Data visualization is pervasive across all business intelligence (BI) applications because of the speed and ease for communicating relationships and trends. However, many organizations still struggle to create effective visualizations because of the lack of training and understanding of basic principles.
While data visualization and business intelligence go hand-in-hand, they are both disciplines that require knowledge of core building blocks and practice. Data visualization should help accelerate the process for reaching a thought plateau where new knowledge is gained. And this should be your sole criteria for success.
Understanding the following distinctions and thinking deeply about your desired visualization goals is pivotal to you and your end user’s success.
Data Versus Information
Data is one raw ingredient for creating information. It’s not necessarily data or the visual representation of data that business users need – it’s information. The desire for speed and simplicity to visualize data often obscures the goal of obtaining actionable information.
As such, the leading questions to create visualization should be: “What information do I need?” and not “What data should I gather or what chart should I use?” The ultimate goal of knowledge and insight requires a deeper understanding of decisions and processes executed by those who will benefit from interactive visualization.
In this example, the information required was a basic view of geographic relationships between the top growing cities in the US.
If the geo-spatial reference and proximity is not a priority, and instead, the required information is the relative difference between population and growth, a bar chart would be a much better choice than a map.
Visualization Versus Analysis
The act of visualizing and analyzing data are not mutually exclusive. Visualization is the communication medium, while analysis is a human driven process for discovering meaning. There are a seemingly infinite number of tools today to help us analyze and visualize data together.
It’s particularly important to make this distinction when working with business users. A challenge with some business intelligence projects today is a belief that end users can make assumptions of what is meaningful by simply visualizing data. This couldn’t be further from the truth because end users may not uncover meaning or what’s important without properly designed information graphics.
Visualizations you create and the tools you choose should take into account a need for context for what is good, bad, or important. In the example below, which visualization is better at indicating which customer has the highest probability of having problems? Using simple calculations to automate the analysis can save time and help users focus on what’s truly important.
While a historical trend of usage is a prettier picture to visualize data, ultimately the table provides a concise list of who the sales person should contact, and why they should contact them. This is a real world example taken from my own sales organization where reps asked for a top 10 list of prospects they should call rather than forcing them to try to interpret meaning behind historical trends.
While some of these distinctions may be a matter of semantics, it’s an important discussion your team should have to level-set and ensure everyone is on the same page for delivering value through your data visualization initiatives.
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