Introduction to Data Visualization

In the not-too-distant past, organizations relied on their IT departments to gather, store, and analyze data for business intelligence, or BI. With today's exponential data growth and the increasing need for real-time information access, traditional BI methods are less applicable. This is where Self-Service Business Intelligence or SSBI comes into play.

SSBI empowers authorized users, even those without data mining or analysis backgrounds, to work with data and easily create their own reports and run queries to make business decisions based on the data. But, both understanding the story the data tells and making that story coherent and compelling to the company's decision makers are difficult challenges.

So, most SSBI tools include data visualization capabilities. Data visualization converts data into comprehensive images, allowing key decision-makers to easily understand complex concepts, discover and analyze data patterns, and uncover value for the business. As numbers on a page, the relationships between data points can be unclear.

But once visualized, those relationships provide solid business insights paving the way for informed decision-making. Suppose your executive team is told the organization's net promoter score is down by a few points in a specific region. This data alone doesn't make it obvious why. By using data visualization tools, your team could drill down further to discover that call resolution is an issue in that region, explaining why customers aren't likely to recommend your organization's products or services to others. That is information that can be acted on to improve the NPS score.

Data visualizations can take many forms. Take an interactive real-time map of various transport systems for a specific area as an example. It allows transit users to filter information so only data relevant to them displays.

A further benefit of data visualization is that people are able to understand it much faster than text. Consider an infoscape, it uses splashes of color to display a range of health symptoms and how they relate to each other. Now compare that to sifting through scores of medical texts to find correlations between the same set of health symptoms. The difference is quite apparent. In this way, the next benefit is equally apparent. Organizations are able to use the information derived from data visualization to make better business decisions.

Consider social media, an unstructured social network data graph might depict networks of friends and their profile info on social media. Without data analytics, this information would be useless. But when transformed into an interest graph, it displays networks of people who have similar interests in terms of brands or products. Used together, these graphs hold the power to steer future shopping trends.

The benefits of data visualization far outweigh any risks. However, for any organization to reap the full rewards of this method of presenting data, those who work with it need to understand the considerations for selecting data visualization software. And once it has the software, there are best practices to follow for communicating clearly with data visualizations. It is important to know when to use which visualization type, and how to create visualizations that are clear and accurate.