Why You Should Visualize Big Data

Big data is . . . lots of data from lots of sources. But we already knew that.

Big Data Is . . . 

Mike Urbonas, director of product marketing for Attivio, says that when we focus on how much, we’re missing the point. Big data is really “the variety of information sources.” When we look at all the avenues of where data takes us, we can gain deeper and clearer insights.

This is Gartner’s definition: “Big data is high-volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”

Now, here’s TIBCO’s definition: “Data that’s difficult to analyze in-memory,” says Michael O’Connell, the chief data scientist at TIBCO Spotfire. He adds that it’s a process of “going back and forth to the databases.”

Big Data + Visualization To Make Sense of Variety & Volume

Anyone who works on anything analytical knows that you’ve got to tap multiple sources of information to create a business case or report to persuade stakeholders to act, not act, or change course.

In fact, this blog post is an example of pulling big data elements together (albeit Google did most of the work) to create an article that defines a foreign concept. That brings us to the importance of data visualization. When you’re pulling from all these sources, it’s easy for stakeholders to get lost in a presentation.

Presentation Really Matters with Big Data

Imagine this example: You’re sitting at a board meeting and there’s a stack of papers on the conference table to reference alongside the PowerPoint presentation. The papers are a number of spreadsheets with barely readable text (due to shrinking 27 columns to a legal-size piece of copy paper).

While you’re supposed to be listening to a presentation on why your company should purchase XYZ, you’re lost in the columns of the spreadsheet. It’s hard to ask questions because there’s too much information.

Big data doesn’t mean bigger spreadsheets. It means thinking about data as a source. For instance, we read 10 articles to source information for this blog post. Then, we tossed aside the information we didn’t need so we could present the most important stuff. Similarly, a focus on data visualization will do the same thing – weed out the extraneous data.

Make meetings more pleasant and more productive with a big data snapshot. Here are some tips on getting started with snappy data visuals:

1. Get past the spreadsheet. While Excel is excellent as a data source, it does not provide a powerful data visual.

2. Decide the story you want the data to tell. Data visualization becomes the foundation for your analytics questions.

3. Leverage line graphs and scatter plots. Graphs are an excellent vehicle for data visualization, but the graph type matters. Scatter plots and line graphs offer better visual representations than your standard pie and bar charts.

Never be caught with a 27-column spreadsheet again.

Next Steps: