Data visualization tools are a great ally in data strategies but require literacy to avoid biases and pitfalls.
According to a Capgemini Research Institute study, 50% of companies globally are data-driven, while in Spain, that number rises to 30%. With digital transformation in full swing and big data as a major player, there are significant opportunities to turn this vast source of information into a competitive advantage for businesses..
One of the best ways to leverage these large volumes is through data visualization tools. Due to the way the brain works, using diagrams, charts, or graphs makes it easier to understand large amounts of complex data compared to spreadsheets or other types of reports.
The cognitive impact of visual elements makes colors, shapes, and patterns more attention-grabbing, more efficient for decoding, and better internalized. This way, we can identify trends and/or outliers with much greater precision.
Data visualization thus becomes an agile, fast, and simple way to convey concepts and allows “playing” with different scenarios with just small adjustments. A good data visualization tool enables identifying areas for improvement, factors that alter variables, situation predictions, comparisons, among other benefits, at a glance.
I would like to pause at this point to note that, as Darrell Huff explained in his classic “How to Lie with Statistics,” there are “multiple ways to torture data until it confesses what one wants to hear,” and this applies to visualization as well.
Although companies, businesses, and advertisers understand the enormous power of visualizing numbers and statistics, unfortunately, there are also ways to use them to create biased results and interpretations. Bias can be present in the choice of chart type, the way axes are constructed, or the questions guiding the visualization itself. If any of these elements change, the interpretation can also change completely, which is where its potential danger lies.
To quote Huff again: “Thieves already know these tricks; honest men must learn them for self-defense.” What does this mean? The point is to discover these traps before interpreting a data visualization and, especially, before using these inputs for our business strategy.
An effective data visualization must balance form and function, and as mentioned earlier, when I talk about form, I mean stopping to consider the framework and context of the entire visualization before drawing premature conclusions. Data and visual elements must work together, and their conjunction must guide the analysis.
I would also like to add that understanding and reading data visualizations is a skill that entrepreneurs, team leaders, and business decision-makers must have. There are many tools, as well as available material and experts who can shed light on how to approach this crucial part of a data strategy.
We are at a time when the amount of data and its importance in business is clearly increasing, along with the need for what is called “data literacy.”
The importance of data education is growing worldwide because those who are better trained in understanding these volumes of information will be in a better position to create and consolidate strategies that benefit both companies and clients.
Julio Cesar Blanco – May 9, 2022
