How Netflix Got Big with Big Data

Author: Femi Akintoye
Posting date: 8/24/2018 7:51 AM
There’s little argument that Netflix have changed the game when it comes to how people consume entertainment. Whilst Amazon, Disney and Apple seek to replicate the success of Netflix’s model, they still lead the way with over 130 million subscribers worldwide and have just broken HBO’s 17-year streak as the most nominated ‘network’ at the Emmy’s with an astonishing 112 nominations. 

Having begun life as a subscription-based DVD rental-service created in response to founder Reed Hasting’s frustration with late rental fines, Netflix were one of the first to offer video-streaming as an option for viewing films and TV. Now filled with scores of original programming, the secret to their success lies not just in creativity and innovation, but in Big Data. 

Top Picks From Your Data


When the former CEO of the now-defunct Blockbuster claimed: “Netflix doesn’t really have or do anything that we can’t or don’t already do ourselves”, he made a vital oversight. Whilst Netflix may have offered fewer films and TV shows at the time, they were already busy collecting, and utilising, customer data in a way that hadn’t been done before. This included:

What do people search for?
When do they watch a program?
What device do they watch on?
Do genre preferences vary with device?
When do they stop watching?
What shows are the likely to ‘binge’?
Or even what are the horror films that people find too scary to watch until the end…

Netflix used, and still uses, this information to create recommendations for each user, curating an individual experience based upon personal preferences. This technique has been incredibly successful with over 75% of viewer activity based upon these recommendations.

And they continue to finesse how their collect their data, switching from a five-star rating system to a thumbs up/thumbs down model. Cameron Johnson, Netflix’s Director of Product Innovation had observed: “a difference between what [users] say, and what they do,”. For example, frequently-watched comedies were being awarded three stars, as opposed to occasionally-watched, but ‘more worthy’ documentaries being given five stars. By simplifying the system to a like/dislike set-up, Netflix can provide subscribers with recommendations “more aligned with what people actually play”. 

Stream if you want to go faster


Unlike traditional broadcast mediums, Netflix’s income doesn’t come from advertising, or a pay-per-view service, but subscribers. That means their main ambitions are to generate new subscribers and keep existing ones.

If Netflix has data that tells them users who stream over a specific number of hours of programming are more likely to stay, they can place their focus on ensuring they watch at least that many hours. It’s highly likely that the introduction of the ‘skip-credits’ feature was a result of Netflix realising that this was the time when people were most likely to turn off, when the was an opportunity to encourage them to watch more. 

Perhaps most interestingly of all, Netflix’s Big Data team are helping inform creativity. This ranges from supplying that data that helps personalise trailers for new content based on each subscriber’s preferences, to deciding which shows to commission. Netflix’s data told them that prison-based dramas, shows with strong-female ensembles, and programs with LGBT+ themes and characters were both popular, and shared a lot of audience overlap. With all this information at hand when they commissioned ‘Orange Is The New Black’ for a full series, Netflix could be sure that there was an audience for the show. 

As more and more companies add their own streaming services, including Disney’s expected behemoth, this targeted original content is going to become more and more valuable for Netflix. Fortunately, they’re long-used to changing not just how people watch, but also what they watch. 

Browse Our Collection


If you’re looking to apply your understanding of Big Data to disrupt and revolutionise an industry, we may have a role for you.

Take a look at our current selection of opportunities here

Related blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out the related posts below.

Using Data Ethically To Guide Digital Transformation

Over the past few years, the uptick in the number of companies putting more budget behind digital transformation has been significant. However, since the start of 2020 and the outbreak of the coronavirus pandemic, this number has accelerated on an unprecedented scale. Companies have been forced to re-evaluate  their systems and services to make them more efficient, effective and financially viable in order to stay competitive in this time of crisis. These changes help to support internal operational agility and learn about customers' needs and wants to create a much more personalised customer experience.  However, despite the vast amount of good these systems can do for companies' offerings, a lot of them, such as AI and machine learning, are inherently data driven. Therefore, these systems run a high risk of breaching ethical conducts, such as privacy and security leaks or serious issues with bias, if not created, developed and managed properly.  So, what can businesses do to ensure their digital transformation efforts are implemented in the most ethical way possible? Implement ways to reduce bias From Twitter opting to show a white person in a photo instead of a black person, soap dispensers not recognising black hands and women being perpetually rejected for financial loans; digital transformation tools, such as AI, have proven over the years to be inherently biased.  Of course, a computer cannot be decisive about gender or race, this problem of inequality from computer algorithms stems from the humans behind the screen. Despite the advancements made with Diversity and Inclusion efforts across all industries, Data & Analytics is still a predominantly white and male industry. Only 22 per cent of AI specialists are women, and an even lower number represent the BAME communities. Within Google, the world’s largest technology organisation, only 2.5 per cent of its employees are black, and a similar story can be seen at Facebook and Microsoft, where only 4 per cent of employees are black.  So, where our systems are being run by a group of people who are not representative of our diverse society, it should come as no surprise that our machines and algorithms are not representative either.  For businesses looking to implement AI and machine learning into their digital transformation moving forward, it is important you do so in a way that is truly reflective of a fair society. This can be achieved by encouraging a more diverse hiring process when looking for developers of AI systems, implementing fairness tests and always keeping your end user in mind, considering how the workings of your system may affect them.  Transparency Capturing Data is crucial for businesses when they are looking to implement or update digital transformation tools. Not only can this data show them the best ways to service customers’ needs and wants, but it can also show them where there are potential holes and issues in their current business models.  However, due to many mismanagements in past cases, such as Cambridge Analytica, customers have become increasingly worried about sharing their data with businesses in fear of personal data, such as credit card details or home addresses, being leaked. In 2018, Europe devised a new law known as the General Data Protection Regulation, or GDPR, to help minimise the risk of data breaches. Nevertheless, this still hasn’t stopped all businesses from collecting or sharing data illegally, which in turn, has damaged the trustworthiness of even the most law-abiding businesses who need to collect relevant consumer data.  Transparency is key to successful data collection for digital transformation. Your priority should be to always think about the end user and the impact poorly managed data may have on them. Explain methods for data collection clearly, ensure you can provide a clear end-to-end map of how their data is being used and always follow the law in order to keep your consumers, current and potential, safe from harm.  Make sure there is a process for accountability  Digital tools are usually brought in to replace a human being with qualifications and a wealth of experience. If this human being were to make a mistake in their line of work, then they would be held accountable and appropriate action would be taken. This process would then restore trust between business and consumer and things would carry on as usual.  But what happens if a machine makes an error, who is accountable?  Unfortunately, it has been the case that businesses choose to implement digital transformation tools in order to avoid corporate responsibility. This attitude will only cause, potentially lethal, harm to a business's reputation.  If you choose to implement digital tools, ensure you have a valid process for accountability which creates trust between yourself and your consumers and is representative of and fair to every group in society you’re potentially addressing.  Businesses must be aware of the potential ethical risks that come with badly managed digital transformation and the effects this may have on their brands reputation. Before implementing any technology, ensure you can, and will, do so in a transparent, trustworthy, fair, representative and law-abiding way.  If you’re in the world of Data & Analytics and looking to take a step up or find the next member of your team, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

It Takes Two: Data Architect Meets Data Engineer

Information. Data. The lifeblood of business. Information and data are used interchangeably, gathered, collected, and analysed to create actionable insights for informed business decisions. So, what does that mean exactly? And to that end, how do we know what information or data we need to make those decisions? Enter the Data Architect. The Role of a Data Architect Just like you might hire an architect to sketch out your dreamhouse, the Data Architect is a Data Visionary. They see the full picture and can craft the design and framework creating the blueprint for the Data Engineer, who will ultimately build the digital framework. Data Architects are the puzzle solvers who can take a jumble of puzzle pieces, in this case massive amounts of data, and put everything in order. It’s their job to figure out what’s important and what isn’t based on an organisation's business objectives. Skills for a Data Architect might include: Computer Science degree, or some variation thereof.Plenty of experience working with systems and application development.Extensive knowledge and able to deep dive into Information ManagementIf you’re just starting your Data Architect path, be prepared for years of building your experience in data design, data storage, and Data Management. The Role of a Data Engineer The Data Engineer builds the vision and brings it to life. But they don’t work in a vacuum and are integral to the Data Team working nearly in tandem with the Data Architect. These engineers are building the infrastructure – the pipelines and data lakes. Once exclusive to the software-engineering field, the data engineer’s role has evolved exponentially as data-focused software became an industry standard. Important skills for a Data Engineer might include. Strong developer skills.Understand a host of technologies such as Python, R, Hadoop, and moreCraft projects to show what you can do, not just talk about what you can do – your education isn’t much of a factor when it comes to data engineering. On the job training does it best.Social and communication skills are critical as you map initial designs, and a love of learning keeps everything humming along, even as technology libraries shift, and you have to learn something new. The Major Differences between the Data Architect and Data Engineer RolesAs intertwined as these two roles might seem, there are some crucial differences. Data Architect Crafts concept and visualises frameworkLeads the Data Science teams Data Engineer Builds and maintains the frameworkProvides supporting framework With a focus on Database Management technologies, it can seem as though Data Architect and Data Engineer are interchangeable. And at one time, Data Architects did also take on the Data Engineering role. But the knowledge each has is used differently.  Whether you’re looking to enter the field of Data Engineering, want to move up or over with your years of experience to Data Architect, or are just starting out. Harnham may have a role for you. Check out our current opportunities or get in touch with one of our expert consultants to learn more.  

RELATED Jobs

Salary

£45000 - £65000 per annum

Location

London

Description

Junior Software Engineer role for one of Europe's highest rated Fintech's.

Salary

£100000 - £120000 per annum + benefits

Location

Oxfordshire

Description

Head of Data role based in Oxford, paying up to £120,000

Salary

US$120000 - US$130000 per annum + Additional Benefits

Location

Cincinnati, Ohio

Description

My client in Ohio are looking for big data engineering experts looking to join a learning-based cutting edge environment to grow technically!

recently viewed jobs