Blog

The writer Robert McKee said, "Storytelling is the most powerful way to put ideas into the world today." It's likely that McKee was referring to fiction or nonfiction writers. In fact, when we think about master storytellers, we might conjure up an image of famous writers, like David Sedaris or Ernest Hemingway. Rarely does the face of a data scientist spring to mind.

Yet, storytelling is exactly what experienced data analysts do on a daily basis. When data scientists analyze data and derive insights, they express those insights in a meaningful way to the project's stakeholders by telling a meaningful story. They accomplish this storytelling through the creation of customized presentation layers. 

The development of visualization layers is often referred to as the "last mile" of a data analytics project. This final phase is also the point at which many projects fail. Why would a project suddenly hit its demise during the last mile? Because of a failure to create powerful visualizations that tell the story of a project's insights.

What Do We Mean By Data Visualization?

According to Mashable, "Data visualization is the study of the visual representation of data, meaning 'information that has been abstracted in some schematic form, including attributes or variables for the units of information.'"

Data visualization conveys information through graphical means; it is as much art as it is science. 

"To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key-aspects in a more intuitive way," said Vitaly Friedman (editor-in-chief of Smashing Magazine). "Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose – to communicate information."

The failure stems from a lack of consideration to storytelling.

Why is Data Visualization Important?

Data insights must be presented in a user-focused way that conveys a message. Simply presenting an analytics project's results in a spreadsheet could fail to identify patterns or other insights.

According to TechChange, "Data visualization allows us to quickly interpret the data and adjust different variables to see their effect and technology is increasingly making it easier for us to do so."

Succeeding at Data Analytics with Visualization Layers

Analytics runs massive amounts of data through complex algorithms, deriving results that address a business's questions and goals. For example, a company may be foundering in customer experience and hope analytics will help them discover their weaknesses and determine how to improve customer satisfaction.

Still, a project's results hold little value if data analysts don't tell the story of those insights using dashboards, charts, and visualizations with effective designs. User-friendly visualization layers illustrate those insights through clever designs that are tailored to a project's primary audience of stakeholders. 

Finding an analytics vendor who works closely with clients to understand their goals and user needs, and then creates a customized presentation layer is key to analytics success.

How Solution as a Service Delivers Customized Presentation Layers

An analytics vendor with a Solution as a Service (SolaaS) approach to analytics understands the importance of an artfully displayed frontend that tells the story of a project's insights. Through a single vendor, SolaaS joins a powerful analytics engine with the services of the industry's top data analysts. These analysts devote themselves to working closely with clients to understand their business goals and user needs and deliver a presentation layer which addresses both. SolaaS tells the story of your data through an artfully constructed visualization layer. 

Summary

The art of storytelling isn't merely relevant to fiction and nonfiction writing. Storytelling plays a valuable role in the world of data analytics. When data scientists transform the results of an analytics project into insights, they do so through the artful use of customized presentation layers. A project's frontend is arguably its most critical piece, and should be handled by experienced data scientists like those working for a vendor with a Solution as a Service approach to analytics.