Netflix and Uber: getting big data right
Whether we are aware of it or not, we are currently witnessing history being made. Big data is a movement that has the power and the potential to completely transform every aspect of business and society.
From the way we go about our daily lives to the way we treat cancer and protect our society from threats, big data will transform every industry, every aspect of our lives. We can say this with authority because it is already happening.
Some believe big data is a fad, but they could not be more wrong. The hype will fade, and even the name may disappear, but the implications will resonate and the phenomenon will only gather momentum. What we currently call big data today will simply be the norm in just a few years’ time.
Big data refers generally to the collection and utilisation of large or diverse volumes of data. In my work as a consultant, I work every day with companies and government organisations on big data projects that allow them to collect, store, and analyse the ever-increasing volumes of data to help improve what they do.
In the course of that work, I’ve seen many companies doing things wrong — and a few getting big data very right, including Netflix and Uber.
Netflix: Changing the way we watch TV and movies
The streaming movie and TV service Netflix are said to account for one-third of peak-time Internet traffic in the US, and the service now have 65 million members in over 50 countries enjoying more than 100 million hours of TV shows and movies a day. Data from these millions of subscribers is collected and monitored in an attempt to understand our viewing habits. But Netflix’s data isn’t just “big” in the literal sense. It is the combination of this data with cutting-edge analytical techniques that makes Netflix a true Big Data company.
Although Big Data is used across every aspect of the Netflix business, their holy grail has always been to predict what customers will enjoy watching. Big Data analytics is the fuel that fires the “recommendation engines” designed to serve this purpose.
At first, analysts were limited by the lack of information they had on their customers. As soon as streaming became the primary delivery method, many new data points on their customers became accessible. This new data enabled Netflix to build models to predict the perfect storm situation of customers consistently being served with movies they would enjoy.
Happy customers, after all, are far more likely to continue their subscriptions.
Another central element to Netflix’s attempt to give us films we will enjoy is tagging. The company pay people to watch movies and then tag them with elements the movies contain. They will then suggest you watch other productions that were tagged similarly to those you enjoyed.
Netflix’s letter to shareholders in April 2015 shows their Big Data strategy was paying off. They added 4.9 million new subscribers in Q1 2015, compared to four million in the same period in 2014. In Q1 2015 alone, Netflix members streamed 10 billion hours of content. If Netflix’s Big Data strategy continues to evolve, that number is set to increase.
Uber: Disrupting car services in the sharing economy
Uber is a smartphone app-based taxi booking service which connects users who need to get somewhere with drivers willing to give them a ride. Uber’s entire business model is based on the very Big Data principle of crowdsourcing: anyone with a car who is willing to help someone get to where they want to go can offer to help get them there. This gives greater choice for those who live in areas where there is little public transport, and helps to cut the number of cars on our busy streets by pooling journeys.
Uber stores and monitors data on every journey their users take, and use it to determine demand, allocate resources and set fares. The company also carry out in-depth analysis of public transport networks in the cities they serve, so they can focus coverage in poorly served areas and provide links to buses and trains.
Uber holds a vast database of drivers in all of the cities they cover, so when a passenger asks for a ride, they can instantly match you with the most suitable drivers. The company have developed algorithms to monitor traffic conditions and journey times in real time, meaning prices can be adjusted as demand for rides changes, and traffic conditions mean journeys are likely to take longer. This encourages more drivers to get behind the wheel when they are needed – and stay at home when demand is low.
The company have applied for a patent on this method of Big Data-informed pricing, which they call “surge pricing”. This is an implementation of “dynamic pricing” – similar to that used by hotel chains and airlines to adjust price to meet demand – although rather than simply increasing prices at weekends or during public holidays it uses predictive modelling to estimate demand in real time.
Data also drives (pardon the pun) the company’s UberPool service. According to Uber’s blog, introducing this service became a no-brainer when their data told them the “vast majority of [Uber trips in New York] have a look-a-like trip – a trip that starts near, ends near and is happening around the same time as another trip”.
Other initiatives either trialled or due to launch in the future include UberChopper, offering helicopter rides to the wealthy, Uber-Fresh for grocery deliveries and Uber Rush, a package courier service.
These are just two companies using Big Data to generate a very real advantage and disrupt their markets in incredible ways. I’ve compiled dozens more examples of Big Data in practice in my new book of the same name, in the hope that it will inspire and motivate more companies to similarly innovate and take their fields into the future.
Chinese Firm Taigusys Launches Emotion-Recognition System
In a detailed investigative report, the Guardian reported that Chinese tech company Taigusys can now monitor facial expressions. The company claims that it can track fake smiles, chart genuine emotions, and help police curtail security threats. ‘Ordinary people here in China aren’t happy about this technology, but they have no choice. If the police say there have to be cameras in a community, people will just have to live with it’, said Chen Wei, company founder and chairman. ‘There’s always that demand, and we’re here to fulfil it’.
Who Will Use the Data?
As of right now, the emotion-recognition market is supposed to be worth US$36bn by 2023—which hints at rapid global adoption. Taigusys counts Huawei, China Mobile, China Unicom, and PetroChina among its 36 clients, but none of them has yet revealed if they’ve purchased the new AI. In addition, Taigusys will likely implement the technology in Chinese prisons, schools, and nursing homes.
It’s not likely that emotion-recognition AI will stay within the realm of private enterprise. President Xi Jinping has promoted ‘positive energy’ among citizens and intimated that negative expressions are no good for a healthy society. If the Chinese central government continues to gain control over private companies’ tech data, national officials could use emotional data for ideological purposes—and target ‘unhappy’ or ‘suspicious’ citizens.
How Does It Work?
Taigusys’s AI will track facial muscle movements, body motions, and other biometric data to infer how a person is feeling, collecting massive amounts of personal data for machine learning purposes. If an individual displays too much negative emotion, the platform can recommend him or her for what’s termed ‘emotional support’—and what may end up being much worse.
Can We Really Detect Human Emotions?
This is still up for debate, but many critics say no. Psychologists still debate whether human emotions can be separated into basic emotions such as fear, joy, and surprise across cultures or whether something more complex is at stake. Many claim that AI emotion-reading technology is not only unethical but inaccurate since facial expressions don’t necessarily indicate someone’s true emotional state.
In addition, Taigusys’s facial tracking system could promote racial bias. One of the company’s systems classes faces as ‘yellow, white, or black’; another distinguishes between Uyghur and Han Chinese; and sometimes, the technology picks up certain ethnic features better than others.
Is China the Only One?
Not a chance. Other countries have also tried to decode and use emotions. In 2007, the U.S. Transportation Security Administration (TSA) launched a heavily contested training programme (SPOT) that taught airport personnel to monitor passengers for signs of stress, deception, and fear. But China as a nation rarely discusses bias, and as a result, its AI-based discrimination could be more dangerous.
‘That Chinese conceptions of race are going to be built into technology and exported to other parts of the world is troubling, particularly since there isn’t the kind of critical discourse [about racism and ethnicity in China] that we’re having in the United States’, said Shazeda Ahmed, an AI researcher at New York University (NYU).
Taigusys’s founder points out, on the other hand, that its system can help prevent tragic violence, citing a 2020 stabbing of 41 people in Guangxi Province. Yet top academics remain unconvinced. As Sandra Wachter, associate professor and senior research fellow at the University of Oxford’s Internet Institute, said: ‘[If this continues], we will see a clash with fundamental human rights, such as free expression and the right to privacy’.