Contact Usinfo@lampros.tech

In the first half of 2025, Web3 protocols lost over $3.1 billion to exploits and operational failures, many of which were caused by blind spots in analytics.

The protocol today spans execution layers, including rollups, sidechains, appchains, and bridges.

Each layer emits data, but none of it arrives in a unified structure. Logs, state diffs, and events are scattered across sources that were never designed to speak the same language.

Generic analytics tools struggle in this environment. They flatten data into siloed tables or dashboards, leaving gaps that matter.

The result is not cosmetic; it changes outcomes:

• Risk blind spots: abnormal flows move across chains without detection.
• Inaccurate incentives: metrics miscount usage, leading to overpayment or underpayment.
• Operational delays: exploits or market shocks surface hours late because of batch pipelines.

These aren’t theoretical. In early 2025, a lending protocol suffered a liquidation cascade. Its monitoring stack relied on daily batch jobs with an 18-hour latency class.

By the time the drift was detected, liquidations had already cascaded across three chains.

With streaming ingestion and lineage-aware anomaly detection, the deviation could have been flagged within minutes, containing the fallout before it spread.

In this blog, we will explore why generic tools fail, what features define a protocol-ready analytics stack, and how specialised infrastructure enables use cases like risk monitoring, governance analytics, and compliance.

Let’s get started.

The Problem: Web3 Data Is Massive, but Disconnected

Blockchain networks were never designed with analytics in mind.

Each rollup, sidechain, and bridge produces its own data streams like transaction logs, state changes, event traces, but they all come in different formats, at different speeds, and often without shared identifiers.

For a protocol operating across several chains, this creates a fundamental visibility gap and a struggle to understand how value moves, monitor risks in real time, and govern effectively.

Most teams, faced with this gap, turn to generic Web2 analytics tools. That’s where the problems multiply.

Why Web2 Analytics Tools Fail for Web3 Protocols

Most teams start with generic or legacy analytics platforms because they are quick to set up.

But these tools were built for Web2 data, where events are neatly structured and centralised. When applied to blockchain systems, they miss critical details like:

• Fragmentation across chains
  • Data from L2s, appchains, and bridges arrives in different schemas.
  • Generic tools flatten them into separate tables, which makes it impossible to see how value actually moves through the protocol.
  • Outcome: A lending protocol might record deposits on one chain but miss collateral adjustments elsewhere, leading to mispriced risk exposure worth millions.
• Latency and batch processing
  • Legacy systems often work in hourly or daily intervals.
  • In DeFi, minutes matter. A delayed update means that exploit detection or liquidation alerts can lag until losses are irreversible.
  • Outcome: By the time a liquidation or oracle exploit surfaces, losses are already irreversible.
• Entity misclassification
  • Wallets and contracts don’t behave like Web2 accounts.
  • A single user might interact through multiple smart contracts or bridges, while automated agents execute transactions around the clock.
  • Generic analytics often double-count or miss these entities entirely, distorting incentive programs and usage metrics.
• No built-in verifiability
  • When dashboards show numbers without clear lineage, teams cannot prove that metrics align with the on-chain truth.
  • This undermines trust, especially in governance and treasury discussions where decisions depend on accurate, reproducible data.

Why This Problem Exists

The root issue is not a lack of data; it’s the incompatibility of data across environments.

Each chain was designed to secure transactions, not to make analytics easy. Without infrastructure built specifically to normalise, verify, and correlate these streams, protocols are left piecing together a broken picture.

Disconnected data is not just inconvenient. It leads directly to mispriced incentives, blind spots in risk management, and governance decisions made on incomplete information.

Solving these challenges requires analytics that are designed for decentralised systems from the ground up.

Let’s look at the key features that make a protocol-ready analytics stack effective in 2025.

Purpose-Built Analytics: Core Features Needed for Protocols

Generic dashboards and Web2-inspired BI tools can highlight surface-level trends, but protocols in 2025 require precision.

Their economics, governance, and risk models depend on metrics that are traceable, reproducible, and chain-aware.

A purpose-built analytics stack brings together several critical features that go beyond reporting; they form the operational backbone of a protocol.

Here are the key features that define a protocol-ready analytics system in 2025.

Real-time and Immutable Ingestion

• It collects every event, log, and state change directly from chains as they happen, writing it to immutable storage.
• Protocols are exposed to market risks that unfold in seconds, not hours.
• Liquidations, MEV exploits, and sudden liquidity shifts can drain treasuries or destabilise markets if not detected in real time.
• Immutable records also mean any anomaly or incident can be reconstructed later, without fear of tampered history.

Outcome: Teams move from reactive post-mortems to proactive detection, gaining forensic-grade data trails that strengthen security and trust.

Chain-Agnostic, Event-Driven Streaming

• It standardises feeds from rollups, sidechains, and bridges into a consistent schema and streams them continuously.
• Cross-chain value flows are now the default. A DEX might settle on Arbitrum while routing collateral to Base, or an NFT marketplace may bridge assets to an L3 for settlement.
• Without event-driven normalisation, metrics fragment and usage appears lower (or higher) than reality.

Outcome: Protocol teams see a single, continuous view of user and liquidity behaviour across environments, allowing them to track true adoption and prevent misaligned incentives.

Verifiability and Provenance

• It embeds lineage tracking and version control into every transformation, with the ability to hash-commit results or prove them with zk-proofs.
• In DAO governance, disputes often arise from “whose numbers are right.” Without verifiable lineage, proposals may be debated on conflicting dashboards.
• With provenance, metrics are reproducible, and communities can validate the exact logic behind them.
• Some teams now use zk-attested dashboards to share token distribution or treasury figures without exposing sensitive raw data.

Outcome: Governance and treasury management shift from subjective debates to verifiable truth, reducing friction and improving accountability.

Privacy by Design

• It integrates zero-knowledge methods and MPC (multi-party computation) to run queries while keeping individual user data private.
• Protocols must balance transparency with compliance and user trust.
• Full exposure of transaction-level data can leak trading strategies, while opaque reporting undermines governance.
• Privacy-preserving analytics allows both aggregated insights without leaking sensitive data.

Outcome: Protocols maintain compliance readiness, protect user activity, and still empower communities with open, verifiable summaries.

Developer-Friendly Surfaces

• It provides APIs, SDKs, and plug-ins for quick integration of metrics into dApps, dashboards, or DAO portals.
• Engineering time is scarce. When every product feature requires writing custom ETL logic, progress slows.
• Developer-friendly surfaces let teams access clean, verified data directly, reducing complexity and accelerating shipping cycles.

Outcome: Protocol teams shorten the distance from data to decision, focusing resources on innovation instead of pipeline maintenance.

Native Integration with Governance and Tokenomics

• This feature connects analytics directly to token models, incentive programs, and governance processes.
• Token distribution, delegate participation, and grant effectiveness are not side metrics; they define protocol sustainability.
• Generic analytics rarely capture this context. A purpose-built stack can track delegate voting consistency, proposal outcomes, token holder concentration, and incentive ROI.

Outcome: DAOs operate with clear visibility into the effectiveness of their governance and token policies, making adjustments before inefficiencies erode long-term value.

Unit Economics and Reliability

• Unit economics models costs tied to blobs, DA layers, and gas fees, while monitoring freshness SLAs, anomaly patterns, and pipeline stability.
• Data itself carries cost. DA fees and gas consumption can shift a protocol’s unit economics in real time.
• Without accounting for this, teams risk mispricing treasury outflows or incentive programs.
• Reliability ensures that metrics are fresh, anomalies are flagged immediately, and teams have documented runbooks for corruption or schema drift.

Outcome: Protocols move from guesswork to continuous economic clarity, understanding not only how they perform but also how much it costs to measure and sustain that performance.

Tooling Landscape in 2025

No single platform solves all of these needs. Protocols increasingly assemble stacks from specialised providers like:

• Dune v3 Workspaces for event-driven, customizable modelling.
• The Graph’s Substreams and SubQuery for high-performance indexing.
• Subsquid for scalable, chain-agnostic ingestion.
• Flipside Data Studio for governance-ready outputs and reproducibility.
• Verifiable compute modules that layer zk-proofs on analytics pipelines.
• Lakehouse architectures to combine streaming performance with durable, reproducible storage.

The key is not choosing one tool, but orchestrating them into a coherent stack that guarantees accuracy, lineage, and reproducibility across chains.

For a structured blueprint on building such a stack, including design checklists and reference architectures, we have built a guide, “Building the Data Backbone of Web3.”

With the core features in place, the next step is understanding how they translate into practice.

Let’s look at real-world use cases where purpose-built analytics delivers measurable outcomes for protocols.

Real-World Use Cases of Protocol Analytics Infrastructure

Flash-Loan Exploit on CETUS - Q2 2025

In one of the largest Q2 2025 exploits, a flash-loan attack drained $223 million from the CETUS protocol within minutes, marking a record-breaking DeFi exploit for the quarter.

It happened because of a legacy analytics stack with hourly or daily batch reporting that failed to detect rapid, multi-step borrowing and price manipulation executed within atomic transactions.

The protocols lacked streaming alerting that could catch transient anomalies in real time.

How analytics can help: With streaming ingestion, lineage-aware event tracking, and anomaly detection, the flash-loan pattern, especially abnormal time-weighted price deviations, could be flagged in under a minute, enabling emergency pause procedures.

Outcome: Instead of reacting hours later, teams equipped with protocol-grade analytics could have mitigated or entirely averted the attack by halting sensitive contract functions almost immediately, saving hundreds of millions in user and protocol losses.

$1.5 Billion ByBit Hack in February 2025

ByBit, a major exchange, suffered a colossal theft of $1.5 billion in ETH in February 2025, making it the largest known crypto heist to date.

The exploit was tied to compromised access controls, with attackers bypassing failsafes and draining funds. Legacy analytics lacked cross-system tracking and verifiable alerts on anomalous withdrawal patterns or unusual authorisation workflows.

How analytics can help: A purpose-built architecture would integrate immutable ingestion from on-chain activity, including massive transfer patterns and tie them to off-chain authorisation flows. Alerts based on lineage and cross-signature anomaly models would trigger as soon as abnormal volume or key changes occur.

Outcome: Early detection and customizable alerts could have enabled ByBit’s team to freeze withdrawals, investigate compromised credentials, and preserve a large portion of funds, dramatically reducing damage.

Close DAO Vote Sparks Recount Demands - May 2025

In May 2025, a DAO governance vote closed with a razor-thin 1–2 vote margin, prompting public recount demands and raising concerns about vote accuracy.

Traditional analytics dashboards misinterpreted cross-chain voting and failed to resolve duplicate identities. Without data provenance, the community could not independently verify the outcome.

How analytics helps: A purpose-built stack enforces canonical identity resolution across chains, tracks lineage of votes (with versioning), and stores vote records in verifiable formats.

This allows stakeholders to audit results using the same pipeline that produced the dashboard.

Outcome: With reproducible metrics and chain-agnostic tracking, the vote results become transparent and incontrovertible, reducing community friction and preserving governance integrity even in tight decisions.

These use cases demonstrate how purpose-built analytics shifts a protocol’s posture, from retrospective to proactive, from fragmented to unified, from reactive to resilient.

It empowers teams to detect exploits faster, govern more transparently, and automate changes confidently. Moving ahead, let’s see the trends shaping the future of data warehousing.

Emerging Trends and the Future of Protocol Data Warehousing

The data backbone of protocols is evolving rapidly. As decentralised systems scale, new trends are shaping how data is stored, analysed, and trusted. Here’s what’s defining the analytics landscape in 2025 and beyond:

Cross-Chain Analytics as the Default

Activity is no longer confined to a single chain; it flows across rollups, appchains, bridges, and DA layers.

Insights must span all execution environments in real time.

Fragmented dashboards are no longer sufficient. Protocols now need unified warehousing capable of tracking liquidity, user behaviour, and funding costs across chains seamlessly.

AI/ML: Powered Inference on Protocol Metrics

AI isn’t just making dashboards smarter, it’s driving predictions. Models now forecast demand surges, predict arbitrage imbalances, and detect subtle fraud patterns.

Protocol teams can shift from reactive monitoring to anticipatory defence, optimising for market behaviour before volatility arrives. AI becomes part of the loop, not a follow-up tool.

Privacy-Preserving Analytics Across Public and Permissioned Chains

Privacy design is being baked into data systems, not added afterwards. Zero-knowledge proofs and MPC ensure that sensitive financial or identity-linked data is analysed without exposure.

This enables protocols to operate transparently in public governance while complying with privacy and regulatory demands. It bridges decentralised openness with institutional-grade privacy.

Growth of Open Data Standards and Data DAOs

Ecosystems are converging on standard schemas and shared models. Data DAOs offer tokenised governance over shared datasets, enabling collaboration and shared tooling.

Shared standards drive interoperability and reduce duplication. Data DAOs are enabling communities to collaboratively manage, curate, and monetise analytics infrastructure.

By 2030, blockchain data volume is projected to grow 10–12x, making reproducibility and streaming non-negotiable.

AI inference, zk-powered privacy, and community-owned Data DAOs are no longer experiments; they are the scaffolding of protocol analytics going forward.

But while open standards provide the foundation, protocols still need a stack that translates these principles into day-to-day operations.

This is where a protocol-ready approach becomes essential.

The Lampros Tech Approach: A Protocol-Ready Data Stack

Protocols don’t just need dashboards; they need infrastructure that can serve as a decision layer across product, governance, and risk.

At Lampros Tech, we approach analytics as a protocol-grade system, not an add-on.

What Protocols Get with a Purpose-Built Stack

• Tailored Dashboards: Designed for treasury, governance, user behaviour, and risk metrics. Instead of generic KPIs, dashboards reflect protocol-specific state machines and token flows.
• Custom Alerting Systems: Threshold-based triggers for utilisation spikes, liquidity anomalies, or governance participation drops. Alerts fire in real time, allowing operators to act before incidents scale.
• Real-Time Multi-Chain Views: A unified lens across rollups, L2s, and appchains. Liquidity migration, collateral flows, or DAO participation can be traced in one continuous view, eliminating fragmentation.
• DAO Analytics: Transparent mapping of delegate activity, proposal outcomes, and incentive ROI. Governance teams can evaluate effectiveness with verifiable, reproducible data.
• Strategic Reporting: Structured outputs that align internal teams and external contributors. Treasury committees, governance delegates, and community members all access the same reproducible truth.

Core Methodology Principles Used

• Canonical Entities: Every user, contract, and transaction is modelled consistently across chains. This prevents duplication, omissions, or misclassification, core problems in legacy stacks.
• Model Versioning: Every calculation, from TVL to incentive ROI, is versioned. If a metric changes, teams can audit why and roll back if needed.
• Freshness SLAs: Analytics pipelines are monitored for latency. Metrics must be available within defined time windows, and failures must surface through automated anomaly detection.
• Public Reproducibility: Where possible, metrics are published in reproducible formats, allowing governance participants to validate claims independently. This increases trust and reduces disputes over data accuracy.

A protocol-ready analytics stack isn’t about adding more dashboards.

It’s about ensuring that the numbers teams rely on to set incentives, approve proposals, or manage treasuries are correct, verifiable, and timely.

When analytics becomes infrastructure, protocols move faster, govern with clarity, and operate with complete visibility.

Closing Thought

Analytics is no longer a secondary layer for blockchain protocols; it is the infrastructure that underpins growth, governance, and resilience.

In 2025, the scale and complexity of decentralised systems mean that protocols cannot rely on generic tools or fragmented dashboards.

For teams ready to design a resilient data foundation, our free guide “Building the Data Backbone of Web3.” It provides checklists, design blueprints, and reference architectures for teams building at scale.

And for those prepared to operationalise, our Web3 Data Analytics service translates these principles into production-ready infrastructure.

Astha Baheti

Growth Lead

Astha Baheti is the Growth Lead at Lampros Tech, a Blockchain development company helping businesses thrive in the decentralised ecosystem. With an MBA in Marketing and hands-on experience in digital marketing and content strategy, she brings expertise in crafting clear, impactful communication that aligns business goals with audience needs. At Lampros, Astha focuses on translating complex Web3 concepts into accessible narratives that drive engagement and awareness.

CONNECT ON:

See more

See more