Data Science and Analytics for Web 3.0

Vidya Subramanian

Disclaimer: All opinions expressed in this article are my own and do not reflect any thoughts held by my current and past employers.

There is a lot of discussion about Web3 and the underlying Blockchain technology. As a Data Science leader and practitioner, I hope to understand the opportunities for Data Scientists to operate effectively in Web 3.0 as I learn more about it.

Web 3.0 is the evolution of the Internet which is crowd "sourced" and crowd "owned." Users no longer need to relinquish control over their data and content to trusted intermediaries; they can retain, sell, or trade their data with complete transparency. Web 3.0 is highly decentralized on the technical stack, driven by machine learning and artificial intelligence, and leverages blockchain technology. To compare and contrast, Web 1.0 was the breakthrough first version of the evolution where users could read and consume static and hyperlinked information. Web 2.0 enabled users to read and write content while allowing multiple companies to own and monetize their data.

Driving the focus back to Web 3.0, we want to understand potential areas to gauge health, imminent risks, and identify opportunities for any business. The five pillars that cover the spectrum of Web 3.0 have tremendous data science opportunities to be explored from multiple stakeholder lenses. I am outlining a few thoughts below:

1. The first pillar we will discuss is economics.
2. Next, we discuss Web 3.0 infrastructure and go deeper into each layer of the architecture
3. Then, we review offerings from enablers, whose service capabilities range from providing a ready-made platform to offering services that reduce time-to-launch.
4. Components like dApps and DAOs make Web 3.0 unique from its predecessors. 
5. Finally, we assess the (potential) regulatory lens that is necessary for the smooth functioning of the end-to-end process. 

Now, let's review them in detail with some examples to help understand the opportunity for each of these components.

1. Economics studies the interactions that would create economic incentives for participation.

• Cryptoeconomics solves participant coordination problems in digital ecosystems by creating decentralized sustainable economies using cryptocurrency. It helps create incentive for people to participate in a peer-to-peer system without any central authority enforcing rules of behavior. Example for analyses would be the participation rate.
• Tokenomics is a portmanteau of token and economics and refers to key indicators of value and use. It is a broader definition of Cryptoeconomics and includes distributed ledger technology (DLT) tokens like voting records and identity documents. Some examples include tracking supply and demand of Basic Attention Tokens, which give an indication of market dynamics. We can also monitor micro-payments made to users based on the token types.
• Digital Assets are assets with unique digital signatures and are characterized by being open and decentralized in the Web 3.0 world with an emphasis on commercial exchange value. Some key data science opportunities could be targeted at the creator economy - generating insights for NFT creators wanting to have a pulse on how commercially successful their creations are. Towards that, they want to proactively get reports on first-time purchases and subsequent transfers for an ongoing royalty in perpetuity. Transparency in monitoring allows them to keep a check on price trends and market fluctuations. NFT Domains can be stored in a wallet by the owner, just like a cryptocurrency.

2. Web 3.0 Technology Stack indicates the kind of analysis we could potentially perform for each architectural layer.

• Zero-trust protocols allow all participating members to share security with each other while Zero/low trust interaction protocols emphasize how different nodes can interact with each other based on trust computation and information shared. This enables decentralization - one of the core tenets of Web 3.0. Measuring the performance of nodes and block finality  are table stakes. Indexers and listeners track protocols and specific contracts, then store metrics and data in blockchains. The events related to these protocols can be used for optimizations. Tracking hacking attempts and successes can proactively be used to reduce fraud on blockchain. 
• Distributed Ledger Technology like Blockchain enables data transparency, privacy, and immutability. We can monitor user experience latency and throughput in real-time to help optimize the blockchain node infrastructure. Private, Consortium, and Hybrid Blockchain analysis will have analyses restricted to a smaller audience compared to Public Blockchain. The types of Blockchains are defined below in the Enablers section.
• Blockchain scalability is impacted by consensus mechanisms, network latency, node infrastructure, and many other factors. Scalability of blockchain networks is similar to any platform involving extending support to an increasing load of transactions, as well as nodes in the network.
• Browser applications allow users to build user interfaces on top of the Web 3.0 stack. Developers will need to understand the app usage to provide users with a better user experience. For example, we can monitor Web 3.0 browser usage and tokens earned to provide users with an ad-free experience.

3. Web 3.0 Enablers provide a range of platform capabilities from creating to managing Web 3.0 data.

• Public Blockchain data aggregators who scrape public blockchains and create an aggregated view of all the data for others to consume. Potentially a user interface will record user usage and interaction.
• Apart from scraping specific private or consortium blockchain, Private and Consortium Blockchains enablers help create an aggregated view of all the data for others to consume. They also create bespoke analyses for their specific customers.
• Service providers can provide blockchain data, sometimes integrated with oracles to facilitate comprehensive analyses. An example of this could be user data integrated with offline user behavior data.

4. dApp and DAO creation service providers have a need to analyze account creation, user experience, and opportunities for dApp and DAO Components, which are unique to Web 3.0.

4.1. Decentralized Applications (dApps) decentralize apps instead of app ownership limited to a few companies. dApps creators need a pulse on user behaviors and patterns with analyses like:

• Classification of user adoption and experience that allows us to cohort users and personalize their experience.
• Identification of transaction similarities and fraud prediction, since all the data collected on the dApp will be on chain. 
• Account anomaly detection by monitoring anomalous peers, wallet theft, key theft, and cryptojacking etc to provide proactive security. It can also help protect users against any attacks by monitoring server requests for real time anomalies.
• User authentication will be a vital aspect of Web 3.0 to allow users access to services and governance. Similar to Web 2.0, too many illegal requests could lead to distributed denial-of-service (DDoS) attacks. 
• Computation Fees are fees that dApps have to pay for computation also known as gas (not to be confused with gasoline). Every dApp starts with some gas inflow and gas limit they have set. Depending on how the dApp has adopted this, we could analyze gas outflow or utilization against the gas limit to gauge the gas burn rate.

4.2 Decentralized Autonomous Organization (DAO) is an emerging legal structure with no central governing body but with common goals, making decisions in a bottoms-up management approach. It decentralizes decision-making, focusing on transparency and fairness in consensus building. We can find those opportunities by analyzing: 

DAO Adoption & Trends

• DAOs like any regular organizations have to be registered as an LLC with an amendment indicating its decentralized and autonomous status. Studying the DAO registrations and deployment trends will shed light on the popularity and trajectory of these organizations in the market.
• DAO memberships can be of three types: token-based share-based, and reputation-based membership. Analyzing the membership data, gives a deep understanding of the preferred membership status, their intrinsic behaviors and patterns and predicts churns.
• Tokens are the intrinsic rewards for membership and participation apart from monitoring gas and gas limits created for the DAO. Evaluating tokenomics involves analyzing total token supply, circulating supply, token price, and market cap etc.
• Since DAO communication is decentralized as well, user sentiment relating to the DAO can be analyzed on Discord or any other communication tool.

DAO Technical Performance 

• Smart contracts for any DAO have to be created, agreed upon, reviewed (QA), verified, and deployed. Apart from that, triggers for the smart contract have to be created. Deep understanding of patterns related to monitoring business rules' invocation, correlated documents, events raised, is vital. Also, corresponding actions processed for Smart Contract creation and triggers have to be monitored to ensure that the Smart contract is fair and error-free.
• Smart contracts are vulnerable to hacking. So proactively monitoring them helps determine Smart Contract anomalies like honeypots, coin freezing, reentrancy amongst others. 
• One of the primary reasons for any DAO’s existence is to create consensus for proposals. So monitoring the consensus building for any anomalies like race attack, eclipse attack, 51% attack, balance attack, fork formation is vital. 

4.3.  Self-sovereign Identity Management puts end users in the driver's seat to manage their data and decide how to share it.

• Self-sovereign Identity Management allows end users to determine how they want to retain their data and who they want to share it with. Users need a quick analysis of how their data was used and who used it. For example, tracking earnings for user attention using the Basic Attention Token instead of Web 2.0 daily unique visitors, since visitors are paid micro-payments for their attention.
• Evaluating Transaction Frequency could provide an early indication of fraud. 

5. Regulations seem to be an inevitable part of the Web 3.0 future. For example, the Presidency of the Council and the European Parliament reached a provisional agreement on the markets in crypto-assets (MiCA) proposal. MiCA aims to protect consumers against some of the risks associated with the investment in crypto-assets. While the agreement has not been inked yet, it's a matter of time before regulations are enforced nationally and internationally.

• Regulators need proactive notification of issues. Analysis like monitoring tax implications for Digital Asset creators, reviewing transaction-based anomalies (Transaction Tampering, Currency Token Theft, Malicious Transaction on Ledger, Money Mixing, Double Spending) would be vital. Also, we could provide legal guardrails at the state, federal, national, and international levels.
• Governance for individual DAOs and dApps would be important guardrails for fair operations. Mediation for any conflicts amongst two or more DAOs or amongst two or more dApps would also be required. Analyses to enable these services would be vital.
• Finally, enabling Auditors to audit DAO, dApps, and all Web 3.0 enablers, smart contacts would promote confidence in the process.

There are a lot of Data Science opportunities in Web 3.0 but this is going to require a shift from how we approach data science in Web 2.0. The stakeholders in Web 3.0 are very diverse and the lens we need to apply to gain insights will be very different as we discussed above.

I look forward to your thoughts,

Vidya Subramanian

Vidya Subramanian is passionate about the Data Science ecosystem. Her ability to build data-driven organizations led Forbes to recognize her as one of the "8 Female Analytics Experts From The Fortune 500". She is an established industry thought leader, speaker, and data practitioner and currently authoring her third book, Data Science for Wiley, slated to publish in early 2023. She has led Data Science at Apple Retail Online, spanning the entire digital footprint across all products and platforms and Apple Ad Platforms, influencing critical strategic and tactical decisions. Before that, she held leadership roles in multiple companies. She enjoys demystifying complex technology and creating products that solve complex problems.

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