We all know by now that data visualization can offer a tremendous opportunity to reach insights from data. It’s been called one of the most significant technologies of the 21st century (although visualizing data itself is a form of analysis that can be traced a long ways back in the visual communication lineage). This is because a well-designed, meaningful visualization leverages our intrinsic hard wiring to understand complex information visually.

Used correctly, data visualization can also deliver immediate, actionable, and aesthetically intriguing insights at a glance. This is a good thing, especially since the increasing self-service capabilities and functionality available in intuitive tools today are giving more business-oriented people the ability to independently create and share their personal visual discoveries.

Of course, like anything, data visualization – especially self-service data visualization – comes with its own set of obstacles. Last year, while presenting a Data Discovery seminar in Rome, an attendee brought up the question: “We've learned how to govern the discovery "process" better now, but how do we govern the "outputs" of that discovery process? Specifically: What do I do with all the data visualizations floating around my organization. How do I know that they’re accurate and effectively representing data appropriately?”

It was a worthwhile question, and he was right. Data visualizations may often be perceived as a climactic visual output of an insight, but they’re by no means fail proof. Poorly designed data representations can distort the message of the data, fail to guide the audience toward meaningful conclusions, or lose the attention of the stakeholder altogether. This creates risk for the business: incorrect or inadequate visual communication can forfeit opportunities to take action on a new insight or – even worse – cause the business to take action in the wrong direction. Therefore, the business must find a way to manage the risks of self-service data visualization, while still capitalizing on its potential to allow a broader, more diverse group of analysts to reach interactive insights at the speed of thought and communicate new insights and discoveries directly back to the business.

Successful data visualization requires using the right kind of graphicacy to correctly interpret and analyze the data, as well as employing the right combination of design principles to curate a meaningful story.

However, guarding that visualization process isn’t one I consider to be a question of governance. Ultimately, we need to be able to construct a way to insulate the business from the risk of self-service data visualization with the essence of governed data visualization, but without restricting or otherwise limiting the inherently creative process that drives individual data visualization activities.

Over the course of the past several months, I have had the opportunity to research within a handful of leading companies and Insights as a Service provider GoodData to introduce the role of the Data Visualization Competency Center.

I consider The Data Visualization Competency Center (DVCC) as an extension of the Business Intelligence Competency Center (BICC). This is because, similar to how the BICC is a cross-functional organizational team with defined tasks, roles, responsibilities, and processes designed to support the use of BI across the organization, the DVCC should also likewise focus on how data visualization is designed, created, and leveraged. And, like the BICC, the knowledge held in the DVCC should be balanced with how it is embedded into the business to avoid the “ivory tower” effect that may cause the business to reject a competency center.

As an extension of the BICC, the DVCC will support the use of effective self-service data visualization. In the recently released report, I outline how the DVCC has three core competencies that will enable it to providing best practices, standards, and education on how these information assets should be designed, created, and leveraged in the business.

End-user clients that participated in the research are gravitating to the concept of the DVCC. One company, quoted in the report, noted “The DVCC is a concept that completely resonates with what we see in the market and in our approach to making self-service data visualization core information assets – we are effectively becoming a DVCC." Another said, “We [now] need a framework to guide and support the users to use the datasets, features and tools the right way. This is where the Data Visualization Competency Center can help.”

What about within your company?

If you’d like to read the full report, please visit here or join us on July 16 at 10am PT for a related webinar.

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