Data storytelling

The volume and range of data now available within organizations is growing exponentially. However, understanding this data, particularly for non-experts can be difficult. Data storytelling aims to bridge this understanding gap by providing context to data and presenting it in ways that users can relate to. This improves understanding, transparency and decision-making.

What is data storytelling?

Data storytelling involves making information easily understandable and compelling by using storytelling techniques. The objective is to bring context to the data to increase transparency and engagement with the data. Data storytelling can be via visualizations, web pages, images, or infographics, for example.

For example, the town of Cary has created a range of data stories around subjects such as waste, real estate prices and the COVID pandemic. Rather than providing datasets in a tabular form which is complex to understand, users can access a page rich in information.

Data storytelling is a type of data visualization (turning data into graphs, dashboards, or maps, etc.). The objective is to transform sometimes complex data into simple and clear information.

How can data stories be used?

Data stories can be used in multiple ways:

• For open data:

Whether a local municipality, public administration or private company, all organizations have a duty to be transparent. The use of data stories allows them to make information understandable by all users, whatever level of data skills they possess. It is therefore the ideal format to communicate on key issues, such as progress towards CSR/sustainability goals, culture, budgets or voting records..

• For internal use:

In addition to making data understandable to everyone, data stories help users make better decisions. Internal decision makers don’t have time or training to study detailed tables with thousands of lines of figures. By presenting the data in engaging, understandable ways employees can quickly understand the objectives to be achieved and take appropriate actions and more informed decisions. Data storytelling allows employees to understand why information is important to them more easily.

• For the creation of data services

Data services aim to provide high value-added information to answer a specific problem, often as an additional service/revenue stream. Delivering this information through data stories enables companies to deliver easily understandable insights to partners or customers.

How do you tell stories with your data?

Every organization can gain value by adopting data storytelling techniques, adapted to the types of data available, their objectives and business context. These best practices are essential if you want to tell relevant stories with your data:

Prepare the data

In today’s world, managing data is becoming increasingly complex due to growth in formats, storage locations and volume.

This growth allows organizations to access rich and complete information. However, it also makes data more difficult to understand – finding an answer can be like looking for a needle in a haystack. It is therefore necessary to process, centralize and standardize data to provide a basis for your data stories.

Create data visualizations

To tell relevant stories, it is necessary to use visualization tools to present your data in different ways: graphs, maps, timelines, key performance indicators, etc. These visualizations should be interactive, rather than static, allowing users to drill-down and customize views through advanced filters.

Add contextual elements

The essence of a data story is to provide context so that users can better understand the underlying data. This context can be written text, images or associated resources for example. The objective is to allow users to understand everything about a specific theme in a very short time.

Share your data stories on the right channels

Finally, data stories must be shared on the right channels so that they can be discovered and used by the greatest possible number of people. This can be via websites, mobile apps, or a dedicated portal. Data stories therefore need to be fully responsive, so that they adapt and interact effectively on all channels and devices.