• Driving constant learning. The cliché ‘the only thing constant changes’ is also actual for the data narrative. Therefore, any data story needs an inbuilt aspect of learning from the environment and agility to adapt based on this learning.

• Building the ‘good-enough’ model. As an analyst, the pursuit of accuracy and predictive power is always on. Thus, the analyst always looks for additional data, data sources and advanced data techniques. However, it is critical that the analysis is robust to achieve the milestones needed for supporting business cycles. Therefore, the data framework and research must be ‘good enough’ to enable decision-making alongside the continued push for precision and learning.

The data storyteller is, perhaps, the most important partner to the business in the roadmap for data transformation, steering and commandeering the journey. This makes it imperative that the storyteller anchors the data narrative in the nuances of the business challenges and then adds data inputs and specialists in service of the decisions to be made - ensuring that the above core principles are adhered to.

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Aarti Bharadwaj is the senior vice president of analytics for APAC at EssenceMediacom