Why Visualization Matters for Predictive Analytics

Reading rows of spreadsheets, scanning pages and pages of reports, and going through stacks of analytical results generated by predictive models can be painstaking, time-consuming, and — let’s face it — boring. Looking at a few graphs representing that same data is faster and easier, while imparting the same meaning. The graphs can bring more understanding more quickly, and drive the point home efficiently.

Arming your data analysts with visualization tools changes the way they analyze data: They can derive more insights and respond to risks more quickly. And they will be empowered to utilize imagination and creativity in their digging and mining for deeper insights. Additionally, through visualization tools, your analysts can present their findings to executives in a way that provides easy, user-friendly access to analytical results.

For instance, if you’re dealing with content analytics and have to analyze text, e-mails, and presentations (for openers), you can use visualization tools to convert the content and ideas mentioned in raw content (usually as text) into a clear pictorial representation.

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For example, these graphs represent the correlation between concepts mentioned in text sources. Think of it as a labor-saving device: Now someone doesn’t have to read thousands of pages, analyze them, extract the most relevant concepts, and derive a relationship among the items of data.

Analytics tools provide such visualizations as output, which goes beyond traditional visualizations by helping you with a sequence of tasks:

  1. Do the reading efficiently.

  2. Understand lengthy texts.

  3. Extract the most important concepts.

  4. Derive a clear visualization of the relationship between those concepts.

  5. Present the concepts in ways that your stakeholders find meaningful.

This process is known as interactive data visualization. It’s different from a simple visualization because

  • You can analyze and drill down into the data represented by the graphs and charts for more details and insights.

  • You can dynamically change the data used in those charts and graphs.

  • You can select the different predictive models or preprocessing techniques to apply to the data that generated the graph.

These visualization tools save the data analyst a tremendous amount of time when generating reports, graphs, and (most importantly) effective communication about the results of predictive analysis.

That effective communication includes getting people together in a room, presenting the visualizations, and leading discussions that emerge from questions such as these:

“What does that point in the graph mean?”
“Does everyone see what I see?”
“What would happen if we added or removed certain data elements or variables?”
“What would happen if we changed this or that variable?”

Such discussions could unveil aspects of the data that weren’t evident before, remove ambiguity, and answer some new questions about data patterns.

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