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In the second of three posts for the Salzburg Questions for Law and Technology series, Anastassia Lauterbach asks about enabling technologies that can be understood and invested into, ensuring any business, non-profit, and municipality can create its own data market

This article is part of the Salzburg Questions for Law and Technology series by the Salzburg Global Law and Technology Forum

Big Tech has already proven the significant value of data. We need to talk about enabling technologies that can be understood and invested into, ensuring any business, non-profit, and municipality can create its own data market.
 
Data markets on a decentralized ledger could be a practical response to invite more businesses into the data economy. They connect buyers and sellers of datasets. They enable traditional companies to benefit from the expertise of artificial intelligence (AI) practitioners and software developers without employing them in their organizations. They open up innovation, bringing up startups, research labs, and amateur developers into the equation without setting up a captive venture capital company.
 
Decentralized data markets are realizable once we understand data as a token. The term "token" is simply a metaphor. Contrary to what the metaphor might suggest, a token does not represent a digital file sent from one device to another. Instead, it manifests as an entry in the ledger that belongs to a blockchain address. Only the person who has the private key for that address can access the respective tokens using a wallet software, which acts as a blockchain client.
 
Unlike distributed databases, where data is distributed but managed and controlled by one single entity, blockchain networks allow for distributed control. Different people and institutions that do not trust each other share information without requiring a central administration.
 
What do I mean when I mention ledgers? A ledger could be a spreadsheet in the cloud. Think of cloud applications like Google Docs, where everyone can access and edit a file simultaneously. But, as opposed to Google Docs, where that file is stored centrally on the Google servers, the ledger of a blockchain network is a document that is not held centrally. Instead, each node of the network keeps an identical copy of the same file at all times, with temporary exceptions every time a new block is created.
 
A new set of protocols is required to define how the network participants interact with each other. It includes under which conditions sending tokens from A to B is valid, the economic rewards for validating transactions with a cryptographic token, how to reference identities and sign transactions, and who decides over network upgrades.
 
Today, businesses can already benefit from the data-sharing Ocean Protocol, developed by a non-profit – the Ocean Foundation. This foundation created an Ocean Market, which provides opportunities to bring together those in need of specific data and data owners.
 
Once again, Ocean embraces the data-as-a-token concept. I will spare you the technical details on Ocean architecture. I am just driving your attention to the fact that your company, non-profit, or municipality can build its own data market using Ocean Protocol without being afraid that your data ownership might be questioned or taken from you. At the same time, Ocean utilizes market forces of developers not employed by Big Tech. It enables these developers and machine learning engineers to work from whatever place they prefer.
 
There are a couple of shortcuts, which will speed up the development of your own data market. First and foremost, your business requires an inventory of your data assets. You have to understand what kind of data matters most and why. Peter Norvig at Google found that very different algorithms perform virtually the same for a given problem with large enough data.
 
Second, you need to invest in the quality of this data. In AI startups, most efforts are going into cleaning and - for use cases - labeling data. There is an industry out there to help your business to clean data. However, it is worthwhile to employ at least one data engineer (if you are a very small business) capable of structuring processes and partnerships around data and future data products.
 
The utilization of outside developers through your own data market does not mean you shouldn't invest in data technologies. You might want to look into how to work with small data. Small businesses don't have the luxury of accessing an insane amount of data. Besides, the noise in the large data sets can often overwhelm the critical signals that relate to the problem at hand. Just imagine trying to hear a vital conversation on a noisy train!
 
In some cases, such as detecting rare diseases, there isn't enough data in the first place, so the missing data in these large corpora presents a sort of confirmation bias that can only mislead. Unfortunately, only skilled data scientists can apply techniques to work with small data. In this context, small businesses should cooperate with universities and research labs in their area, investing in creating an ecosystem of data-minded people and institutions around them.

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Anastassia Lauterbach is an international board member, technology founder and entrepreneur. She serves as a non-executive director for Dun & Bradstreet, easyJet PLC, and Wirecard AG. Anastassia is member of the Advisory Council Next Generation Directors for NASDAQ and of the Diligent Institute of Corporate Governance. Anastassia serves on the advisory boards of the Ocean Protocol, a private global blockchain infrastructure and intellectual property company; Cogitanda AG, a private European cybersecurity insurance broker; and TM Forum, a non-profit global association of telecommunication companies and their vendors. Previously, Anastassia served as senior vice president and executive vice president at Qualcomm, Deutsche Telekom, and Daimler Chrysler. She started her professional path at the Munich Reinsurance Group and McKinsey & Company. She is CEO and founder of 1AU-Ventures and currently advises several U.S. and European based artificial intelligence (AI) and cybersecurity companies and investment funds, including Evolution Partners and Analytics Ventures. She trains boards in cybersecurity and cognitive AI and robotics-related technologies and their links to corporate governance. She advises the International Telecommunications Union, a United Nations organization, on AI policy and governance. Her book The Artificial Intelligence Imperative: A Practical Roadmap for Business has sold 35,000 copies. Anastassia has a Ph.D. in linguistics and psychology from the Rheinisch Friedrich-Wilhelms Universität Bonn and a diploma in linguistics from the State Lomonosov University, Moscow. She is a Fellow of Salzburg Global Seminar.

The Salzburg Questions for Law and Technology is an online discussion series introduced and led by Fellows of the Salzburg Global Law and Technology Forum. The articles and comments represent opinions of the authors and commenters and do not necessarily represent the views of their corporations or institutions, nor of Salzburg Global Seminar. Readers are welcome to address any questions about this series to Forum Director, Charles E. Ehrlich: cehrlich@salzburgglobal.org