Netflix’s use of data mining exposed the interests of its 30 million subscribers
Big data is what it sounds like it is – large, complex data sets (“What Is Big Data?,” n.d.). With the exponential growth of technology in today’s society, big data is becoming more common and even necessary for companies to utilize in order to develop strategies. The data allow for more accurate business decisions, as it can answer many questions that without big data would take too long to figure out. The art of searching and analyzing big data to support one’s theory or questions is called data mining. Mining data can result in discovered patterns, problems, or even more questions.Petersen (2016) identified 20 different large companies that data mine, one of those being Netflix. Netflix saw the value in big data and leveraged it to try to understand the likes and dislikes of their subscribers so they could ultimately target shows toward their audiences that they know they would watch. The most notable success of Netflix’s use of data mining was the release of the series House of Cards. Netflix’s use of data mining exposed the interests of its 30 million subscribers which led to the company confidently signing two seasons of House of Cards because they data said it would be a success and it surely was (Sull, 2015). The company did the same thing with Orange is the New Black, another Netflix series success that resulted from data mining. Sharpe et al., (2019) explain the four V’s that make big data “big” which consist of volume, variety, velocity, and veracity. The sheer volume of Netflix subscribers, now at an impressive 182.8 million and 105TB of data (Lee, 2020) sets the company up well for useful data mining. Netflix tracks which users are watching which shows, what the same user also watches, for how long they are watching, and so much more. Netflix collects information on a variety of data points.To name a few:Netflix users by region, generation, incomeMain complaintsViewershipThe velocity Netflix operates on when it comes to leveraging data mining is apparent on the home screen that presents movie or television series recommendations. It happens in real time and is tailored specifically to the viewer. From the viewer’s perspective, it occurs effortlessly and instantaneously, but in the background, machines and tools are at work to quickly display the right content that maximizes the viewer’s desire to watch. According to Kasula (2020), it is calculated that the average Netflix subscriber watching 2 hours of content each day.The fourth V of Big Data is veracity which refers to the quality of the data. There can be abnormalities in the data especially as the other 3 V’s increase. Kasula (2020) discussed the problem that resulted from the Netflix Prize Challenge that aimed at enhancing the algorithm used to predict preferences based off of previous ratings. There was a large discrepancy in the number of ratings as the average user rated 200 movies but on the low end just 3 and high end 17,000 ratings per user.Aside from just the Netflix example, there are challenges many companies face when it comes to data mining. Some data that is incomplete or wrong is referred to as “noisy”. This tends to happen because many times, the data is only as good as a human who originally created it. The larger the data set, the more likely it is to contain errors. Another issue is distributed data, or data being sourced from multiple systems. With big data, there are typically many sources of data that need to be extracted and combined to create the larger data set and things can get lost and changed along the way as complicated integrations take place. Lastly, the performance of the data mining system itself can be a challenge. If the algorithm being used is not efficient, then it can lead to skewed or incorrect results. Results may be directionally correct. But still have significant problems.
Kasula, C. P. (2020, June 28). Netflix Recommender System — A Big Data Case Study. Retrieved from Medium website: https://towardsdatascience.com/netflix-recommender-system-a-big-data-case-study-19cfa6d56ff5
Lee, E. (2020, April 21). Everyone You Know Just Signed Up for Netflix. The New York Times. Retrieved from https://www.nytimes.com/2020/04/21/business/media/netflix-q1-2020-earnings-nflx.html#:~:text=Netflix%20has%20182.8%20million%20subscribers%2C%20making%20it%20one
Petersen, R. (2016, November 7). 20 companies do data mining and make their business better. Retrieved from BarnRaisers, LLC website: https://barnraisersllc.com/2016/11/07/companies-data-mining-business-better/ (Links to an external site.)
Sharpe, N. D., De Veaux, R. D., & Velleman, P. F. (2019). Business statistics (4th ed.). Retrieved from https://www.redshelf.com
Sull, D. (2015, April 26). Netflix’s “House of Cards” secrets: The real story behind Kevin Spacey and Frank Underwood’s meteoric ascent. Retrieved March 31, 2021, from Salon website: https://www.salon.com/2015/04/26/netflixs_house_of_cards_secrets_the_real_story_behind_kevin_spacey_and_frank_underwoods_meteoric_ascent/What Is Big Data? | Oracle. (n.d.). Retrieved from www.oracle.com website: https://www.oracle.com/big-data/what-is-big-data/
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