The rapid and impressive rise of Uber and Airbnb in revolutionizing the car service and lodging industries is a testament to the rise of the “sharing economy.” In the sharing economy, assets that are traditionally owned and utilized by a single person for a single purpose, such as a personal car or personal apartment, are repurposed for use by those willing to rent or borrow it at a price. But this sharing also requires data. That data comes from digital platforms that connect buyers and sellers. Digital platforms enable such commerce. Such a repurposing of assets is timely and speaks to the general abundance in supply found in various asset sectors. How many cars do we really need if cars were shared? My guess is we will find out in the coming decades, and automobile manufacturers will not be pleased with the answer. Parked and idle cars in personal garages will be uncommon, and instead will be dispatched for sharing. Today, cars are summoned with a mobile app. In the near future, the car may even arrive autonomously without a driver, with the seats adjusted to your liking and the car playing your favorite music (I hope).
With Airbnb, the challenges of finding a room during a peak period are reduced. In theory, any and every room can now be rented. It challenges the lodging industry and the tax systems built on hotel stays too. Regulation is challenged by the sharing economy. It pits entrepreneurs against big business and governments. Society roots for the entrepreneur. The entrepreneur has something else – data!
As exciting as we find the sharing economy, it is important to remember that it is predicated on a sharing of data. This is important to its success now and in the future. Drivers and riders of Uber and similar ride services are empowered because precision in rider (demand) and driver (supply) information is captured and shared through a marketplace, so that each side can make rational economic decisions, such as a driver driving a few more hours to accommodate rider demand, or a rider accepting a surge price during a peak period. This sharing is largely enabled by the ubiquitous mobile platforms that are changing how we do business.
For Airbnb, the sharing of information is enabled by the low cost to share photos, videos, and reviews, and also by the ability to track local hotel prices algorithmically. Such offerings have inevitably raised consumer expectations on the availability of such features. All of that requires the sharing of data, most notably before any commerce is conducted. The data is given away! For Uber and Airbnb, the data is there to be shared– in particular to be shared through data products, customized to each user.
As many firms aim to capture value from the ensuing Big Data tidal wave, sharing data may be the right strategy. It may not seem obvious at first pass, but sharing data can be more valuable than keeping it proprietary. For Uber it is worth about $41 billion and for Airbnb, it is worth $10 billion. Good thing, they gave away data!
The success of sites like Uber and Airbnb offers some important lessons in growing and managing data exchanges and the bold strategy of giving away data in the sharing economy. Their success in creating data, analyzing data, and leveraging that data in novel ways offers lessons for all firms:
Data exchanges have become increasingly important in many markets. Many exchanges like eBay and Amazon are now accessible on mobile platforms. Customers have shown a strong preference for data exchange use on mobile platforms. Leverage the movement to mobile apps to provide vicinity information. Increase customer participation on mobile apps by refreshing data at a high rate, making frequent consumer visits worthwhile and valuable.
Relevant data comes from many sources and formats. Fusing data from public sources with proprietary data improves convenience for consumers and creates a economic benefit in that multiple sites do not need to be accessed. It promotes scale in the number of consumers and achieves increased precision in data too. Data is also not just numeric. Develop platforms that take in text, photos, video, and even audio. Develop rankings and indices that can help organize non-numeric data. Tagging and descriptions of non-numerical data are necessary to finding and accessing such data. It is proved critical for measuring drivers, rooms and all of which is now in the sharing economy.
Having lots of data requires understanding the rank, severity, or importance of any specific entity. Indices help bring scale and communicate these natural inquiries. Just as a temperature scale allows us to understand severity and make relative comparisons, develop indices that allow users of your data to do the same. Uber and Airbnb brought measurement to drivers and the experience of users of them.
In general, customers do not want data. Customers have market questions. They seek answers to those questions. Buyers in the sharing economy want information of the provider so that their risks are reduced. It is absolutely important that data products be created to solve the problems facing customers and answer the questions that they have. The data products permit monetization and marketplaces. For the sharing economy, giving away data reduces the risk in making a purchasing decision, so be ready to give away data if you are in the sharing economy!
Airbnb and Uber emerged as leaders quite quickly. This was possible because they achieved scale quickly. That scale was enabled by data creation and data sharing. It included more drivers and properties before the other sites. The marketplace rewards firms that achieve scale first. With more data, more innovation and more data products are possible. With that comes more users, and with more users comes more opportunities for monetization.
These important implications of Big Data use are developed in greater detail in my recent book, From Big Data to Big Profits: Success with Data and Analytics. The book examines the evolving nature of Big Data and how businesses can leverage it to create new monetization opportunities. Using case studies on Uber, Airbnb, Apple, Netflix, Google, LinkedIn, Zillow, Amazon, and other leading-edge users of Big Data, the book also explores how digital platforms, including mobile apps and social networks, are changing customer interactions and expectations, as well as the way Big Data is created and managed by companies. Companies looking to develop a Big Data strategy will find great value in the SIGMA framework, which assesses companies for Big Data readiness and provides direction on the steps necessary to get the most from Big Data.
Professor Walker provides keynote talks, seminars presentations, executive training programs, and executive briefings.
Recent talk topics enjoyed by clients have included:
“From Big Data to Big Profits: Getting the Most from Your Data and Analytics”
“Leveraging Artificial Intelligence and Automation at Work”
“Winner Take All – Digital Strategy: From Data to Dominance”
“Success with an Inter-Generational Workforce: From Boomers to Millennials”
“FinTech, Payments, and Economic Trends and Outlooks in Consumer Lending”
“The World in 2050: Risks and Opportunities Ahead”
Exceptional executive training programs have included:
“Digital Disruption, Automation, Analytics, Data Science, the IoT, and the Big Data Wave”
“Master Course on Operational Risk: Measurement, Management, Leadership”
“Complete Course in Risk Management: Credit, Market, Operational, and Enterprise Risk”
“Cyber-security Training: Prevention, Preparation, and Post-Analysis”
“Managing Your Brand and Reputation in a Crisis.”
“Strategic Data-Driven Marketing”
“Enterprise Risk Management and the CRO”
About Russell Walker, Ph.D.
Professor Russell Walker helps companies develop strategies to manage risk and harness value through analytics and Big Data. He is Clinical Professor of Managerial Economics and Decision Sciences at the Kellogg School of Management of Northwestern University.
His most recent book, From Big Data to Big Profits: Success with Data and Analytics is published by Oxford University Press (2015), which explores how firms can best monetize Big Data. He is the author of the text Winning with Risk Management (World Scientific Publishing, 2013), which examines the principles and practice of risk management through business case studies.