Why Your DAO's Research Is Trapped Without Open Schemas

DAO voting, treasury movements, and delegate activity are locked in disparate subgraphs (e.g., Tally, Snapshot, Safe) with incompatible schemas. This prevents cross-protocol analysis of voter influence or capital allocation.

• Manual reconciliation of delegate identities across platforms takes days.
• Impossible to track a whale's voting power across Compound, Aave, and Uniswap in a single query.

Without a standard schema for MEV flows, DAOs cannot quantify extractable value lost to searchers or assess the true cost of their transactions. Proprietary data from Flashbots or EigenPhi is not composable for internal analysis.

• Blind to sandwich attacks on treasury swaps, costing ~50-200 bps per trade.
• Cannot audit validator compliance with OFAC lists or censorship resistance metrics.

DAOs operating on Ethereum, Solana, and L2s like Arbitrum have no unified view of their total financial position or user activity. Bridging data from LayerZero, Wormhole, and Axelar is a manual, error-prone process.

• TVL calculations are guesses, delayed by weeks.
• Risk exposure from bridge vulnerabilities (e.g., Nomad, Wormhole hack) cannot be modeled in real-time.

Each DAO used a different analytics stack—Dune Analytics, Flipside, and custom subgraphs—creating incompatible data models. Comparing voter turnout or proposal success rates across Aave, Compound, and Uniswap was impossible without manual reconciliation.

• Wasted 300+ analyst hours on data wrangling
• Zero consensus on baseline metrics like 'active voter'
• Delayed treasury decisions by ~6 weeks

Adopting a shared schema (e.g., Governance Data Working Group proposals) for core entities—proposals, votes, delegates—enables composable analysis. This turns raw blockchain data into a portable asset.

• Enable cross-DAO benchmarking against MakerDAO or Optimism
• Plug-and-play dashboards using The Graph or Goldsky
• Unlock automated reporting for Messari-grade summaries

With structured, comparable data, DAOs can move from descriptive analytics to predictive strategy. Identify governance attack vectors before they happen and optimize treasury deployment across Ethereum, Arbitrum, and Polygon.

• Model proposal success probability using historical patterns
• Detect voter apathy trends to trigger incentive campaigns
• Quantify the ROI of governance participation for delegates

Every research DAO becomes its own oracle, publishing data in proprietary formats. This creates a new layer of trust assumptions, mirroring the very problem DeFi sought to solve.

• No Verifiable Provenance: Can't audit the data lineage from raw on-chain to final insight.
• Fragmented Truth: Competing reports on the same protocol (e.g., Lido vs. Rocket Pool metrics) become impossible to reconcile, leading to market inefficiencies.

Research outputs are dead-end artifacts, not composable primitives. This stifles the network effects that drive Web3 innovation.

• Zero Leverage: A deep dive on Uniswap v4 hooks can't be programmatically fed into a treasury management model or a risk engine like Gauntlet.
• Manual Hell: Analysts waste ~70% of time on data wrangling and validation instead of generating alpha, recreating the extract-transform-load pipelines of TradFi.

With no schema to standardize methodology, researcher reputation becomes opaque and non-portable. Quality is judged by marketing, not merit.

• Unauditable Work: Can't verify if a Messari-style report on Aave correctly handled liquidations or oracle price feeds.
• Talent Lock-in: A top analyst's work at one DAO is a resume line, not a verifiable, portable reputation graph that could be used in a SourceCred or Coordinape system.

Major capital allocators (VCs, hedge funds) require standardized, auditable data. The current chaos locks out $50B+ in potential institutional research funding.

• Due Diligence Impossible: A Paradigm or a16z can't systematically evaluate the quality of a DAO's research output, treating it as an unqualified opinion.
• No Benchmarking: Cannot create a Bloomberg Terminal-like benchmark for crypto research quality, stifling the entire field's professionalization.

Every analytics tool (e.g., Dune, Flipside) uses its own schema, forcing your DAO to manually reconcile conflicting metrics. This creates ~70% data prep overhead and makes cross-protocol analysis impossible.

• Unverifiable Insights: Can't audit the raw data behind a dashboard.
• Vendor Lock-in: Switching tools means rebuilding all queries from scratch.
• Fragmented Truth: Treasury reports from different tools never match.

Adopt community-defined schemas (e.g., Spice.ai's OSS models) that map raw chain data to a common language. This turns data into a composable public good, not a proprietary asset.

• Instant Composability: Build atop Goldsky or The Graph subgraphs without custom parsing.
• Auditable Research: Anyone can trace a metric back to its on-chain source.
• Collective Curation: The schema improves as protocols like Uniswap and Aave contribute definitions.

Open schemas enable predictive modeling and agent-based simulation. Your DAO can stress-test proposals against historical MEV patterns or Compound liquidation cascades before execution.

• Agent-Ready Data: Train simulation agents on a canonical dataset.
• Risk Modeling: Model treasury diversification across Lido, Maker, and Frax with consistent metrics.
• Protocol Design: Test new AMM curves against a unified history of Uniswap v3 pools.