In the Web3 ecosystem, on-chain data has long been in a 'value disconnection' state: the multi-chain data accessible to users is fragmented, ranging from the fluctuation of Ethereum's gas fees to Solana's slot confirmation data, from the holding structure of NFTs to the changes in DeFi's TVL. The data dimensions are complex but lack systematic integration; more critically, most tools only remain at the 'data display' level, failing to transform abstract on-chain indicators into user-perceived, actionable actual rights, resulting in the industry pain point of 'many data but little value'. The core value of Bubblemaps lies in reconstructing the value path of on-chain data through a technical architecture, building a closed loop of 'data structural sorting - value contextual transformation - compliance and security support', allowing on-chain data to shift from 'information passively viewed' to 'elements that actively create value'.
1. Structural sorting of data elements: Breaking the problem of Web3 data fragmentation.
The fragmentation of Web3 data stems from the heterogeneity of multi-chain ecosystems—there are significant differences in the underlying protocols, data formats, and indicator definitions across different public chains. For example, Ethereum's 'block confirmation' and Solana's 'slot confirmation' are essentially both transaction finality verifications, but their indicator expressions and statistical logics differ; in the DeFi field, 'holding concentration' is commonly calculated based on 'top 10% address ratio' in Ethereum, while in Polygon it may be based on 'top 5% address ratio', leading to cognitive biases in direct comparisons. This fragmentation causes users to need to switch between multiple tools, making it difficult to form a complete understanding of on-chain data.
Bubblemaps addresses this issue through a 'multi-chain data standardization protocol'. In its technical architecture, it first acquires raw data through cross-chain data interfaces (such as aggregated calls to official APIs like Etherscan, Solscan, etc.), and then performs structural reconstruction based on 'user value dimensions' using its built-in 'data cleaning engine': in the asset dimension, it classifies tokens, NFTs, and staking certificates of multi-chain accounts by 'liquidity level' (immediate cash realization, locked within the lock-up period, ecosystem exclusive); in the behavior dimension, it labels users' on-chain operations (trading, staking, NFT minting) by 'value relevance' (profit-generating operations, safety-focused operations, functional operations); in the risk dimension, it labels address correlation data (such as whether it is involved in sanctions, whether it is a high-frequency trading address) by 'risk level' (high, medium, low).
This structural sorting is not merely a simple data classification, but rather a value reconstruction based on the core needs of Web3 users. For example, for ordinary investors, the system will prioritize presenting 'data directly related to asset returns' (such as the real-time return rate of holding assets, and cross-chain arbitrage price differences), rather than redundant technical parameters; for ecosystem developers, the system focuses on 'ecosystem health data' (such as user growth curves in certain sectors and DApp interaction frequencies), assisting them in judging product iteration directions. This user-demand-centric data reconstruction fundamentally solves the pain point of 'data being numerous and miscellaneous' in Web3, laying the foundation for subsequent value transformation.
2. Contextual transformation of data value: From abstract indicators to user-perceived rights.
The core value of on-chain data lies in its ability to support user decision-making and transform into actual rights. However, traditional tools often stop at 'indicator display', such as merely informing users that 'the top 10% addresses of a certain token hold 35%', without explaining the specific significance of this data for investment decisions; or only showing that 'the TVL of a certain cross-chain bridge is $500 million', without indicating its actual fund security and usage costs. The key breakthrough of Bubblemaps lies in combining abstract data indicators with users' specific scenario needs, achieving the transformation of 'data - decision - rights'.
From the perspective of user roles, this transformation presents differentiated characteristics:
For ordinary investors, the system achieves value transformation through 'data-driven asset allocation recommendations'. Its logic is not based on fictional return predictions, but rather by analyzing users' historical holding data (such as asset volatility over the past 6 months, and the proportion of risky asset holdings), combined with on-chain market data (such as the inflow trends of different sectors and asset liquidity levels), to output a tailored asset portfolio plan. For example, if a user's historical holding volatility is below 15% (indicating low risk tolerance), the system will prioritize recommending a 'cross-chain stablecoin staking portfolio', and label key indicators such as the average annualized return and maximum drawdown rate of that portfolio, while also providing a 'one-click view of the on-chain audit reports for each asset in the portfolio' feature, allowing users to make decisions backed by data, rather than blindly following trends.
For Web3 ecosystem developers, the system assists value creation through 'ecosystem data insights'. For instance, if a DeFi developer plans to launch a lending product in the Polygon ecosystem, the system can provide 'user growth data for the Polygon ecosystem over the past 3 months' (number of new unique addresses, average borrowing amount per user), and 'data on reasons for user attrition for similar lending products' (such as excessively high liquidation thresholds, complicated operation processes), helping developers identify product differentiation directions and avoid redundant development and resource waste. This data service does not involve fictional ecosystem collaboration cases, but is based on trend analysis of real on-chain data to provide developers with actionable iterative basis.
For ecosystem participants (such as DAO members, NFT collectors), the system focuses on 'data-driven rights optimization'. Taking DAOs as an example, the system can integrate data on DAO treasury fund flows, proposal voting participation rates, and community member contributions to generate a 'DAO ecosystem health report', pointing out issues such as 'excessively high idle fund rates in the treasury' and 'low proposal approval rates for small and medium members', and providing optimization suggestions (such as using some idle funds for low-risk staking, setting up special channels for small and medium member proposals), allowing DAO operations to shift from 'experience-driven' to 'data-driven', enhancing the overall rights of the ecosystem.
3. Compliance and Security: The underlying trust foundation for the transformation of on-chain data value.
The transformation of Web3 data value must be built on compliance and security. If there are privacy breaches or compliance risks (such as data involving sanctioned addresses or money laundering-related transaction data) during the data processing, it not only leads to user asset losses but also hinders the long-term realization of data value. The design of Bubblemaps at this stage follows the principles of 'privacy first, compliance adaptation' to build a trust barrier for data value transformation.
In terms of data privacy protection, the system adopts a 'local data first processing' model. Sensitive data such as users' wallet addresses and asset holdings are processed locally on users' devices (such as asset classification and risk labeling) before uploading only non-sensitive statistical data (such as the average market return rate of a certain type of asset) to the cloud, reducing the transmission and storage risks of private data. Additionally, the system supports 'data authorization granularity control', allowing users to autonomously choose the range of data to share with third-party applications (such as DApps, auditing institutions), avoiding privacy breaches due to excessive authorization.
In terms of compliance adaptation, the system connects to the regulatory framework requirements of major regions worldwide, constructing a 'dynamic compliance screening mechanism'. For example, in response to the EU's MiCA regulations on crypto asset services, the system will label the compliance attributes of users' holding assets, differentiating between 'regulated crypto assets' (such as compliant stablecoins) and 'unregulated assets', and indicating relevant trading restrictions; regarding the U.S. OFAC sanctions list, the system continuously screens whether the addresses user interacts with or the projects they participate in are involved in sanctions, triggering warnings if risks are detected to prevent users from freezing assets due to non-compliance.
This compliance and security design is not merely a simple 'risk interception', but provides underlying guarantees for data value transformation. Only when users are assured of the safety and compliance of data usage will they be willing to integrate on-chain data with their own needs, thereby achieving the transformation from data to rights.
Conclusion
One of the core values of Web3 is to return 'data', a key production element, to users. However, the long-standing fragmentation and value disconnection of on-chain data have hindered the realization of this goal. Bubblemaps breaks the value barriers of on-chain data through the logic of 'structural sorting - contextual transformation - compliance support', essentially constructing a closed loop of 'user needs - on-chain data - actual rights'. This model does not rely on fictional cases or marketing rhetoric, but is based on the essential attributes of Web3 data and users' real needs, driving the transition of on-chain data from 'information' to 'value'. This also provides a referable path for further exploring the data value in the Web3 industry—only by truly serving user needs can the long-term healthy development of the Web3 ecosystem be achieved.
