8 steps to improve data visualization literacy | 7wData

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Organizations must quickly make sense of an enormous amount of information for business analysis, but data visualization literacy techniques help improve the speed and efficiency of these data-based decisions.

Data visualization literacy encompasses a variety of skills for expressing oneself clearly and knowing how to read and understand the meaning of visualizations effectively.

"Data visualization literacy is no longer the sole responsibility of data teams," said Deepti Srivastava, head of product at Observable, a data collaboration company. "Basic data and data viz literacy have become an essential part of most job functions."

Enterprises need to take an organizational approach to cultivate data visualization competency across the Organization to drive better business outcomes. This starts with setting up best practices and standards for data workflows so teams have transparency into how the insights are created, what data sources are used, and what analytics methods and tools are used, Srivastava said.

"Everyone in the org should be able to not only see a data viz, but also be able to trace how that insight was reached, be able to interact with it to get a deeper understanding of it," she said.

Data visualization competency can also help employees learn to identify the difference between glossy visualizations and the data's actual business value.

"Even if one can choose the right chart and showcase the data in a meaningful way, how can we empower people to take it one step further and derive the right next steps based on the data presented?" said Sean Zinsmeister, senior vice president of product marketing at ThoughtSpot, a business intelligence and big data analytics platform provider.

Once a project arrives at the visualization step, somebody has already told a portion of the story. That is why understanding the use case is crucial to data visualization literacy, as the point of data visualization is to arrive at answers quickly. Data literacy and data visualization can be seen as complementary disciplines that require an understanding of where the information came from, why it is collected and how it is used, said Michael Schwarz, senior vice president of professional services at Resultant, a technology, data analytics and digital transformation consultancy.  Data visualization skills help answer questions from a given set faster by using visuals to communicate the analysis, which allows individuals and teams to build a cohesive narrative across different but related data to drive better data-based decisions faster. Data visualization literacy can help verify that the story being told visually is also being told accurately. Data visualization literacy usually refers to the two complementary skills of data presentation and data exploration, said Rosaria Silipo, Ph.D., head of data science evangelism at data science software provider KNIME. Data presentation skills help visualize results where KPIs or other meaningful metrics produce a summary of company data. Data exploration skills help explore unknown data visually to understand statistics and the correlation.

Data literacy involves understanding the broader field of practices around data collection, storage and how data can help drive decisions. Data visualization literacy is understanding how to make more effective charts. Competency involves understanding the strengths and weaknesses of each chart type and how to format and adorn them. Consumers of charts also need data visualization literacy to interpret charts correctly and judge their authenticity. Data visualization skills help answer questions from a given set faster by using visuals to communicate the analysis. They enable people to view visuals such as charts, graphs, dashboards or animated graphics and understand the information quickly. This allows individuals and teams to build a cohesive narrative across different but related data to drive better data-based decisions faster.