The New Economy: A Data Revolution
In traditional economics courses, students learn about three different factors of production: capital, labor and land. Each one of these factors possesses their own unique characteristics that collectively work together to produce goods and services for economic profit. However, as we sit in the midst of a paradigm shift from a traditional physical economy to one that exists in large part digitally, a new form of capital is becoming increasingly recognized as another and extremely significant economic factor of production: data. Alex Pentland et al. discuss the ongoing transition into the digital economy, along with proposals to capitalize on making it as user-centric as possible, in their book Building the New Economy: Data as Capital. Their proposals, along with high levels of consumer buy-in into them, are going to prove not only necessary, but integral to the functionality and success of the new data economy. However, the challenges lie in achieving such buy-in: how do you convince consumers to agree to the concept of sharing all of their private information for the communal good? How do you compel corporations to release their growing authority over the data market?
Throughout their book, the authors discuss the ongoing evolution of the data economy and their own responses to growing issues regarding the current concentration of data ownership. In considering the ever-growing importance and influence of data on our lives, Pentland et al. contemplate various ideas, some big picture and some in fine detail, and introduce a blueprint in their call to promote more user-centric ownership of data and return the value earned from data back to the mass of people that supply it.
The authors liken the situation arising in regards to data ownership to similar situations that occurred in the late 19th and early 20th centuries when the economy was transformed by the birth of industrialization and consumer banking. These shifts in economic structure introduced exploitations that were ripe for the picking and corporations took full advantage, which is what led to the creation of labor and credit unions to protect the rights of workers and consumers. When industrialization took rise in the late 1900’s, the working conditions of laborers worsened continuously for the purpose of profit of steel and oil monopolies. The dramatic shift in the conditions and status of blue collar workers created by the rise of industrial America gave way to an equally dramatic response in the formation of labor unions — some of which are still present today — to protect the rights and increase the power of laborers in the workplace. Unions did this by “consolidating a majority of a factory or industry’s workforce into a single body” in order to collectively direct the workforce of a single industry thus empowering employees to have more influence in labor decisions (Bischoff). In the midst of the newest paradigm shift, exploitations are yet again ripe for the picking. As it stands, the value being generated from individuals’ personal data that is being aggregated for useful analysis is not being sufficiently returned to the individual. The concentration of data ownership, and thus profits from data analysis, are centered around groups of corporations such as Google, Apple, Facebook, etc., leading to the worsening of conditions of present-day consumers in similar ways to that of industrial laborers. Only, instead of working conditions being made unbearable for laborers, consumers’ ownership of their very own private and personal data is being threatened by corporate exploitation.
In response to this particular paradigm shift, Pentland et al. discuss the existing concept of platforms called “data exchanges”. Data exchanges are platforms which collect and coalesce huge amounts of data from any number of different sources, provide the collected data to a third party for processing and analysis, and sell the insights gained from the processed data for profit to corporations and businesses. The notion of data exchanges closely resembles a structure that accomplishes the authors’ goal of creating a user-centric platform for data sharing; however they take it a step further and propose a similar concept to that of credit and labor unions they designated “data cooperatives”. Data cooperatives are described to be “voluntary collaborative agreements by [collections of] individuals that their personal data may be used to derive insights for the benefit of their community” (Pentland 273). In this sense, data cooperatives are membership-based data-sharing platforms that are independent of third party data collection affiliates, which allows for all of the resources (i.e. data) being collected for use from its members to be widely and wholly accessible to all of its members. These cooperative platforms are intended to be owned and controlled entirely by its collective members and their elected officials, thus creating a system in which all of the authority regarding data collected and analyzed lies within the community of members inside of the organization. There are some small-scale existing examples of successful data cooperatives within our economy today. Salus Coop is a platform that allows users to securely provide, store and use their healthcare information how they need to: sending records to verified medical professionals, insurance agents, etc. Platforms such as this create ecosystems for their users to engage with services that they need using the data they have at their disposal. Efforts such as organizing and establishing data cooperatives can prove invaluable in working to return value generated by consumer data back to the consumer whether that be in monetary form or in insightful and useful analysis, thus centering data ownership back towards users instead of select parties.
In addition to proposing the implementation of such platforms as data cooperatives into the new data economy, Pentland et al. also suggests adopting a structure to create interoperability within the system so as to maximize the potential value output by a user-centric data economy. Interoperability is the concept of a distinct group of systems to work interchangeably with one another to exchange, use and generate value from one another’s production. In the context of the digital data economy, interoperability is largely associated with the blockchain, or the digital ledger on which the whole digital economy is based. Through the definition provided by the National Institute of Standards and Technology (NIST), Pentland explains an interoperable blockchain as “a composition of distinguishable blockchain systems, each representing a unique distributed data ledger,…, where data recorded in one blockchain is reachable, verifiable and referenceable by another possibly foreign transaction in a semantically compatible manner.” (Pentland 336). This could look something like distinct data cooperatives externally working together to share data amongst their different platforms or perhaps it looks like greater and larger data exchanges being established for data coalescence. In either case, the goal of an interoperable data economy is one in which distinct entities can and will work together to derive the greatest potential amount of value from all of their users’ data. Data is most useful when it is collected and used in the aggregate, so creating a system in which as much useful data can be shared as possible is the clearest path to achieving the maximum potential of the data economy.
While there are notable privacy concerns to be considered with an interoperable system of data platforms exchanging users’ information, there are ways to promote interoperability and greater functionality without compromising user privacy and security, or at least with providing consumers with better incentives. Much of this comes with centering data ownership around the users. With a system in place that grounds data ownership and authority with the supplying users, it is reasonable to believe that there would be an increased willingness and desire by users to share their personal data into such an expansive environment. In essence, with a greater potential to get value out of it, users would likely be more interested in sharing their personal information into an interoperable system of data platforms so as to maximize production.
Something that Pentland and his fellow authors fail to consider regarding their proposed architecture of the data economy is the reaction that the corporations already with established authority over data ownership will have. Entities in the data market that have already taken a hold, such as the FAANG companies, will certainly be less inclined to buy into this new idea of the data economy that resembles more user-centricity and shouldn’t be expected to sit idly as it happens. On paper, the shift into a data economy without a slew of federal regulations already established is the perfect recipe for such companies to create exploitations for mass profit. However, looking at the bigger picture, a lack of trust and transparency is evident between the users and these tech giants, and it is already and will continue to create long-term limitations to just how successful and impactful the overall data economy can be. This lack of trust and transparency will suffocate the growth of the data market before it can ever even begin to flourish as users constantly grow more wary of providing useful information to data platforms. Such a result would be a dramatic setback to the growth of our society as much of future technological potential is grounded in this concept of a digital infrastructure supported by data and data analytics.
Pentland et al. notes that not all of the value derived from data is going to be monetization, which will be a weird idea for some parties to wrap their heads around and thus buy into. In particular, the world is staring at a tool that can be used to dramatically improve the living conditions of people worldwide. While corporations have really only ever taken advantage of data as a resource for profit, the real incentive for communities is the potential to vastly improve day-to-day operations and living conditions with data. Whether this be in the form of systems that can more economically and efficiently distribute resources such as clean water and food to people or improving labor conditions by optimizing manufacturing and production processes, the potential for growth from the data economy is limitless. Pentland is not the only one who sees the value in what is referred to as “data-driven decision-making” (DDDM) — in writing on the advantages of DDDM, Harvard business writer Tim Stobierski notes that “highly driven data organizations are three times more likely to report significant improvements in decision making compared to those who rely less on data” (Stobierski). Employees are also already beginning to note the improved working conditions produced by data-driven decision-making. A Harvard Business Review study of almost 650 executives, managers and representatives from all industries and geographical regions reports that 78% of corporate-wide respondents have seen improved productivity from data analytics and 70% saw improved financial performance among other things (Harvard Business Review). These represent first-hand accounts of data working to directly produce value for a corporation that is not money. There certainly is and there certainly will continue to be a place for the monetization of data, especially as we shift into an economy operated in electronic transactions and supported by electronic currencies and digital ledgers. However, the largest source of potential value in a data economy lies in the insights gained from the analysis of aggregate data to constantly improve living conditions and general day-to-day operations and management.
The future is a shared data economy — which means one in which data is provided from all parties and the value derived from it is distributed amongst all parties. In order to completely capitalize on the revolutionary concept that is the data economy, it is absolutely essential that all parties involved, both the tech giants whose authority and power over the industry will need to change and the users who will need to supply the resource (data), fully buy into the system. Those looking to set themselves up for success in the new data economy should read Pentland’s Building the New Economy: Data as Capital and prepare to buy into his blueprint for user-centric data ownership.
Works Cited
Bischoff, Adam. “The Rise of Unions.” Gustavus Adolphus College Library, http://homepages.gac.edu/~kranking/DigitalHistory/HIS321/HIS_321/W1.html.
Pentland, Alex, et al. Building the New Economy: Data as Capital. MIT Press, 2021.
Stobierski, Tim. “The Advantages of Data-Driven Decision-Making.” Business Insights Blog, 26 Aug. 2019, https://online.hbs.edu/blog/post/data-driven-decision-making.
“The Evolution of Decision Making: How Leading Organizations Are Adopting a Data-Driven Culture.” Harvard Business Review (N.d.)., https://scirp.org/reference/referencespapers.aspx?referenceid=2941384.
