Whether it be the rise of the internet, the popularity of social media, the rising importance of big data or the practicality of location based services; these are all moments in history that have propelled society into the digital revolution.
And now with these tools working in a synchronised manner, society has been exposed to new business innovative models that are changing the way individuals live their daily lives.
Welcome to the gig economy.
Evolving from the shared economy which saw society collaborate online to fund businesses (Dickinson, 2016), this ongoing phenomena is characterised by influential forces such as urbanisation and technological innovation.
One of the most prominent companies that has capitalised on this innovational shift is Uber. Launched in 2012, Uber is a transportation network company that connects riders with drivers using their phone’s GPS services. The app has done more than just disrupt the transportation industry. It has transformed the way individuals can conveniently call for a ride and pioneered innovative ways to integrate mobile phone functionality into our everyday lives.
What began as a luxury car service in San Francisco is now expected to valued at $120 billion in 2019, operating in more than 35 cities worldwide.
Dogtiev (2018) provides quick comparison between Uber and traditional taxi services:
In order to completely evaluate how Uber has mobilised social or cultural change as an agent of interconnected change, it is essential to assess Uber’s role in the following areas:
- Utilisation of big data in the query society
- Functionality and its transformation of the internet of things
- Transforming the internet of things to create smart cities
An overview of the areas and how they operate interdependently can be seen below:
Big Data in the Query Society
Living in the Information Age where data is used as commodity in economic exchanges, the value of big data is insurmountable. According to Desouza and Smith (2014), big data is used to describe “the growing proliferation of data and our increasing ability to make productive use of it”. In attempt to simplify the complexity of big data, IBM summaries the four V’s:
In defining Google as a “database of intention”, Konig and Rasch (2014) concludes that the “gathering of user information is the backbone of digital media economics”. Based on an interview with Uber’s head of machine learning, the transportation company’s machine learning is said to use a combination of web interfaces, advanced passenger information system (APIs) and software development kits (SDKs).
By using these tools to learn about the experience of both successful and unsuccessful pick-ups, Uber creates an algorithm that detects patterns of successful pick-ups. This machine learning algorithm allows Uber to utilise predictive modelling in real time based on traffic patterns, supply and demand predictions which is crucial for:
- improving the accuracy of their pricing strategy — price surges in peak areas and hours; and estimated costs
- the estimated time of arrival
Uber can be seen as responding to the challenges of Big Data, as described by Gobble (2013) who comments that “big data is when your data sets become so large that you have to start innovating how to collect, store, organise, analyse and share it.”
Lange, head of machine learning at Uber summarises this by commenting that:
Machine learning has been in our DNA from the beginning. The nature of Uber is this idea of a two-sided marketplace where you have drivers on one side and riders on the other side. The essence of creating an efficient marketplace is really having a lot of these dynamic properties that benefit from machinery.
These network capacities demonstrated by Uber exemplify the rising relevance of big data for success in businesses with Kho’s (2017) paper concluding that “when organisations can truly leverage their Big Data assets…the impact on customer satisfaction can be palpable”. Such claim is also reinstated by Gobble (2013) who reveal that “existing business are leveraging big data to streamline processes, create efficiencies, and improve customer service.”
Internet of Things
Following the discussion on Uber’s utilisation of big data , it becomes evident that there is a strong correlation between big data and the internet of things. According to Scuotto, Ferraris and Bresciani (2015), the Internet of things describes Internet connected devices as being interconnected whilst capable of collecting and exchanging data. Due to the “pervasive use of computational capabilities applied to many goods” (Scuotto, Ferraris & Bresciani, 2015); the internet of things have played a significant role in making the lives of individuals efficient and smarter.
This is summarised by Intel in their video below:
In discussing how the Internet of things can benefit our daily lives, Borne (2018) predicts that the aggregated value and economic benefit of the IoT will exceed $1.9 trillion in the year 2020 alone.
Some examples in each industry can be seen below in the infographic provided by BigNerdRanch:
How is Uber utilising the internet of things?
Uber’s core business value proposition lies in their offering of a seamless transport experience. This process is contingent on two factors:
- quick driver pick-ups
- frictionless payment
However, this relies on Uber tapping into the data provided by their users.
Location – Based Services
When using the app, users are required to enable Uber to access their location information. In doing so, both drivers and riders are able to pinpoint each other’s location so that the eventual pick up is quick and easy. By using real time location information, Uber also allows riders to adjust their location which will automatically update on their driver’s app — eradicating any possible confusion. Furthermore, access to location information will provide riders with the opportunity to share their status with their friends and family so that they know when to expect your arrival.
Upon creating an account with Uber, riders are requested to provide their banking details. This allows for a quick payments made automatically when a ride is finished as well as the opportunity for users to split their fares across friends and family through a text message requesting permission for the split fee. In fact, Uber recently announced a new business partnership with Venmo that will allow riders to use Venmo as a payment method. This mobile payment service that allows friends to pay and request money from each other will assist in facilitating a seamless and frictionless transport experience.
In addition to Uber’s usage of personal data, the app offers users with multiple inter-app connectivity features which have played a significant role in maximising the customer experience for both drivers and riders. Riders are able to connect their calendars to the app (as seen in the video below) so that they can sync their events with the app which will generate a calendar shortcut so they can request a ride to the respective address of that event.
Other recent examples of cross app functionality include Spotlight which is a tool that helps make it easier for the rider and driver to find each other. This feature allows the phone to light up with a preselected colour and the driver will be notified of what colour to look for (as seen in the video below).
Further attempts by Uber to optimise their customer experience can also be seen in the development of their mapping system to ensure that drivers are picked up exactly where they requested, and improving their ability to detect fraudulent behaviour so riders with a stolen credit card are not accepted.
In recent times, Uber has revealed plans to incorporate artificial intelligence into their customer inquiry system by “clustering customers and creating segments to understand niche needs through machine learning” (Koetsier, 2018). In addition to the company’s use of AI, Uber filed a patent on AI tech to detect drunk passengers by identifying people in an “abnormal state” from factors including number of typos made, walking speed, and the precise clicks on buttons and links on their phones (Cuthbertson, 2018).
By maximising the full functionality of mobile phones to enhance user experience, it becomes evident that Uber has responded to Desouza and Smith’s (2014) comments that “mobile phones contain a treasure trove of information … act[ing] as an individual sensor collecting relevant information from its environment.” Ultimately, it becomes evident that Uber is revealing how the Internet of things has potential in transforming the way we operate our daily lives.
These innovative efforts and endless advances in technology by Uber to optimise every ride experience reflect their aim to transform the transportation industry. Earlier in 2018, the company announced plans that its app will soon feature multiple new transport options, including bike-sharing, options for mass transit, and peer-to-peer car rentals alongside their long-promised autonomous ride-hailing (Gelb, 2018).
In advocating these plans, chief executive of Uber, Khosrowshahi comments “Uber is not just going to be just about taking a car, but is about moving from point A to point B in the best way.” Due to these plans, Uber has now taken a strategic approach towards partnering with cities. Part of this strategy will involve Uber shifting focus on “making transportation safe, equitable and affordable across multiple modes” (Dickey, 2018); whilst also harnessing consumer consciousness of carbon impacts.
Evidence of this has been demonstrated through their partnership with e-ticketing service Masabi, which allows public transit riders to use the Uber app instead of a ticket or pass in Boston, Los Angeles, Las Vegas, New York, San Francisco and Europe. Moreover, to address the carbon impact, Uber aims to reduce the need for individual car ownership which would eventually reduce congestion in cities. This will be supported by Uber’s investments in its carpooling product (Dickey, 2018).
“Uber envisions being able to bring data, on-demand transportation and solving for real-time transportation needs to public transit. If people can reliably know when a bus will show up, we think that’s going to drive use and ultimately that’s going to make it even better to live in a city like this one.”
These strategic moves by Uber underpin the creation of a smart city. Involving sustainable “open and user-driven innovation ecosystems”, smart cities aim to “transform rural and urban areas in places of democratic innovation” (Scuotto, Ferraris & Bresciani, 2015). Extending upon this, Chauhan, Agarwal & Kar (2016) explain how smart cities implement modern information and communication technologies to improve the “quality and performance of municipal services whilst reducing costs for its citizens”. The benefits of a smart city can be seen below in the infographic illustrated by Advantage Business Marketing:
According to market research company, Frost & Sullivan, to be deemed as a smart city, a cart must adopt five of the following eight requirements:
- smart energy
- smart building
- smart health care
- smart infrastructure
- smart technology
- smart governance
- smart education
An exemplar application of these parameters can be seen with Uber when they recently announced the launch of their new tool, Uber Movement.
The tool is intended to be used by city officials looking to make infrastructure decisions, plan transportation routes and strategies, and get a better understanding of the transportation picture in their city (Kufel, 2017). Breaking down traffic zones, the tool aims to offer valuable information about how traffic in a city is affected by things such as road closures or big events. The video below demonstrates these features.
The Mayor of Washington DC supports this tool by stating:
“We’re excited to be one of Uber’s early partners on this new platform. We want to employ as many data sources as possible to mitigate traffic congestion, improve infrastructure, and make our streets safer for every visitor and resident in the nation’s capital.”
This move to create a smart city has been validated by the CEO as seen below:
Come along for a ride with our new CEO, @dkhos. He’s in the back seat talking about improvements to the app and how, moving forward, he’s committed to listening. https://t.co/e1Sl6BSEnQ pic.twitter.com/anJdS8ckWg
— Uber (@Uber) May 18, 2018
With conversations surrounding the benefits of smart cities, it becomes inevitable for cybersecurity to be overlooked. In explaining that big data requires big responsibility, Adams (2017) discusses how the internet of things will “magnify individual data privacy threats”. Despite the economic impact of sensor devices, the collection of data and customisation creates vulnerabilities in individual data privacy. In relation to Uber, the use of GPS technology may expose an individual’s whereabouts and activities thus threatening the personal safety of individuals. Even Uber’s recent idea of using artificial technology to detect drunk passengers has been questioned with many fearing that drivers will be discriminate and potentially abuse drunk passengers, especially those who are female.
Whilst the ethical use of information technology is questioned, smart cities are also at harm especially when considering the launch of Uber Movement which has the capability of giving insight into city traffic. Such innovation is debated to have the risk of threatening the public safety of cities, as mentioned by Rouland (2015) who hypothesises that this kind of high level technology might potentially lead to a “nation-state infiltrating a city’s smart grid and halted the distribution of electricity.”
The creation and development of Uber has revolutionised the world through its synergistic utilisation of big data and the internet of things to transform the way we transport to point A to point B. With recent innovations such as Uber Movement, Uber is proving that smart cities can soon be a reality.
It is hard to deny that the organisation is now more than just a ride sharing app.
Since operating, Uber has helped change many lives. For the better too.
Whether you’re a driver looking to take advantage of the gig economy for some extra income or a rider seeking convenient ways of getting to your destinations, there is no doubt that you have benefited from Uber’s innovation.
However, what makes all this so astonishing is that Uber has only been operating for seven years.
With so many individual lives being impacted so significantly, who knows what the $120 billion company has installed for society as a whole.
All that can be said is that as an innovation powerhouse, Uber will continue to improve our lives in compelling ways we can’t even anticipate.
Adams, M. (2017). Big data and individual privacy in the age of the internet of things. Technology Innovation Management Review, 7(4), 12-24.
Advantage Business Marketing. (2016). Smart City in a Box (Infographic), Retrieved 27 October 2018, from https://www.ecnmag.com/data-focus/2016/11/smart-city-box-infographic
Borne, K. (2014). 14 Benefits and Forces That Are Driving The Internet of Things, Retrieved 23 October 2018, from https://mapr.com/blog/14-benefits-and-forces-are-driving-internet-things/
Chauhan, S., Agarwal, N., & Kar, A. K. (2016). Addressing big data challenges in smart cities: A systematic literature review. Info : The Journal of Policy, Regulation and Strategy for Telecommunications, Information and Media, 18(4), 73-90.
Cutherbertson, A. (2018). Uber Patent Uses Artificial Intelligence to Tell If You’re Drunk, Retrieved 26 October, 2018, from https://www.independent.co.uk/life-style/gadgets-and-tech/news/uber-patent-drunk-passenger-ai-artificial-intelligence-app-a8395086.html
Desouza, K. C., & Smith, K. L. (2014). Big data for social innovation. Stanford Social Innovation Review, 12, 38-43.
Dickinson, B. (2016). The rise of the gig economy. Municipal World, 126(9), 19-20
Dickey, M. (2018). Uber CEO Outlines Mobility plans, Retrieved 25 October 2018, from https://techcrunch.com/2018/04/11/uber-ceo-outlines-mobility-plans/
Dogtiev, A. (2018). Uber Revenue and Usage Statistics (2017), Retrieved 22 October 2018, from http://www.businessofapps.com/data/uber-statistics/#1
Geib, C. (2018). In Uber’s Vision of the Future, Every Form of Transport Is Fair Game, Retrieved 25 October 2018, from https://futurism.com/uber-future-transport
Gobble, M. M. (2013). Big : The next big thing in innovation. Research Technology Management, 56(1), 64-66
Gooch, J. (2018). Masabi and Uber Announce First-of-its-Kind Ride-Sharing and Public Transit Ticketing Partnership, Retrieved 26 October 2018, from http://www.masabi.com/2018/04/11/masabi-and-uber-announce-first-of-its-kind-ride-sharing-and-public-transit-ticketing-partnership/
Faggella, D. (2018). What is Machine Learning?, Retrieved 22 October 2018, from https://www.techemergence.com/what-is-machine-learning/
Hill, J. (2016). What the Internet of Things Means for Your Business, Retrieved 23 October 2018, from https://www.bignerdranch.com/blog/what-the-internet-of-things-means-for-your-business/
IBM. (2018).The Four V’s of Big Data, Retrieved 20 October, 2018, from https://www.ibmbigdatahub.com/infographic/four-vs-big-data
Kanter, J. (2018). Wall Street banks tell Uber it could be valued at $120 billion if it goes public next year, Retrieved 22 October 2018, from https://www.businessinsider.com.au/uber-could-be-valued-at-120-billion-in-ipo-2018-10?r=US&IR=T
Kho, N. D. (2017). The state of big data. EContent, 40(1), 10-12.
Koetsier, J. (2018). Uber Might Be The First AI-First Company, Which Is Why They ‘Don’t Even Think About It Anymore’, Retrieved 25 October 2018, from https://www.forbes.com/sites/johnkoetsier/2018/08/22/uber-might-be-the-first-ai-first-company-which-is-why-they-dont-even-think-about-it-anymore/#5e3544c15b62
Konig, R., & Rasch, M. (2014). A database of intention. In Society of the query reader: Reflections on web search (pp. 16–29). Institute of networked cultures.
Kufel, K. (2017). How Big Data Is Influencing Uber and Transportation, Retrieved 27 October 2018, from https://www.passportinc.com/how-big-data-is-influencing-uber-and-transportation/
McDaniel, R. (2017). Rise of the Sharing Economy, Retrieved 21 October 2018, from https://thedisruptivefuture.com/rise-of-the-sharing-economy-7ebd6382ffe5
Richman, D. (2016). Q&A: Uber’s machine learning chief says pattern-finding computing fuels ride-hailing giant, Retrieved 22 October 2018, from https://www.geekwire.com/2016/uber-collapse-without-pattern-finding-computers-says-chief-machine-learning/
Resse, H. (2016). How data and machine learning are ‘part of Uber’s DNA’, Retrieved 22 October 2018, from https://www.techrepublic.com/article/how-data-and-machine-learning-are-part-of-ubers-dna/
Rouland, C. (2015). SMART CITIES: Weighing the risks and rewards of connecting communities. Risk Management, 62(10), 26-30
Scuotto, V., Ferraris, A., & Bresciani, S. (2016). Internet of things. Business Process Management Journal, 22(2), 357-367
Singh, H. (2018). The Internet of Things: IoT Solutions and its Benefits, Retrieved 23 October, 2018, from http://customerthink.com/the-internet-of-things-iot-solutions-and-its-benefits/
Strand, E. & Gilbertson, J. (2018). Bringing more data to Uber Movement with street speeds, Retrieved 28 October 2018, from https://medium.com/uber-movement/bringing-more-data-to-uber-movement-with-street-speeds-32da498329a7?lang=en-AU
Technopedia. (2018). Digital Revolution, Retrieved 21 October, 2018, from https://www.techopedia.com/definition/23371/digital-revolution
Twitter. (2018, October ). Come along for a ride with our new CEO, @dkhos. He’s in the back seat talking about improvements to the app and how, moving forward, he’s committed to listening. [Twitter post]. Retrieved from https://twitter.com/Uber/status/997522616805199872
Uber (2018). Sharing ETA and trip status, Retrieved 24 October 2018, from https://help.uber.com/en-AU/riders/article/sharing-eta-and-trip-status?nodeId=e1f8ed2b-c0e5-4456-9c73-552cf11c5581
Uber (2018). Uber and Venmo Team Up on a New Way to Pay, Retrieved 24 October 2018, from https://www.uber.com/newsroom/uber-venmo-team-new-way-pay/
Uber (2018). Sync your calendar, streamline your schedule, Retrieved 24 October 2018, from https://www.uber.com/en-AU/ride/how-uber-works/connect-your-calendar/
Uber (2018). Riding with Uber: Spotlight, Retrieved 24 October 2018, from https://www.uber.com/en-AU/ride/how-uber-works/spotlight/
Uber. (2018). Uber Movement, Retrieved 27 October 2018, from https://movement.uber.com/?lang=en-AU