Accounting Decision Analysis Paper
YouTube started out as a small video sharing website in 2005, a home for funny cat videos and the place where anyone could get their fifteen minutes of fame. Now, YouTube has become a place where information can be shared live, video gamers can make money for having fun, and professors can look for content to educate their students. Almost anything in video format can be found on YouTube now, including copyrighted content. Because of the ease in which content can be stolen, YouTube implemented their Content ID system in June 2007. This automated system uses a database to identify when an uploaded video contains content that is copyrighted. After flagging the video, the person who owns the copyrighted material can block the video, track video statistics, or add advertisements to the video, allowing them, in turn, to make money off someone else’s video. While the system has been mostly effective, in some cases, it’s caused content creators to lose revenue because of misidentified content. The system lacks human checking for fair use, since every case is different.
As a content creator, I speak from experience on this matter. As of September 6, 2016, of the 915 videos I have uploaded over approximately three years, 54 have been flagged for copyrighted content. Of these 54, 44 had been flagged incorrectly. One of these videos was a cover of a song, which is considered original content. The rest are gameplay walk-through videos, all of which are flagged for their musical content. Proper permissions had already been given for the developers to use in their games, so in this case the system is flawed. I have lost revenue on 1,770 views. The average YouTube CPM (cost per mille or cost per thousand views) is $7.60. So, in the grand scheme of things, I’ve lost about $13.45, which is really not a whole lot. However, I’m just a small-time YouTuber. The consequences can be much, much worse as your popularity and view count goes up.
Currently, the ratio of lost revenue views to my total view count is 1,770 to 151,154, or 1.2%. Now, let’s say I had about ten times that amount, about 1.5 million views. Total earning potential here would be $11,400. I’d lose about $137. Finally, perhaps I had the popularity of MarkiplierGame, another gameplay channel. He currently has about 5.5 billion views. His total earnings would be about $41.8 million. If 1.2% of his content was incorrectly flagged ineligible for monetization, he’d lose $501,600, which is definitely no small amount.
As content creators, we have to take steps to avoid incorrectly flagged content. Many gameplay channels have resorted to editing out copyrighted content (i.e. music) to avoid being flagged for copyright infringement. If it isn’t possible to do so, human intervention is necessary. The content creator can file a dispute and the database is forced to be re-checked. The success or failure of the dispute can help change the massive content database, the advertising database, and the payroll database, all of which are relational and must be maintained.
In today’s world, businesses have to do everything they can to protect themselves from fraud and financial loss. Often, they turn to risk assessment specialists to help them strengthen to their internal controls and prevent mishandling of assets. I hope to use big data analysis to my advantage in this field. Scenario building, like I did above with the YouTube example, can help businesses better understand how taking certain steps to protect their assets can really make a huge positive impact in the long run, not to mention give business owners peace of mind that their assets are safer.
In summary, big data has a big impact on the way entrepreneurs look at the contents of their business, be it a YouTube channel or a bank. We must look at what our assets are, what kind of threats can impact them or create loss, and determine the best ways to protect them from these threats. Big data can help us build scenarios to determine just how much of an impact a type of situation can bring to our business. If we use data correctly, we can prevent loss, maximize gain, and be successful.