Why Visualization Important?:
Data visualization is the representation of data or information in a graph, chart, or other visual format. It communicates relationships of the data with images. This is important because it allows trends and patterns to be more easily seen. With the rise of big data upon us, we need to be able to interpret increasingly larger batches of data. Machine learning makes it easier to conduct analyses such as predictive analysis, which can then serve as helpful visualizations to present. But data visualization is not only important for data scientists and data analysts, it is necessary to understand data visualization in any federal agency. Whether you work in finance, marketing, tech, design, or anything else, you need to visualize data. That fact showcases the importance of data visualization.
Expressiveness: A set of facts is expressible in a visual language if the sentences (i.e. the visualizations) in the language express all the facts in the set of data, and only the facts in the data.
Effectiveness: A visualization is more effective than another visualization if the information conveyed by one visualization is more readily perceived than the information in the other visualization.
According to the World Economic Forum, the world produces 2.5 quintillion bytes of data every day, and 90% of all data has been created in the last two years. With so much data, it’s become increasingly difficult to manage and make sense of it all. It would be impossible for any single person to wade through data line-by-line and see distinct patterns and make observations. Data proliferation can be managed as part of the data science process, which includes data visualization.
Data visualization can provide insight that traditional descriptive statistics cannot. A perfect example of this is Anscombe’s Quartet, created by Francis Anscombe in 1973. The illustration includes four different datasets with almost identical variance, mean, correlation between X and Y coordinates, and linear regression lines. However, the patterns are clearly different when plotted on a graph. Below, you can see a linear regression model would apply to graphs one and three, but a polynomial regression model would be ideal for graph two. This illustration highlights why it’s important to visualize data and not just rely on descriptive statistics.
Faster Decision Making:
Agencies who can gather and quickly act on their data will be more competitive in the marketplace because they can make informed decisions sooner than the competition. Speed is key, and data visualization aides in the understanding of vast quantities of data by applying visual representations to the data. This visualization layer typically sits on top of a data warehouse or data lake and allows users to discover and explore data in a self-service manner. Not only does this spur creativity, but it reduces the need for IT to allocate resources to continually build new models.
For example, say an agency market research analyst who works across 20 different ad platforms and internal systems needs to quickly understand the effectiveness of market researches. A manual way to do this would be to go to each system, pull a report, combine the data, and then analyze in Excel. The analyst will then need to look at a swarm of metrics and attributes and will have difficulty drawing conclusions. However, modern business intelligence (BI) platforms will automatically connect the data sources and layer on data visualizations so the analyst can slice and dice the data with ease and quickly come to conclusions about marketing performance.