8 steps to improve data visualization literacy

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,” he 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.

What is data visualization literacy?

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 visualization literacy vs. data literacy

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.

“Data literacy, on the other hand, is important for understanding the dataset itself and its relevance in the context in which it is created and pulled together,” Srivastava said.

This includes fluency in data sources, how data pipelines are created, different analysis techniques and data transformation techniques. An organization needs competency in these areas to make effective and efficient data-driven decisions to drive the business forward.

Here are eight steps companies can take to promote data visualization literacy across the organization.

1. Adopt a consistent visual language

Enterprises should embrace a consistent application of visual language, said Dan Lawyer, chief product officer at Lucid Software, a visual collaboration tool provider. For example, a particular shape could always communicate a specific concept. This allows for faster understanding and more clarity in communication.

In addition, directly associating the visualization and data can help organize the data into clear and accessible buckets. A proper visual language with direct connections to the data makes it easier to analyze complex webs of data through simplified visuals.

2. Digitized metrics

Digitization and data collection are crucial to cultivating data visualization literacy, Silipo said. The more data is collected and organized, the more teams can visualize and collaborate around data.

One aspect of this step consists of defining KPIs and metrics for consistently measuring processes and events that may not seem easy to quantify. This forms the backbone that makes it easy to choose and apply the appropriate data visualization technique to a given question, analysis or discussion.

“Every business problem and every data set shines at its best with the appropriate data visualization technique,” Silipo said.

3. Understand the users

EPAM Systems, an IT consulting firm, works with many different types of users. Success depends on understanding who the users are, whether they will use the same data and how they will work with it, said Pavel Tahil, senior UX designer at EPAM.

The following are some of the best practices Tahil employs at EPAM for aligning data visualization practices with users:

  • Find out who the users are and how they will use the data. Understand the target audience and connect business and user needs.
  • Divide data into multiple pages. One of the best data visualization practices is keeping the page short. Lots of data in one place will not help maintain focus on the information needed.
  • Find out the connection between different types of data. Various charts can be connected, and filters can control them. Users’ needs can determine whether and how to group the data.
  • Use accessibility standards for fonts and colors. Ensure that data is easy to read for users with disabilities.
  • If the audience mainly uses mobile devices, start with a mobile-first approach and then extend it to desktop.

4. Understand the business context

There needs to be a lot of thoughtfulness around how data is presented visually and how the story is told. Consider the user experience and the types of charts used to visualize data.

Eight steps to improve data visualization literacy

“Achieving a baseline understanding of the business context and the audience that you’re working with is an essential step to data visualization literacy,” Schwarz said.

For instance, someone could visualize a general population and segments within that population very differently than they would present information where only two data points are tracked over time. With only two data points, a simple line chart is often sufficient.

When visualizing a general population, someone might want to relate demographics such as age, race, occupation and income to some metric relevant to the business using a combination of line charts, bubble charts or bar charts.

Data visualization literacy would inform the best way to connect the presentation of the data to some aspect of the business it may relate to, such as identifying specific population segments that spend more on particular product lines or categories.

5. Set up a feedback loop

With data visualization, most of the responsibility is on creators to adjust their visualization, much like book writers adapt their language to the audience. The way they present the data must be understandable by consumers.

Quick data analysis and visualization feedback loops can help improve everyone’s skills at creating and consuming visualizations, said Marcin Bartoszek, head of business intelligence at Spacelift, an infrastructure-as-code platform for DevOps engineers.

In the requirements gathering phase, the analyst needs to understand what needs to be done and present an initial data analysis design, including the visualization. The stakeholder can then learn about the methods, give input and learn how to interpret the data. The analyst can then adjust the level of complexity based on the feedback.

6. Identify gaps

Teams also should identify data visualization literacy gaps within the organization or for individuals, said Srivastava. Individuals can then take steps to address the skill or knowledge gaps by learning more about data analysis and visualization techniques and tools, or making effective data visualizations for communicating insights from different types of data.

For example, determine the best type of chart types and when to use them, or learn about the difference between a sunburst and a pie chart.

7. Have fun with it

Data visualization is an art and a science, said Andy Cotgreave, technical evangelist for Tableau at Salesforce. He recommended finding some data that is personally relevant to learn how to use a data visualization platform.

“Using personally interesting data will motivate you to follow hunts and explore the data,” he said.

It is also helpful to consider how charts are used in the media. Are they honest or deceptive? How else can the same data be visualized? Also, when charts appear in presentations at work, be more mindful as to whether they are effective or distracting and why that is the case. Collaboration is key as well.

“The Data Visualization Society is a huge community of other practitioners,” Cotgreave said. “Online collaborative projects such as Real World Fake Data, Viz For Social Good, Back 2 Viz Basics are all safe places to play with data and share with others.”

8. Consider technology, process and culture

Data visualization literacy requires understanding technology, process and culture, said Shawn Rogers, vice president of analytics strategy at Tibco. Putting the analytics technology in the hands of the right people is key to growing and democratizing data-driven insights. Repeatable processes ensure consistency, standardization and governance, which provide teams a curated path to being more insightful with the visualizations and insights from analytics platforms

Culture helps create the appropriate framework for skill-building, education, collaboration and funding.

Data literacy and data visualization literacy go hand in hand in delivering the final mile of analytics value.

“You can possess all the data in the world and use it to power beautiful visualizations,” Rogers said. “But if the user isn’t data literate and lacks the skills to accurately understand and communicate these insights to drive action, the value is immediately lost.”

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