Data is not oil. Anyone caring to read – and anyone caring about this topic should – the famous article in The Economist will quickly find some of the reasons why data is different from oil.
Data is not the new oil and is not even new. Data – the body of items of information according to the Dictionary or anything recordable in a semantically and pragmatically sound way according to Martin Frické (2008) - has been everywhere since we were able to record. Companies and governments have always used data to run their operations and perform their functions. The first attempts to build censuses happened a long time ago, commercial companies have been tracking shipments and recording revenues and expenditures since they started operating, and insurance companies were some of the pioneers in using statistics and actuarial tables to calculate premiums.
What seems to be new is the power in our ability to produce, collect, and use data for novel purposes, good and bad. This newness is intimately linked to advances in technology. First, a big part of our lives - the work we do, the things we buy or watch, the conversations and interactions we have with our friends and family – happen through digital tools that record this activity, easily transforming it into data. Many of the things that used to go unrecorded are now being registered and stored somewhere. And this process is not limited to the activities that we carry out while browsing websites. Through our smartphones, sensors and other self-tracking devices, a lot of our analogue life has become digital. In fact, the border between the digital and the analogue is closer to our ocean-expanding shorelines than to our refugee-secure borders. Second, companies and governments have become increasingly aware of the potential uses of this data and are actively investing in the infrastructures and tools to collect, process and use it. The growing interest and hype around big data and artificial intelligence are just reflections of this increased (financial, scientific, regulatory, media and commercial) attention to data.
The increasing pervasiveness of data, and with it the spree to generate and capture value out of it, has moved to a whole new level. Some of the largest companies by stock capitalization (Google, Facebook, Amazon…) are companies whose value rests in their ability to collect, process and monetize data. In almost every other sector, from industry to services, companies are striving to gain a competitive advantage through data analytics, but also making new products from the data they collect. In fact, while data may not be the core product or service they provide, in many cases data is the key enabler of their core business (e.g., Uber needs the data it gathers to be able to operate or an insurance company makes key decisions on premiums based on the data that it collects from consumers and other sources) or the data becomes another product line (e.g., a company that runs smart grids, but also sells the data on energy flows to other interested parties). There are also companies, particularly new ones, that have built a business on gathering data from novel sources and selling either data with various degrees of processing or data analytics products to third parties.
The age of data comes with its important set of challenges. Concerns about the widespread loss of our individual privacy to governments and corporations are no longer the worries of some libertarian geeks. The 2018 GDPR seems like an old piece of legislation three years later, and despite its influence within and beyond the EU, it has not prevented the permeation of surveillance as a routine practice by many actors. But security and privacy are not the only concerns that are emerging. There are now many accounts about the unethical results of data practices, including the entrenchment of social and racial inequities due to decisions about what data to collect, how to collect it, how to interpret it as well as by delegating some of these decisions to algorithms that may further deepen and solidify existing biases in the data that they use. Finally, there are challenges that relate to who owns and profits from the value generated from the data, and whether these models are the best ones not for those making the big bucks, but for the society overall. Underpinning a lot of these challenges is a profound debate about power. About who is making key decisions about the technological and regulatory infrastructures that shape our data landscape. Currently, at least with regards to the Internet, this landscape seems to be concentrated in a few companies that amass much of the data produced, acting as gatekeepers to many other actors.
Cities are a key site where these trends are being materialized, also in the sense of becoming corporeal: in the city, the digital takes a physical form. Cities are the absence of distance. They are, by definition, a concentration of activity. Density is one of the core characteristics of urban agglomerations. A lot of the people browsing the internet are doing so while sitting in their apartments located in cities. Many other urban activities are being recorded through restaurant reservation systems, metro turnstiles, private vehicles’ GPS, energy grids, water meters, smart building applications, museum entrance booths, street sensors, store cameras and cashiers or bank ATMs. Individualized data generated from smart phones and watches that can now be geolocated is also predominantly generated in cities, where the majority of those individuals live. Local governments are also generating and collecting data through different means and from different sources: directly through their regular operations and delivery of services, from sensors and other automated technologies, and from providers and contractors that deliver services.
The generation of large quantities of urban data has the potential to transform how cities are built, run, and lived. All the major recent urban innovations derive or are connected to this potential. We can highlight four. The Smart City movement promises to improve the functioning of cities through the application of technologies to improve the efficiency of running traffic, buildings, streetlights, etc. The industry has been estimated in the billions – if not trillions – of dollars and attracts many companies and evangelists. Platform businesses such as AirBnB, Amazon, Uber or Deliveroo are also transforming cities thanks to the marketplaces generated through digital technologies. Many of these marketplaces, although digitally established, operate in urban settings, and some of them need the density of consumers and providers existing in cities to be viable. One of the latest waves of urban technological dreaming are Autonomous Vehicles, which have the potential to fundamentally reshape not only urban mobility, but also the built environment and the configuration of space in cities. Finally, governments are undergoing a fast digital transformation which has made some city governments frontrunners in the use of analytics and the creation of institutional practices such as Chief Data Officers.
City governments are playing an increasingly important role in technology and data debates. In contrast to many federal or state agencies, cities do not just regulate or issue policies. They provide services and run operations on a daily basis. In many countries, cities are being much more agile than state and federal governments in adopting new technologies and in using data to perform their functions. Cities often keep abreast of new technologies and are early adopters among their government peers. The regulations and policies issued by city governments can also have a great impact on the innovations that are occurring at the local level. For example, cities can issue licenses and establish requirements for AirBnB to operate in their city by establishing stay limits, quotas to listings, etc. A lot of the regulations of ride-hailing and ride-sharing operators also fall under the jurisdiction of cities. With regards to data, cities are experimenting with new governance mechanisms, regimes and tools to respond to some of the challenges posed by data described above. All this makes cities interesting places to explore and understand how data is shaping our societies and what can city governments, and frankly, all of us, do about it.
This and other questions that I will explore in this Newsletter are:
· How important is the data phenomenon? How much data is being generated and used? By whom and for what purposes?
· Is this increasingly importance of data really (re)shaping cities? How exactly?
· Overall, are we better off? Or worse off? What are some of the trade-offs posed by the pervasiveness of data?
· What is the impact of all this on people? And what can city governments do about it?
· What are the models of governance that deal with these trade-offs? How? How successful are they?
· What conditions are needed for governments to achieve these outcomes?
· What capabilities do they need?
Although I will try to investigate these and other questions, I will do so in an idiosyncratic order. Sometimes I will focus on an aspect that seems more tangential, for example, provide commentary on a book on theories of bureaucracy or digital era government. The reason for this is that I think about this Newsletter as a travel log of my PhD journey, which I am just starting. This also means that, at some point, I will also need to move from general debates to studying particular cases, for example the regulations of data at the EU level, or the experiments with tools to govern micro-mobility data in a city or a set of cities. My hope is that some of these will still be interesting and relevant for people curious about the broader topic, insightful for those focused on the narrower aspects, or at least will be enjoyable to read. Most importantly, I hope we will learn and find the process something it was worth investing our time on.