What is a “data economy”? The idea is a broad concept that generates excitement and promises immense green pasture opportunities. But, the specifics are often lost in conversation.
The Data Economy
Data is a valuable tool, a utility even, with potential for a variety of high impact applications in the industries of healthcare, financial services, marketing and advertising, retail, insurance, oil and gas, media and entertainment, and food and beverage, just to name a few.
The word “economy'' implies the existence of a market based on supply and demand, with some good(s) being transacted. For the data economy, the production of new data (the supply of goods) is currently taking off at a rapid pace. Simultaneously, there is an increasing demand for that data across various industry verticals.
Transacting Data in the Data Economy
Transactions, however, are uniquely complex in a data economy for a variety of reasons. Transacting data creates a copy of the dataset each time it changes hands. From an economic perspective, this is a significant issue.
Take, for example, a car salesperson. If each time a car was sold, the salesperson cloned the car on their lot and sold the clone, the transaction would increase the supply of cars. Maybe that is not such an issue if the dealerships are the only ones who can do this “cloning”, but what if the person who purchases the car turns around and sells it to someone else? The car would be cloned again!
Now, three copies of the same car exist, supply has tripled, and demand has remained constant. It does not take an Economics Ph.D. to imagine how quickly cloning devalues the car. When supply increases more rapidly than demand, scarcity is impossible. Over time, the price of cars will settle at zero.
In building the data economy, the players involved must be wary of the supply duplication issue. Although car cloning is (currently) impossible, duplication is a genuine concern for data transactions. If data is replicated during every transaction, it will eventually become a valueless asset.
Data Privacy Laws & the Data Economy
Another unique issue pertaining to the data economy is the emergence of strict data privacy laws, which tend to silo data in place. Legislation like Europe’s General Data Protection Regulation (GDPR) and regional data localization regulations are put in place to protect individuals’ personal information. Although important for retaining privacy, these laws come with side effects. Namely, limiting opportunities for data sharing between parties and impeding global organizations from accessing their own data.
So, how does an economy centered around transacting data operate when the data itself cannot, in many cases, move?
Taking a broader view, what is really being demanded is not the data itself. Rather, the demand is for new and actionable knowledge, gained from analyzing data. After all, the reason data is used is to gain insight into the way the world, or some sliver of the world, operates. When insights enable humans to make more informed decisions, tangible value is assigned to the data behind those decisions.
Reframing the Data Economy
Let us take another look at the data economy in the context of transactions and privacy laws.
Perhaps the idea of a “data economy” can be reframed entirely. Suppose actionable knowledge and insights can be gathered from data without moving it or sharing the raw data. In that case, data owners will have more control over supply, and data maintains its value.
Assuming this is possible, now the “buyers” (the demand side), pay for the ability to perform operations to gain insights and knowledge from a dataset, not for ownership of the data itself.
This reframed version of the data economy excludes the necessity to transact (clone or copy) data. Data suppliers, instead, are able to charge on a per-use basis. If done properly, this approach reduces liability concerns for both the data suppliers and the data buyers. Concern for the possibility of a private data leak is minimized, and the buyers no longer assume the responsibility of being “caretakers” of sensitive data.
Building the Infrastructure for a Data Economy
Bringing this data economy to reality will, of course, require the proper infrastructure.
Specifically, the infrastructure must be engineered to address two questions. 1. How can technology be used to “free-up” data for high-value, innovative purposes? 2. How can the construction of a global data economy take place when laws and regulations bind data regionally?
Teams of engineers representing companies large and small around the world have put their minds together to develop reasonable solutions to address these pressing questions.
As a result, some promising technologies have emerged, including secure enclaves, tokenization approaches, homomorphic encryption, differential privacy, and federated learning, among others. However exciting these technologies may be, they have largely struggled to become practical. They either require data to be physically aggregated, require specific hardware to operate, eliminate important data fields from computation, or add significant computational overhead to the process.
Recently, TripleBlind, a Kansas City-based company, launched to solve these challenges while addressing the downsides of using the incumbent approaches listed above. Its team of engineers and cryptographers have made breakthroughs on top of thirty years’ worth of trusted technologies to enable insights to be gathered from complete, unaltered datasets sitting safely in place behind an organization’s firewall.
The TripleBlind solution uses a heavily verified one-way encryption technology that enables data to be used by third parties without transferring raw data. Importantly, no decryption key ever exists, meaning the encryption is irreversible. Counterparties, or data consumers, can run any operation approved by the data provider without ever seeing the raw data. Additionally, TripleBlind provides protections for the data consumer, in the case that their algorithms contain valuable intellectual property. All this occurs without significant compute or speed costs, making the technology practical and scalable today.
In using the TripleBlind tools, data consumers and providers can interact in a peer-to-peer way, without the need for establishing trust or spending significant amounts of time and resources negotiating business and legal agreements.
Organizations can simply make any type of data or algorithm, including machine learning models, available for others to use for a fee and retain the original asset, never replicating it.
This approach enables entire datasets to be computable in encrypted space, eliminating expensive de-identification tasks. It is also entirely software-based and API-driven, with no specific hardware requirements, and operates wherever your assets (data or algorithms) are stored, whether on-premises or in the cloud.
With this type of technology, it is possible to create a fully functioning economy centered around data, which both preserves the value of the data and unlocks its usefulness for the consumers. But, this “data economy” will not involve transacting data itself - it will be centered on transacting the right to gain new, actionable knowledge from valuable datasets.
Mitchell Roberts is the Director of Product Marketing at TripleBlind, which provides the most complete and scalable solution for privacy-enhancing computation, allowing organizations to fully and securely leverage the value of sensitive data. A 2021 graduate of Washington and Lee University, Mitchell received a B.S. in integrated engineering with computer science and economics. He brings a blend of technical understanding and writing skills to TripleBlind's Marketing and Sales teams, translating technical concepts into language that resonates with people facing real-world business issues. Mitchell has data science training, with experience building neural networks from scratch, but equally enjoys the creativity and strategy involved in marketing technology. Outside of work, Mitchell enjoys the outdoors and creating art, and is an avid skier, ultramarathon runner, rugby player and youth soccer coach.