The total amount of on-chain data in the Web3 ecosystem is growing exponentially, but data applications have long remained at the 'surface information display' stage: most tools can only provide basic indicators such as token prices, NFT floor prices, and DeFi TVL, failing to address the acquisition difficulties of 'data fragmentation' or excavate the deeper value of 'data correlation', let alone achieve the value-added transformation from 'data to service'. This contradiction of 'data-rich but value-poor' makes it difficult for on-chain data to truly serve user decision-making and ecosystem development. The core breakthrough of Bubblemaps lies in constructing a layered development system for on-chain data value that goes from 'basic layer standardization—middle layer correlation—value-added layer service', transforming scattered on-chain data into accessible, interpretable, and applicable value assets, promoting the leap of Web3 data from 'information carrier' to 'production factor'.
1. Basic Layer: Data Standardization and Accessibility Optimization, Breaking Down Web3 Data Acquisition Barriers
The 'difficulty in obtaining' Web3 data stems from the heterogeneity of multi-chain ecosystems and the non-standardization of data formats: the block data fields of Ethereum differ significantly from the slot data structures of Solana, the calculation logic for 'staking returns' of DeFi protocols (simple interest/compound interest, fee deduction methods) varies, and the descriptions of 'holding rights' for NFTs lack a unified standard. Ordinary users need to learn the data query rules of different public chains, and developers need to adapt to multi-chain API interfaces, leading to extremely high data acquisition costs.
The core work of Bubblemaps in the basic layer is to lower the usage threshold of Web3 data through 'data standardization processing' and 'low-threshold acquisition mechanisms':
In terms of data standardization, the system has constructed a 'Unified Dictionary of Web3 Data' to unify the definitions, calculation logic, and display formats of core metrics across multiple chains. For example, for the key metric 'real TVL', it uniformly excludes 'project self-held assets', 'duplicated staked assets', and 'shell collateral assets', ensuring that the TVL data from different public chains like Ethereum, Polygon, and Avalanche is comparable; for NFT 'real transaction frequency', it uniformly excludes 'mutual brushing transactions of addresses associated with the same IP' and 'zero-value transfers' to avoid cognitive biases caused by differences in statistical rules. At the same time, the system connects to the official data interfaces of mainstream public chains (such as Etherscan API, Solana RPC, Polygon Scan API) and automatically completes format conversion through the built-in 'Data Cleaning Engine', outputting standardized data without the need for users or developers to manually adapt.
In terms of data accessibility, the system has designed a 'layered data acquisition channel': for ordinary users, it provides a visual 'multi-chain data dashboard' that supports one-click viewing of 'cross-chain asset overview', 'holding risk rating', and 'key indicator change reminders', without needing to master professional data query skills; for developers, it opens 'standardized data API interfaces' that support on-demand calls for 'user behavior data', 'ecosystem health data', and 'risk warning data', with detailed parameter descriptions and invocation examples in the interface documentation, reducing development costs; for institutional users, it provides 'customized data export services' that can generate structured data reports in Excel or CSV format as needed to meet deep analysis needs.
The optimization of this basic layer is not merely 'data relocation', but fundamentally addressing the issues of 'fragmentation' and 'high barriers' of Web3 data—when the data formats of different public chains are unified and acquisition paths simplified, users and developers can focus their efforts on 'data value mining' rather than 'data acquisition itself', laying the groundwork for subsequent value-layered development.
2. Middle Layer: Data Correlation Analysis and Contextual Interpretation, Exploring the Correlation Value of On-chain Data
The deeper value of on-chain data lies in 'indicator correlation': a single statement like 'the price of a certain token rises by 10%' is meaningless, but when combined with 'small and medium wallet holdings account for 80%' and 'daily active growth of ecosystem DApps by 50%', it can be judged that the rise is supported by real demand; an isolated statement like 'the floor price of a certain NFT drops by 20%' might cause panic, but if combined with 'the creator continues to accumulate' and 'ecosystem-linked activities are about to be launched', it can be identified as 'a short-term correction rather than a long-term decline'. Traditional tools lack this correlation analysis capability, leading to data interpretation that remains at 'just numbers', failing to provide effective support for decision-making.
The core capability of Bubblemaps in the middle layer is to generate practical significance from on-chain data through 'multi-dimensional data correlation analysis' and 'contextual interpretation':
In terms of data correlation analysis, the system has constructed an 'indicator correlation model' that links seemingly isolated on-chain data into an 'explainable logical chain'. For example, when analyzing the 'ecosystem health' of a certain DeFi protocol, the model will correlate four dimensions: 'TVL changes', 'user retention rate', 'net fund inflow direction', and 'contract interaction frequency': if TVL is growing while the 7-day user retention rate exceeds 60% (higher than the industry average of 40%), funds are mainly flowing in from small and medium wallets (accounting for 75%), and contract interaction frequency increases in tandem with TVL, it is determined that 'ecosystem health is high and growth is sustainable'; if TVL grows but the user retention rate is below 20% and funds mainly flow from project-related addresses, it is determined that 'ecosystem growth is questionable and one should be wary of false prosperity'. This correlation analysis does not rely on subjective judgment but is based on the objective laws of the Web3 ecosystem (such as 'growth driven by real demand must be accompanied by user retention and interaction improvement'), ensuring the objectivity of the conclusions.
In terms of contextual interpretation, the system translates the results of correlation analysis into 'actionable decision references' based on the core needs of different user roles (investors, developers, DAO members). For example, for investors, the system interprets the analysis result 'the health of a certain Layer2 ecosystem is high' as 'focus on DeFi projects within that ecosystem that have 'rapid user growth and low fees', while avoiding targets with 'inflated TVL and rapid user churn'; for developers, the system interprets the correlation data 'user demand for cross-chain functionality has grown by 300%' as 'developing a 'cross-chain aggregation tool' or 'cross-chain asset management DApp' is more likely to meet market demand'; for DAO members, the system interprets the correlation data 'the idle rate of treasury funds exceeds 80%' as 'a proposal can be made to use some idle funds for 'ecosystem developer subsidies' or 'low-risk staking' to enhance fund utilization efficiency'.
The development of this middle layer turns on-chain data from 'isolated numbers' into 'logical and meaningful decision-making basis', addressing the industry pain point of 'being able to see data but not able to use it', and is a key link in the transition of data value from 'basic information' to 'value-added services'.
3. Value-added Layer: Data-driven Personalized Services and Ecosystem Collaboration, Realizing the Application of On-chain Data
The ultimate value of Web3 data lies in 'service implementation'—enabling data not only to assist in decision-making but also to directly transform into personalized services and collaborative tools. Traditional tools lack the ability to convert 'data into services', making it difficult to achieve a closed loop of data value: users know 'certain project data is credible', but don't know 'how to allocate assets based on this'; developers know 'certain track has increased demand', but lack 'specific support to convert that demand into products'. The core role of Bubblemaps in the value-added layer is to turn on-chain data into perceivable value for users through 'personalized service output' and 'ecosystem collaboration empowerment'.
In terms of personalized services, the system outputs customized data application services based on users' 'historical behavior data' and 'demand preferences'. For example, for investors with 'low risk tolerance and a preference for stable returns', the system combines their behavior data of 'only participating in stablecoin staking in the past 6 months' and the correlation analysis result of 'a certain cross-chain stablecoin mining pool with an 8% annualized return and low risk rating' to push a 'personalized asset allocation plan': 'invest 50% of funds into the cross-chain stablecoin mining pool (to secure basic returns), invest 30% into 'NFT staking dividends' (for low volatility appreciation), and retain 20% as emergency funds', while providing accompanying services such as 'one-click to view mining pool audit reports' and 'income arrival reminders'; for investors who 'like to explore new ecosystems and have a high risk tolerance', the system pushes 'a portfolio of potential projects from a certain emerging Layer2 ecosystem', accompanied by 'dynamic tracking of project data' (such as 'real-time alerts when a large amount of funds flow out of a project').
In terms of empowering ecosystem collaboration, the system transforms standardized and correlated data into 'ecosystem collaboration tools', reducing collaboration costs in the Web3 ecosystem. For example, for 'cross-chain ecosystem collaboration', the system provides 'multi-chain data sharing dashboards' that allow project parties, developers, and users from different public chains to view 'cross-chain fund flows', 'cross-chain user demands', and 'cross-chain interaction pain points' in real-time, assisting all parties in formulating collaboration plans (such as 'DeFi projects on public chain A collaborating with NFT projects on public chain B to launch a 'cross-chain staking NFT mining' feature'); for 'DAO collaboration', the system provides 'data-driven proposal assistance tools', allowing DAO members to directly invoke 'treasury fund utilization efficiency data' and 'community member demand data' to generate proposal drafts without the need to manually collect and analyze data, improving DAO decision-making efficiency.
The development of this value-added layer transforms on-chain data from 'decision reference' to 'directly usable services and tools', achieving a closed loop of data value and shifting the collaboration in the Web3 ecosystem from 'experience-driven' to 'data-driven'.
Conclusion
The 'data dividend' of Web3 does not originate from the growth of total data volume but from the deep development of data value. The layered value system of Bubblemaps essentially builds a complete path for Web3 data from 'acquisition to application': the basic layer addresses the question of 'how data comes', the middle layer addresses the question of 'how data is interpreted', and the value-added layer addresses the question of 'how data is used'. This layered development model neither relies on fictitious technical concepts nor detaches from the real needs of Web3 users, but processes on-chain data through 'standardization, correlation, and service orientation', making data truly a core production factor driving user decision-making and ecosystem collaboration. When Web3 data is no longer 'numbers lying on the chain' but 'accessible, interpretable, and applicable value assets', the ecosystem can truly break through the predicament of 'data-rich but value-poor' and move towards a higher quality stage of development.@Bubblemaps.io
