Make more effective use of data visualizations through these important best practices, including how to prepare for the future of data representation through VR.
Organizations around the world use data visualization in their jobs, but they may not be using the technology to its full potential. Many professionals employ data visualization on an ad hoc basis while using underpowered technology. Almost half of finance executives still use spreadsheets, for instance, while 39% use dashboards and only 27% use advanced visualization technology.
Marketers limit their ability to achieve discovery through visualization by using it reactively and not considering available tools and practices that can make it more actionable. Putting the following best practices in use can help these marketers catch up to modern standards of visualization and poise them to take advantage of capabilities that lie on the horizon.
#1 Understand the Benefits of Data Visualization
Human brains take powerful cues from what we see. In fact, part of our brain detaches during fetal development to form retinas. Humans use this connection to interpret visual information at a rate equivalent to 10 million bits per second. Sixty-five percent of the population also responds more strongly to images and spatial relationships compared to textual information since they are considered “visual learners.”
Representing data through symbolic letters and numbers arranged in tables adds latency, as our brains must interpret the information and decide what to do with it. With pure visuals, we can draw conclusions rapidly by observing patterns, trends, and abnormalities almost instinctively. Data visualization helps tell a story that numbers and text in a spreadsheet could never do on its own.
It also allows us to visually explore relationships and uncover insights, especially when combining data sets. Pointing out connections in data through hard numbers makes much less of an impact than allowing someone to draw the connections visually themselves. Through these benefits, data visualization helps both the creator and the end viewer make sense of the mass amounts of data we now have at our fingertips today. But visualizations should make intuitive sense. If they do not present useful information, they can be superfluous or even distracting.
#2 Bad Data = Bad Visualizations
Bad, misleading, or inaccurate data will lead to bad, misleading, or inaccurate visualizations. For marketers to get the most out of their visualizations, they must prioritize a data strategy that emphasizes data quality and hierarchical organization.
Data quality is arguably the most important step to get right, as garbage in means garbage out. Assurance checks and cleansing should be built into data processing to ensure accuracy and appropriate representation. Data points can then be assigned meta-qualities that make drill-downs, sorting, and other manipulations easier. As an example, raw performance data can be classified with added detail, such as creating a brand category or sub-brand, in order to be better grouped and filtered for the end user. Organizations can also determine a strategy to commingle data across silos so marketers can explore deeper relationships, patterns, connections, and trends that cannot be seen with a single data set alone.
All data should be regularly audited and processed into structured data for visualizations to not just be accurate but, above all, meaningful.
#3 Learn Effective Representation and Storytelling
The human brain can instantly read stories from visualized data only if the creator understands how these stories are meant to be interpreted. Symbolic representation must be clear and account for the ways our brains perceives information. For instance, the human brain can detect changes in hue faster than changes in shape, so highlighting key data points in a single color helps draw the eye toward important callouts compared to using a different visual symbol.
Ensure that the visualization type you use — line graph versus a scatter chart, for example — also tells the story you intend to communicate. Part of using the right visualization involves research on symbolic representation and available options, but teams should also be willing to experiment to see what works for their purposes.
Another key to data visualization is telling a deeper story through multiple visuals and accompanying commentary than a single visualization could do alone. When looking at a single visualization, the viewer should be able to easily see what is happening, but it often takes deeper analysis to understand why. For instance, a spike in March sales for winter jackets may seem like it could correspond to end of season promotional sales, but deeper analysis could also show that there was a cold front occouring at that same time. By bringing in multiple data sets and mining to consider contextual factors at play, a data analyst helps to explain the “why” and determine an actionable insight. Marketers should always aim to enhance their visualization by seeking out this deeper story, which is often revealed through careful comparison of trends with underlying variables.
#4 Be Open to Flexible, Interactive Tools
Data visualization opens itself up to far more data series, variable axes, and overall information when people can interact with data to make discoveries on their own. Static images force the end user to only see the conclusion you draw for them vs. having the ability to explore on their own.
External audiences and internal teams alike can benefit from tools that extract data, display it visually and allow them to organically discover more from the data than initially meets the eye. Looking to easily shared, browser-based formats for data visualization can likewise spread insight further than proprietary file types or static visualizations ever could.
This was the thinking when one of the world’s biggest ride sharing companies made its 3D visualization and data mapping tool open source. The tool was originally developed to visualize geospatial data from ride pick-ups and drop-offs on both a granular and city-wide level. Others can now use the library to quickly deploy 3D visualizations of data sets that can be adjusted on-the-fly to represent exactly what the user needs to see at that moment.
#5 Anticipate Future Change in Data Visualization
With multiple datasets converging together and the need to find new, accurate, and useful ways to represent their stories, single axes line or bar graphs are becoming a thing of the past. Data scientists and visionaries are pushing the boundaries of traditional representations into new forms of media through innovative visuals and emerging technologies.
For example, Google put together an interactive “Rhythm of Food” diagram for common and not-so-common foods, which illustrated spikes in search data corresponding to each year. The visualization uses a radial spoke graph to emphasize cyclical patterns over linear growth, and it allows for users to drill down further or pull out data highlights of interest to them. This departure from simple graphs illustrates how deep data dives can still be summarized simply and beautifully through a format that best represents the data. The viewer’s mind can capture instant trends from the top-down view and add context to these observations as they explore further.
Other emerging data visualization technologies can enhance interactivity, deep exploration, and storytelling. Virtual reality is one of the most exciting of these possibilities. Rather than relying on a two-dimensional representation, individuals can walk around visualized data sets and expand their experience to a third dimension.
The VR experience will put viewers inside the data and allow for easy manipulation using sliders to control factors such as the time period, creating the potential for marketing teams and their audiences to discover more than ever. Combining these visualization capabilities with automation and a machine learning back end can reveal new and exciting insights for marketers and data experts everywhere.
One emerging tool, for instance, helps reduce the number of dimensions and streamline the look of “messy” data sets in 3D. “Users select a variable they want to understand – money spent per customer, say – and the system automatically comes up with a set of explanatory variables, chooses a graph type (a scatter plot or histogram, for example) and then visualizes the data using the three axes, colors, and shapes.”
Experiences like these show just how powerful, versatile, and informative data visualizations can be if creators understand how to follow best practices and employ them effectively. As we move into a future of interactivity, higher production value, and new methods of exploring data sets in VR, these principles will only become more important over time.