Every path to assetization begins with standardization. Without it, there is no way to price, trade, or clear assets at scale. Finance has long understood this. Securities are standardized into units of shares; bonds follow strict contract formats with clear coupon rules; and commodities are defined by delivery quantities and quality grades in futures markets. All these conventions make assets liquid and markets possible. It also means, if data is to become an asset, it must undergo the same transformation. But why? Why Data Must Be Standardized Everything online is data. Every post, every click, every purchase, every location ping, all of it exists as data scattered across the internet, whether static or dynamic. These pieces are spread across platforms, stored in different formats, and governed by inconsistent rules. This makes data fragmented, diverse, and inconsistent. In such a raw state, it cannot enter a market, as it is too incompatible to be treated as a single class of asset. At this point, you might raise an objection: isn’t there already some form of standardization in the data industry? After all, companies buy, sell, and integrate datasets every day, and without some standards this would hardly be possible. That is true, but the kind of standardization that exists today is fundamentally different from what assetization requires. In the traditional data industry, “standardization” usually means creating labels, building taxonomies, or applying models that make data easier to classify and use. For example, customer demographics may be normalized into categories like age ranges or income brackets, and browsing histories may be tagged by content themes or purchase intent. These efforts serve an operational purpose: to make data interpretable, searchable, and ready for analysis. Yet, this form of standardization does not make data into an asset. Assetization operates under a different logic. In finance, standardization does not just describe assets, it transforms them into fungible, comparable, and contractible units. Take equities as an example. A company is infinitely complex. It includes assets, liabilities, governance, earnings potential, and risks. If investors had to negotiate investment terms based on these raw elements, every negotiation would be different — one buyer might want to price assets, another to discount liabilities, a third to argue over governance. No two trades would ever align, and a market could never scale. Standardization solves this by compressing all that complexity into a single unit: the share. One share represents the same slice of the company for all holders, making it fungible; mandatory reporting rules make shares comparable across companies; and legal frameworks tie rights and dividends to the unit, making it enforceable. In this way, the share turns an otherwise untradeable bundle of complexity into a liquid asset. Back to the data industry. The kind of “standardization” we see there cannot create fungible units, guarantee comparability across markets, or tie legal or contractual rights to data. In other words, it falls short of enabling data to be priced, traded, or cleared in the way financial assets are. So, what kind of framework could give data this level of standardization? DataFi provides the answer. Standardization in DataFi In DataFi, standardization begins at the proof level. When a user uploads a purchase history or browsing activity, it is converted into structured proofs (often with ZKPs), so each record follows a consistent schema. This makes proofs interpretable and comparable. But proofs alone do not circulate. At DDC, our solution is to wrap proofs into NFT-based containers, which act as exchangeable units in the marketplace. An NFT might represent a bundle of purchase records tied together by common attributes, with ZKPs inside providing verifiability. In this design, proofs define the format, while NFTs define the tradable unit. This is one path. Other projects explore different ones, such as feeding data directly into AI models and monetizing access via APIs. The field is still open. When it comes to pricing and execution, DataFi relies on the same foundation: smart contracts. Comparable schemas and metadata rules allow datasets from different regions or categories to be benchmarked side by side. Once a price is set, the contract encodes how the value flows. A part goes to the platform as a fee, part to the seller who packaged the NFT, and part is distributed to the original data contributors whose proofs are inside. All of this is enforced automatically on-chain, ensuring that both valuation and payout are transparent, auditable, and tamper-proof. In this way, pricing and enforceability are not separate steps, but two sides of the same mechanism. And together, cryptographic proofs, standardized schemas, and contract-bound rights push data beyond operational use. They give it the qualities assetization demands: fungibility, comparability, and enforceability. Conclusion The history of markets shows one truth: without standardization, there is no asset class. Finance proved this with shares, bonds, and commodities, each transformed from complexity into simple, tradable units. Data is now at the same threshold. For decades it has been collected, tagged, and modeled, but never in a way that made it liquid or enforceable as an asset. DataFi changes this by introducing cryptographic proofs, standardized schemas, and contract-bound distribution. This is not yet a universal standard — different projects are testing different routes, from proof-based exchanges to AI-driven monetization. But the direction is clear. Standardization is no longer just about making data usable; it is about making data tradable. And that is the decisive step that turns data from information into an asset class. About DataDanceChain DataDance is a consumer chain built for personal data assets. It enables AI to utilize user data while ensuring the privacy of that data. DataDance caters to both individual users and commercial organizations (brands). Through the DataDance Key Derivation Protocol, the network’s nodes achieve multi-layered privacy protection while being EVM-compatible. This ensures absolute data privacy while enabling rights management, data exchange, asset airdrops, and claims. Website: https://datadance.ai/ X (Twitter): https://x.com/DataDanceChain Telegram: https://t.me/datadancechain GitHub: https://github.com/DataDanceChain GitBook: https://datadance.gitbook.io/ddc DataFi 101: Why Standardization is the Key to Data Assetization was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this storyEvery path to assetization begins with standardization. Without it, there is no way to price, trade, or clear assets at scale. Finance has long understood this. Securities are standardized into units of shares; bonds follow strict contract formats with clear coupon rules; and commodities are defined by delivery quantities and quality grades in futures markets. All these conventions make assets liquid and markets possible. It also means, if data is to become an asset, it must undergo the same transformation. But why? Why Data Must Be Standardized Everything online is data. Every post, every click, every purchase, every location ping, all of it exists as data scattered across the internet, whether static or dynamic. These pieces are spread across platforms, stored in different formats, and governed by inconsistent rules. This makes data fragmented, diverse, and inconsistent. In such a raw state, it cannot enter a market, as it is too incompatible to be treated as a single class of asset. At this point, you might raise an objection: isn’t there already some form of standardization in the data industry? After all, companies buy, sell, and integrate datasets every day, and without some standards this would hardly be possible. That is true, but the kind of standardization that exists today is fundamentally different from what assetization requires. In the traditional data industry, “standardization” usually means creating labels, building taxonomies, or applying models that make data easier to classify and use. For example, customer demographics may be normalized into categories like age ranges or income brackets, and browsing histories may be tagged by content themes or purchase intent. These efforts serve an operational purpose: to make data interpretable, searchable, and ready for analysis. Yet, this form of standardization does not make data into an asset. Assetization operates under a different logic. In finance, standardization does not just describe assets, it transforms them into fungible, comparable, and contractible units. Take equities as an example. A company is infinitely complex. It includes assets, liabilities, governance, earnings potential, and risks. If investors had to negotiate investment terms based on these raw elements, every negotiation would be different — one buyer might want to price assets, another to discount liabilities, a third to argue over governance. No two trades would ever align, and a market could never scale. Standardization solves this by compressing all that complexity into a single unit: the share. One share represents the same slice of the company for all holders, making it fungible; mandatory reporting rules make shares comparable across companies; and legal frameworks tie rights and dividends to the unit, making it enforceable. In this way, the share turns an otherwise untradeable bundle of complexity into a liquid asset. Back to the data industry. The kind of “standardization” we see there cannot create fungible units, guarantee comparability across markets, or tie legal or contractual rights to data. In other words, it falls short of enabling data to be priced, traded, or cleared in the way financial assets are. So, what kind of framework could give data this level of standardization? DataFi provides the answer. Standardization in DataFi In DataFi, standardization begins at the proof level. When a user uploads a purchase history or browsing activity, it is converted into structured proofs (often with ZKPs), so each record follows a consistent schema. This makes proofs interpretable and comparable. But proofs alone do not circulate. At DDC, our solution is to wrap proofs into NFT-based containers, which act as exchangeable units in the marketplace. An NFT might represent a bundle of purchase records tied together by common attributes, with ZKPs inside providing verifiability. In this design, proofs define the format, while NFTs define the tradable unit. This is one path. Other projects explore different ones, such as feeding data directly into AI models and monetizing access via APIs. The field is still open. When it comes to pricing and execution, DataFi relies on the same foundation: smart contracts. Comparable schemas and metadata rules allow datasets from different regions or categories to be benchmarked side by side. Once a price is set, the contract encodes how the value flows. A part goes to the platform as a fee, part to the seller who packaged the NFT, and part is distributed to the original data contributors whose proofs are inside. All of this is enforced automatically on-chain, ensuring that both valuation and payout are transparent, auditable, and tamper-proof. In this way, pricing and enforceability are not separate steps, but two sides of the same mechanism. And together, cryptographic proofs, standardized schemas, and contract-bound rights push data beyond operational use. They give it the qualities assetization demands: fungibility, comparability, and enforceability. Conclusion The history of markets shows one truth: without standardization, there is no asset class. Finance proved this with shares, bonds, and commodities, each transformed from complexity into simple, tradable units. Data is now at the same threshold. For decades it has been collected, tagged, and modeled, but never in a way that made it liquid or enforceable as an asset. DataFi changes this by introducing cryptographic proofs, standardized schemas, and contract-bound distribution. This is not yet a universal standard — different projects are testing different routes, from proof-based exchanges to AI-driven monetization. But the direction is clear. Standardization is no longer just about making data usable; it is about making data tradable. And that is the decisive step that turns data from information into an asset class. About DataDanceChain DataDance is a consumer chain built for personal data assets. It enables AI to utilize user data while ensuring the privacy of that data. DataDance caters to both individual users and commercial organizations (brands). Through the DataDance Key Derivation Protocol, the network’s nodes achieve multi-layered privacy protection while being EVM-compatible. This ensures absolute data privacy while enabling rights management, data exchange, asset airdrops, and claims. Website: https://datadance.ai/ X (Twitter): https://x.com/DataDanceChain Telegram: https://t.me/datadancechain GitHub: https://github.com/DataDanceChain GitBook: https://datadance.gitbook.io/ddc DataFi 101: Why Standardization is the Key to Data Assetization was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story