Streaming services like Netflix and Amazon are notoriously protective of their data. In fact, not one of the top video-on-demand streaming services shares its user data with other platforms or anyone else for that matter.

Netflix, for example, is so tight-lipped about its user data that we have almost no idea about how successful individual shows are. Everyone from its users to the general public, and even its investors, don’t really have any idea of what the actual numbers are.

From time to time, the company does release some information, as in the case of the release of Martin Scorsese’s The Irishman in 2019, however, it is clear that Netflix definitely does not like sharing data.

The logic behind this is crystal clear – data is priceless. If you want to improve your platform’s services and improve the quality of the films and TV shows that you make, you need lots of accurate data. By sharing that data, you are giving your competitors information that can only help them beat you.

The Importance of Data

It is one of the most common fears relating to AI in movies and TV that the technology will be hoarded by a handful of super-powerful companies, something which will eventually give them total control the market.

Data mining gives companies the ability to, firstly, understand exactly what each member (or user) of their audience wants to see, and, secondly, to use this understanding to slowly ‘educate’ or influence new and existing users to increasingly like similar and similar things.

This phenomenon is not unique to big data analytics. Indeed, this is a normal mechanism that has been a part of human cultures for as long as we have been the way we are.

Take, for example, the assimilation of new cultural practices such as worship rituals. These have often initially been forced on new peoples to replace their existing customs, but have ultimately taken root by individuals’ desire to emulate new things whenever there is some deeper appeal or growing mass adoption of them.

However, what is significant in regards to big data is that, for the first time ever, there is the possibility of an immense concentration of data, which maps and explains our personal preferences, being held in the hands of a few companies.

The concentration of such enormous amounts of data would, in effect, make these few companies all-powerful, and would inevitably severely harm smaller companies that are trying to compete with them.

Democratizing the Power of Big Data

To put the scale of the problem into perspective – Netflix had 204 million streaming subscribers as of Q4 2020. Amazon, the second biggest subscription-based streaming service, had 150 million.

Both of these companies have reached the top by leveraging the power of big data. While one might think that factors such as who has the biggest catalog might be primary drivers of user uptake, this is simply not the case.

Netflix has some 54 million more users than Amazon despite having far less content.

  • Netflix has 3,579 films and 1,836 TV shows
  • Amazon has 25,792 films and 2,605 TV shows.

Despite Amazon having far more content, Netflix leads the way and accounts for 34% of US streaming.

Combined, these two companies account for an enormous percentage of the U.S streaming market. Given just how much data this is providing for just two, highly secretive platforms, it is vital that if smaller companies are going to survive, that they be able to access the power that data provides.

It is on this premise that independent AI-assisted moviemaking companies are trying to make the technology of big data analytics and artificial intelligence available to all. The real challenge to these smaller companies is that they are able to gain access to the data they need to train their AI systems to be as accurate as possible.

Since the rule of thumb with AI big data analytic systems is the more data they can analyze, the more accurate they will become, the real battle in this industry will not be about the AI systems themselves, but rather how much and how freely they are able to access accurate data.

Thankfully, there are a large number of data sources available to AI-assisted moviemaking companies. Platforms such as IMDB, for example, are a gold mine when it comes to information regarding production budgets, gross earnings, actors’ profiles, and appearances, etc. Such platforms should be commended for providing high-quality/low-cost data to all.

Meanwhile, AI companies are able to access hugely important data in the form of films and TV shows via video-on-demand subscriptions, or by purchasing them, etc. To date, no company has tried to restrict this practice, something which will hopefully continue as the films and TV shows themselves are an invaluable source of huge amounts of data and represent the central driver of current AI analytical development.

The main goal of all artificial development in this area is to develop programs that can understand and interpret the actual content or ingredients of films and TV shows. Such a high-level interpretation is vital if AI programs are going to unlock their true potential as powerful production-assist tools. Thankfully, the data that this requires is currently available to all.

The real challenge that AI-assisted companies face is obtaining data that refers to ‘user’ viewing habits, i.e. which movies they like/dislike, how often they watch them, what times, etc. Without this data, AI moviemaking companies may well find themselves struggling to analyze with them with the same degree of accuracy available to the likes of Netflix. This will limit their ability to ascertain how successful any given film or TV show is likely to be.

It is for this reason that AI moviemaking companies may well soon find themselves partnering with smaller video on demand platforms in order to obtain this data.

Largo Films, the parent company behind, already foresaw this development and launched its own video-on-demand platform,, to ensure that it would not only get access to such data but would also benefit from a far more in depth perspective as to how the market is developing and how this data is benefiting platforms.

Given that it seems unfathomable that dominant companies like Netflix are ever going to share their data, it will be up to the ‘independents’ to come up with ever more creative ways to access the data that they need to democratize access to the game-changing technology of AI big data analytics.