My Data’s BIGGER than Yours

Data viz examples that make your Big Data problems seem small

Sizing up your big data problems to some of the largest companies in the world is a great way to put things in perspective. Big data analytics can require extremely sophisticated algorithms, but the success hinges on delivering actionable information to those who need it.

The following examples exemplify big data visualization success because the result of sophisticated big data problems funnels into the hands of front-line workers like designers, marketers and delivery truck drivers.


60 Million Users watch 10 Billion hours of video each month on Netflix. It is no surprise that the volume of data that Netflix collects to understand viewer preferences is massive. The following example illustrates a big data visualization that quantifies how cover-art design indirectly correlates to viewership.

“Through Big Data and data viz, Netflix seamlessly delivers mind-boggling personalization to each customer. At the same time, Netflix can easily aggregate data about customers, genres, viewing habits, trends, and just about anything else. Equipped with this data, Netflix can attempt to answer questions that most organizations can’t or won’t even ask.”

Read more on Wired Magazine


In 2014, UPS delivered 4.6 billion packages and documents worldwide with a delivery fleet of almost 100,000 vehicles.  UPS invests more than a billion dollars a year in operational and customer systems, so it is no surprise they have some very sophisticated big data analytics. At that scale, small adjustments in route optimization can result in millions in savings, as shown in the example below:

An optimized route map using ORION. (Credit: UPS)

“UPS saved 3 million gallons of fuel during its testing of the program from 2010-2012 and says it’ll reduce its consumption by another 1.5 million gallons this year. Once the program is rolled out to every driver by 2017, the company says it can save $50 million by taking just one mile off each of its driver’s daily routes.”

Read More on Forbes

How big are your Data Visualizations?

We want to hear what kind of big data crunching problems you are solving with visualization. Sound off here in the comments section.


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