Data Management in the Web 3.0 Era: An Interview with Greg Tulquois

Greg Tulquois (DLA Piper, France)

From headlines on ChatGPT and the like to applications powered by machine learning, Web 3.0 seems to be all the rage these days. What seemed like technological advances reserved for niche industries, R&D, or crypto nerds—such as AI, machine learning, blockchain, and related decentralized ledger technologies—is now expanding dramatically into many aspects of the daily life of companies and individuals alike.

One of the key claims of the proponents of Web 3.0 is its decentralized nature and the impact this has on each person’s rights, assets, and data. What does Web 3.0 really mean when it comes to the ability to manage one’s data? From the perspective of the businesses operating in the Web 3.0 ecosystem, what are the ways to ensure compliance and limit risks?

Greg Tulquois leads the global Commercial and Contracts practice at the international law firm DLA Piper.  Mr. Tulquois, who is based in Paris, France, advises businesses on the structuring, negotiation, and optimization of contractual ecosystems dedicated to e-commerce, as well as the launch of new digital businesses. In the most recent episode of INTA’s podcast, Brand & New, Mr. Tulquois discusses the challenges involved with managing and protecting data in the Web 3.0 era.

Below is an excerpt from Mr. Tulquois’s Brand & New podcast interview. It includes some minor edits to improve readability.

Before digging into the fascinating topic of Web 3.0, let’s start by discussing what data management means, from the perspective of a brand owner that manufactures, markets, advertises, sells, and fulfills. Could you please give us an overview of why data management is critical to any business today?
We find ourselves in a period where online transactions have accelerated and been amplified even further. Supply chains are still an issue in several sectors, and sustainability and ESG (environmental, social, and governance) are the most important developments that brand owners must address in order to satisfy investors, shareholders, regulators, suppliers, customers, and employees.

If we start with e-commerce, broadly speaking, still and maybe more than ever, it presents a number of concrete opportunities: to digitize further in order to sell online more. Customers are more and more demanding on quality of service and seamlessness of the digital and physical experience. And the proverbial omnichannel approach is still very much what everyone in the consumer world is aiming for. But, in order to get there, brand owners have to reach a highly mature and sophisticated level of digital operations. They have to continue to be, or must become, very credible and trusted e-commerce operators. And they have to do this in the context of a still-troubled supply chain and of very demanding sustainability requirements across the board.

How do you do all this? You focus on data: customer data, consumer preference data, retargeting data, environmental data, product characteristics data, contracts performance data, and operations data. A key aspect of data in e-commerce is the management of operational data. It’s very important to focus on data in a way that’s broader than personal data privacy, security, and restrictions on the use of certain personal data or restrictions to profiling. The key is to master performance data, ensure that you have a robust and resilient supply chain, check and tighten your contracts, and make use of contract performance data to incentivize your suppliers to perform.

This, of course, can be done through different mechanisms. The key is also to address customer expectations for relevant, personalized, available, and immediately delivered products. That probably requires a more efficient combination of physical and digital channels, and ideally an omni-channel approach with seamless showcasing, ordering, paying, picking, delivering, and returning across channels, and for that, you need state of the art logistics operations.

But beyond these process improvements, I can show you that perhaps it is the very business model that should be looked at and adjusted: If your e-commerce is done through distributors, you master a lot less data than if you sell personally. If you sell personally, your command of relevant data will be more or less extensive and exclusive, depending on whether you sell on owned and operated sites or apps, or you use a commissionaire (undisclosed agent), or you sell through a third-party marketplace.

We have to add ESG data that is attached to operations. It’s a significant project in itself to collect, analyze, and manage all aspects of relevant ESG data regarding scopes for emissions, but also components, component data, materials, traceability data, waste data, social impact, D&I, and transparency. Data governance policies themselves become data of paramount importance as part of ESG policies and obligations.

Many Web 3.0 proponents claim that it constitutes a true data revolution. How is Web 3.0 changing the dynamics of data use, flows, and ownership?
Web 3.0 is a bit of a buzz word that was crafted essentially for marketing purposes in order to promote a new approach to online operations, transactions, and interactions.

But there’s a tech reality behind it: decentralization of data. Web 3.0 uses blockchains and distributed peer-to-peer networks. That means that data will no longer be hosted on company servers but by distributed ledger technology like blockchains. That means that certain key data, including personal data, will no longer be in the hands of those companies that operate the apps and the platforms. Data is stored over a highly distributed and decentralized network, and not on a single server or network of servers. Exchanges are authenticated automatically on a blockchain channel. That means that you’re not transferring control over your data to anyone in particular. You no longer need a company to be a trusted intermediary. This is what we now call “trustless” environments. It actually means “trustful” because the automated distributed ledger brings automatic and failproof trust. And this allows us to get some sort of ownership over data sets by tokenizing the data.

The most active tech players promoting Web 3.0 are companies that master distributed ledger technologies: that’s cryptocurrency and blockchain platforms primarily. This technology enables:

• Control over your own data;
• “Ownership” of the data: meaning association between you and the data is established and may not be tempered with;
• Accuracy of data; and
• Security of data.

That doesn’t mean that there will be a creation of IP rights in data per se, but there’s control and transparency regarding all the data placed in the system, which may not be tampered with.

From those angles, this is a revolution, or at least a major shift in paradigm because the infrastructure that is being used and the nature of the technical and legal interactions with Internet players are changing. Users are more than ever at the center of the relationship, and they become genuine customers and no longer a product themselves.

But I think Web 3.0 is actually even more than that because of how natural language programing of AI allows automated delivery of output by the Internet. The Internet used to be a supplier of raw data and now starts adding value through automated and rapid analysis and delivery.

Let’s focus on the impact of Web 3.0 technologies and services from the perspective of data owners and brand owners. How do decentralized services such as the metaverse or ChatGPT change the ways “data owners” and data subjects understand and manage their data. Will brand owners have opportunities to continue interacting with customers?
OK, so if we’re talking about the metaverse, we have to clarify that the metaverse is not the same thing as Web 3.0, and that ChatGPT is also different from Web 3.0.

Web 3.0 is the tech infrastructure that enables certain key services such as the metaverse and, to a certain extent, certain AI services. Web 3.0 will develop in the context of the metaverse and AI and vice versa.

The metaverse for B2B promises some very interesting process improvement for 3D imaging and remote working—for example, on digital twins, for health care imaging, or to provide collaborative work on complex aspects, and more accuracy in the analysis.

Brand owners are still probably highly interested in figuring out the extent to which the B2C metaverse is anything more than some gamified reality, virtual reality, or augmented reality allowing some immersion in a parallel world that is pretty much akin to videogames and where some sales, online sales, or fully virtual sales, or a combination of physical, digital, and virtual sales can take place. There can be some interesting so-called phygital applications. For example, when a system checks your cortisol levels or infers your cortisol levels from an explanation of the mood that you’re in, and then proposes to you a beverage that is tailored to your needs, which you can then buy, that is a fairly concrete “phygital” experience.

That being said, we’re now seeing that metaverse projects are being somewhat deprioritized for 2023, to the benefit of AI.

When the metaverse emerges in a significant B2C format, it will probably mean a multisensory experience and therefore a collection of a number of data, including emotional and physiological data. It is unclear how the use of all that new and complex data will be monitored.

More generally decentralized systems present a systemic issue when it comes to regulating the collection, processing, and the circulation of data because data privacy and security regulations were built for the Web 2.0 and a centralized environment. But my colleagues who are data privacy and security experts in our firm work on these highly sophisticated topics to adapt to Web 3.0.

If you had a crystal ball, what would be your prediction for the future of ChatGPT or alike from a data management perspective?
I’d say that AI is going to be used at all levels of data management, and data management is going to be used at all levels of the use of AI.

AI tools will be helpful to manage data, to cleanse data, to make it relevant, and to satisfy demanding data management policies that companies must put in place. At the same time, high quality data management is mandatory to feed AI in a way that can produce strong, relevant, non-infringing, and lawful output.

Data management is at the heart of what the future holds for the digital age.

Listen to the full Brand & New podcast.

Although every effort has been made to verify the accuracy of this article, readers are urged to check independently on matters of specific concern or interest.

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