In a data-driven environment like Netflix, data visualization plays a key role. It must. In The Visual Organization, I offer the following definition of data visualization. Dataviz signifies the practice of representing data through visual and often interactive means. An individual dataviz represents information after it been abstracted in some schematic form. Finally, contemporary data visualization technologies are capable of incorporating what we now call Big Data.
According to its corporate blog, Netflix considers data visualization to be of paramount importance. Many of Netflix’s major systems contain significant dataviz components. And, like other Visual Organizations covered in this section, Netflix uses data-visualization tools on a continuous basis, not occasionally. That is, Netflix employees routinely look to existing dataviz tools to tweak algorithms, garner new insights, and solve pressing business issues.
Jeff Magnusson serves as the manager of data platform architecture at the company. On June 27, 2013, at the Hadoop Summit, he provided a rare window into the Netflix Big Data ethos. Magnusson presented with Charles Smith, a colleague and a software engineer. The two talked about how data should be accessible, easy to discover, and easy to process for everyone. The title of the talk: “Watching Pigs Fly with the Netflix Hadoop Toolkit.” During their presentation, Magnusson and Smith laid out three key tenets of the Netflix data philosophy:
- Data should be accessible, easy to discover, and easy to process for everyone.
- Whether your dataset is large or small, being able to visualize it makes it easier to explain.
- The longer you take to find the data, the less valuable it becomes.
These canons explain why Netflix is the quintessential Visual Organization. At the heart of its business lie some of the most sophisticated Big Data tools on the planet, including no shortage of dataviz applications. At a high level, these tools serve the interests of two critical constituencies: customers and technical professionals. It’s important to note, however, that satisfying both masters ultimately benefits everyone: executives, stockholders, nontechnical employees, and others.
Customer Insights
Look at the covers of House of Cards and the 2010 version of Macbeth that ran on the PBS series Great Performances.
At first glance, they are eerily similar. They both display older white men with blood on their hands—Kevin Spacey and Patrick Stewart, respectively—against primarily black backgrounds. Figure 3.1 illustrates the detailed color breakdown:
Figure 3.1 manifests the obvious: the covers of the two shows are much more similar than dissimilar. At the same time, though, subtle differences exist—and Netflix can precisely quantify those differences. What’s more, Netflix can see if they have any discernible impact on subscriber viewing habits, recommendations, ratings, and the like.
Figure 3.2 shows a similar color analysis of the House of Cards, Arrested Development, and Hemlock Grove, an American horror thriller and Netflix original program that premiered on April 19, 2013.
Given the cost of producing high-quality original content, why would Netflix create the cover for a new series in a vacuum? Why wouldn’t decision-makers look at the company’s vast trove of data? With subscribers bombarded by nearly unlimited options, why leave such a potentially critical aspect completely to chance? After all, Netflix possesses the data to make the most informed business decision possible. No, Netflix didn’t invite outsiders to production meetings for Hemlock Grove and House of Cards. Still, you can bet that its head honchos carefully reviewed subscriber data when selecting the covers to these series.
At Netflix, comparing the hues of similar pictures isn’t a one-time experiment conducted by an employee with far too much time on his hands. It’s a regular occurrence. Netflix recognizes that there is tremendous potential value in these discoveries. To that end, the company has created the tools to unlock that value. At the Hadoop Summit, Magnusson and Smith talked about how data on titles, colors, and covers helps Netflix in many ways. For one, analyzing colors allows the company to measure the distance between customers. It can also determine, in Smith’s words, the “average color of titles for each customer in a 216-degree vector over the last N days.”
In a word, wow.
How many organizations understand their customers to this extent? I would hazard to guess that few do. Most companies would love to know even half as much about their customers as Netflix does.
This begs the obvious question, how? Through Big Data and dataviz, 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. With respect to color and covers, these include the following:
- Are certain customers trending toward specific types of covers? If so, should personalized recommendations automatically change?
- Which title colors appeal to which customers?
- Is there an ideal cover for an original series? Or should different colors be used for different audiences?
- And plenty more.
Simon Says: Think Visually
In short, Visual Organizations like Netflix can ask better questions and make better business decisions based upon superior data, dataviz tools, and a culture that recognizes the importance of both.
Watch the trailer for The Visual Organization here, then share your thoughts below.
Phil Simon is a frequent keynote speaker and recognized technology expert.
This post was adapted from his new book, “The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions” Wiley, 2014.
