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My first job—long before I was in IT—was as a literature teacher in an inner-city high school. It left an indelible impression on me about data visualization, and its use is applicable to big data.

I took over a high school literature class after the previous teacher left, and the syllabus included major American writers, such as Faulkner, Dos Passos, and Mailer. My students were more interested in reading the daily sports page to see how the horse races turned out. I determined that the syllabus wasn’t going to work, so I discarded it. We developed a mock school paper and created a friendly rivalry with the real school paper, and the students began writing “street theater” plays that reflected the realities of their lives, and we performed them at local events.

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By the sixth week of the class, students were reading Twain and Steinbeck. They had discovered the joy of self expression and were now ready to read the writings of others. In short, the students had succeeded in visualizing themselves as writers.

Why data visualization matters in big data projects

What happened in that classroom visualization experience also applies in IT and business—and it has major implications for big data projects.

Data visualization is best developed in the end-product dashboards, spreadsheets, and graphics that can easily inform a senior executive or a line manager of the status of a business situation or goal.

If you’re using Internet of Things (IoT) sensors, an alert emanating from a sensor can be picked up, display a red, yellow, or green status, and it can be mapped geographically so you can see the exact location where the event alert is occurring. Or, if you’re performing sentiment analytics on social media as they concern your company or your products, you can immediately see spreadsheet, pictorial, or graphical representations that inform you which age groups, gender categories, and more have the most favorable response to a new product, as well as revenues charted against your strategic sales revenue goals.

These are the end-product heavy hitters when it comes to big data, because you have to keep executives and others who hold the budgetary purse strings engaged with big data projects or they lose interest. The more you show your work with data visualization, the more likely you are to get buy-in from other executives.

How data visualization will help you achieve big data goals

If you’re a big data project leader, data visualization tools like graphics, maps, bar charts, and spreadsheets deliver two important things:

  • They produce a visual summary of information that’s tied to an important business goal that management cares about.

  • They create belief.

I can’t say enough about the latter, especially since 77% of business leaders say they believe business adoption of AI and big data is still a major challenge, even though big data projects have been carried out in enterprises for nearly a decade.

You should also be producing the algorithms and the drill-down tools for employees to perform their analytics, and giving attention to the high-level information summaries that management can easily digest and act upon.

You can make data visualization work for you

Your end goals for analytics should always be creating big data solutions that enable employees at every level of the organization to use information from big data and visualize how it helps the business. These are the solutions that continue to create belief, confidence, and support for future big data projects.