The difference between making good decisions and bad decisions is the quality of information used to make the decision. Timely, relevant, and well-articulated data can guide decision making, but messy and incomprehensible data can stymie innovation and undermine confidence and trust. In many businesses today, decisions are being made based on data that is old or incomplete, because there’s no easy way to extrapolate data and share the findings in a timely manner.
How can businesses and individuals make sense of the volume of data coming their way? Moreover, how can organizations increase understanding of complex concepts and questions? The answer may lie in the twin concepts of visual intelligence and business intelligence.
What is Visual Intelligence?
Visual Intelligence is not exactly a buzzword, but the concept is relatively easy to grasp. Being visually intelligent means being able to process, understand, and express visual information. In her book “Visual Intelligence: Sharpen Your Perception, Change Your Life,” author Amy E. Herman describes the concept as:
the ability to see what’s there that others don’t, to see what’s not there that should be, to see the positives and the negatives, the opportunity, the invention, the upside, the warning signs, the quickest way, the way out, the win.”
Herman purports that one’s Visual Intelligence is a quotient that can be improved by practice. She often uses art in her training and examples, showing how the effort of examining and expressing visual stimuli can have a positive effect on general communication and understanding. The details are what seem to be her focus; those details that others seem to miss, but can be instrumental in solving the task as hand. She shares real-world examples from her decades of law enforcement and municipal consulting, like crime scenes that were only solved because a detail unnoticed by many; a ceiling fan still turning, or a pair of pants that was mysteriously inside-out.
Seeing things clearly, and in a way that others cannot, is valuable in any setting but has particular efficacy in business. Increasing visual acuity, and in turn the communication of the insights gained from visual acuity, can have enormous impact on the success of business initiatives. This is where ideas can generate, where pitfalls can be avoided, and opportunities are uncovered.
Blending Visual Intelligence and Business Intelligence
Business Intelligence, as a field, tries to make sense of data. Business Intelligence software is one of the main tools in this mission, as it empowers users to blend data from multiple applications and databases, providing a coherent picture of business operations in aggregate. How, though, does that picture come together? How can we assure that the data is being communicated effectively? How do businesses make the connection between “business intelligence” and “visual intelligence”?
Data visualization is an efficient way to process complex information, which is crucial given that the amount of data generated today can feel overwhelming. Visualizing data opens the door to greater comprehension of complicated data points by catering to the fundamentals of visual perception like pre-attentive processing and Gestalt principles. Our brains are uniquely suited to processing visual information quickly and effectively, and a well-planned data visualization can communicate more than a table or paragraph ever could.
Read next: Psychology of Data Visualization
Dashboards aggregate data from various sources and display that data visually, communicating the message behind the numbers and encouraging viewers to take action on the information at hand. A visual dashboard is synced real time to the data sources the user already relies upon, whether that’s Excel spreadsheets, databases, CRMs, online apps like Google Analytics, or a central data repository where multiple sources are blended.
Data Visualization as Process
One may draw the conclusion that data visualization and data analysis are the same thing, or that data visualization is the resulting product from “successful” data analysis. While it is true that a certain amount of data work is necessary to create a data visualization or dashboard (especially one using several different data sources), that data viz is not the end of the road. In fact, a data visualization can be an effective tool to begin data analysis.
Frank Anscombe, the founding chair of the statistics department at Yale University, illustrated the importance of visualizing data prior to analysis based on research done in the 1970’s. Using four similar data sets, Anscombe developed what is commonly known today as “Anscombe’s Quartet” which shows the effect of “outliers” within the data set when put in to a graphic representation of that data. All four have many of the same data properties, such as linear regression line and mean values of X and Y, but graph very differently.
(Fun fact: Anscombe was brother-in-law to another famous statistician, John Tukey, father of Exploratory Data Analysis. I imagine holiday dinners were scintillating.)
Anscombe’s Quartet is a clear example of how visualizing data can uncover insight. Without the ability to use visual intelligence to identify the outliers, the important aspects of the data set might have been missed. A preliminary data visualization can be critical to identifying the real questions one eventually wants to answer, leading to further analysis and potentially further visualizations to communicate the insights uncovered.
If Visual Intelligence is the quotient of one’s perceptual acuity, and if indeed practice makes perfect, it would stand to reason that the more data visualizations one makes and analyzes, the more visually intelligent and effective one will be. Data visualization allows us to both exercise our visual intelligence and to benefit from it, uncovering insights and sharing those insights with colleagues and collaborators. The right business intelligence platform will provide the forum for visual intelligence to flourish, leading to greater understanding, communication, and decision-making.
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