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The Importance of Data Visualization in Data Discovery


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Successful data-driven organizations are differentiated by the fact that they recognize the need for new tools and technologies that support data visualization, and enable a visual culture of data discovery in its people, processes, and technologies, according to Lindy Ryan, research director for Radiant Advisors' Data Discovery and Visualization practice and a research associate with the Rutgers Discovery Informatics Institute (RDI2).  Ryan recently talked with Big Data Quarterly about her new book, The Visual Imperative, and the business and technology forces shaping the data analytics space today. 

What was your motivation in writing this book?

The primary catalyst behind this book grew from the fact that I saw a huge need to address the concepts of disruption and data discovery, and how these are supported – even encouraged – by the resurgence of data visualization. These are all buzzwords we hear thrown around in the data industry today, and there is a real need to and unify them in a way that supports the business to take informed action. Data visualization is vital to business today, but it is often not utilized to its full potential. That’s why it’s imperative that people understand how to effectively communicate visually.

Who is it targeted at?

The book is written foremost for the “modern data analyst” – users across the organization that, regardless of job title, are (or want to be) working with data in self-service, visual ways to discover insights or who have a problem/question and would like to explore data to find an answer. But, there’s something for everyone – executives building their organization’s data strategy; data scientists or architects putting it into practice; academics interested in learning more. Ultimately, this book is for anyone who wishes to become a leader in transforming the data culture within their organizations and making data accessible and pervasive in all decision making roles enterprise-wide. Data visualization is a universal language, and I wanted the book to reflect that in how the subject matter was addressed.

What are the biggest forces shaping the data landscape today?

Tools, technologies, changes in the workforce demographic – the list is long. However, I tend to think that the biggest force shaping the data landscape today is the data itself. It’s not necessarily because of “big data.” Yes, we have more variety, velocity, and volume of data than ever before, and we’re finding more ways to put use it and getting more people to work with it. But, big or small, data is powerful. It separates leaders from laggards and drives business disruption, transformation, and reinvention. Today’s most progressive companies are using the power of data to propel their industries into new areas of innovation, specialization, and optimization. Further, the ability to “see” insights in data is imperative, and these visual insights are becoming increasingly dominant in information management. Together, this is the driving force to creating a culture of visual data discovery. 

Why is it relevant now?

It’s no secret that the data industry is changing – and fast, almost faster than analysts can keep up with. We’ve called it an evolution, but really it’s more aptly described as a transformation. The horsepower of all of our new tools and technologies provide more opportunities than ever before to harness, integrate, and interact with massive amounts of disparate data for business insights and value – something that will only continue in the era of the Internet of Things and the future in general. And, as a new breed of tech-savvy and digitally native knowledge workers rise to the ranks of data scientist and visual analyst, the needs and demands of the people working with data are changing, too. Now, we’ve got to figure out what to do with all this new information and new expectations on how to work with it. Sometimes – perhaps most times – the best ways to really see and understand data is quite literal: by visualizing it.

How does visual data discovery compare to a traditional data representation?

Traditional data visualization isn’t going away, but the traditional standards are making way for richer, more robust, and more advanced visualizations and new ways of seeing and interacting with data. However, while data visualization is a critical tool to exploring and understanding bigger, more diverse and dynamic data, we can go further. By understanding and embracing our human hardwiring for visual communication and storytelling, and incorporating key design principles, standards, and evolving best practices, we can transform data visualizations to unique visual information assets that offer competitive value.

What do you see ahead as the next wave of innovation in data visualization and data discovery?

I think we’ll see more focus on data visualization as a key component of meaningful visual data storytelling in the organization. The concept of data storytelling is not new, but like so much, it’s changing. After all, what is a good data story, and how do you tell it visually? Separately, with the push towards more data and more self-service whether in discovery, visualization, or any other analytics exercise, more questions on things like governance, privacy, and security, and so on – our “ethical quagmires,” if you will – will emerge. That’s an area of research I personally would like to continue talking about.


The Visual Imperative: Creating a Visual Culture of Data Discovery, published by Morgan Kaufmann, is available on Amazon here


Image courtesy of Shutterstock.


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