November 1, 2023
New research brought by decentralised.co analyzed and compared the various data providers in crypto. The research, led by Founder, Joel John and Saurabh Deshpande at Decentralised compiled comprehensive information on data providers highlighting the factors affecting costs, such as data granularity, infrastructure needs, client base, and sales cycle duration. In this competitive landscape, Amberdata was ranked the top choice for financial institutions seeking the deepest and most granular digital asset data. The research emphasizes that the context surrounding data is what truly distinguishes one data product from another. While many products draw from the same underlying data sources, it is the unique context they provide that makes them appealing to different audiences.
Amberdata maintains historical order book data from exchanges, empowering traders and investors to build and test various models. Our commitment to preserving this detailed historical data positions them as a go-to solution for those in need of comprehensive, real-time, and tick-by-tick order book data.
Amberdata's offering is tailored to serve the needs of financial institutions, a critical consumer persona in the cryptocurrency data product landscape. These institutions, particularly during bear markets, demand the most granular data with frequent updates. Amberdata's focus on providing data for pre-trade decisions, post-trade reporting, taxation, and compliance aligns perfectly with the requirements of this sector. Moreover, our data is presented in a format that allows clients to carry out proprietary analyses and build visualizations according to their preferences.
In the following sections, we highlight some of the key findings directly from the report.
Leveraging Context in Cryptocurrency Data Products
In the cryptocurrency and blockchain technology sector, data is not merely information but a critical asset, and the way this data is presented significantly impacts its value. Recently published research by decentralised.co dives into the complex landscape of data products within the cryptocurrency and blockchain realm, emphasizing the central role that context plays in shaping their value. This understanding unlocks insights into how various data products cater to the distinct requirements of users, whether they are financial institutions, developers, researchers, or retail investors.
Contextualizing Data: The Engine of Value
At the core of this study is the premise that context is the key to transforming raw data into actionable insights. Notably, data products that have flourished in the crypto space are those that provide context around the data they offer. For instance, financial data powerhouse Bloomberg excels in converting complex financial data into easily consumable information for investors and traders. Similarly, companies like Similarweb and Newzoo leverage their expertise to infuse social or gaming-related context into the data they track. Meanwhile, blockchain-native data products offer a unique advantage by providing user context through queries that answer specific questions relevant to particular user segments. For example, TokenTerminal calculates the economic fundamentals of blockchain protocols, Nansen assists market participants in understanding asset movements, and Parsec offers on-chain data queries for in-depth DeFi analysis. These products, often drawing from the same underlying data sources, distinguish themselves through their unique contexts, making them appealing to different target audiences.
On-chain vs. Off-chain Data: A Crucial Distinction
A pivotal factor that shapes the landscape of cryptocurrency data products is the origin of the data—whether it is derived from on-chain or off-chain sources. Some data products use a combination of both, but the differentiating factor is the manner in which they present the data to users. The research underscores that data providers employ context filters to create products, mirroring the niche-based approach seen in Web 2.0 data products. In the Web 3.0 environment, data companies establish competitive advantages based on their core competencies.
The backgrounds of the product's founders often determine the nature of the products they create. Teams with extensive experience in traditional capital markets often model their products after Bloomberg, while those deeply ingrained in the crypto space tend to resemble Nansen. Different products cater to distinct needs, even when sourcing data from the same underlying blockchain infrastructure. For example, exchanges typically discard data after predefined intervals as they are not inherently data-focused entities. Storing historical data necessitates additional resources, and companies like Amberdata seize this opportunity by maintaining historical order book data, enabling traders and investors to build and test various models. In contrast, products like Nansen or Arkham offer insights into the on-chain behavior of specific addresses or entities, targeting specific needs within the broader data ecosystem.
Market-map is not representative of every player in every category
Understanding Consumer Personas: A Blueprint for Market Positioning
To decipher how data products are strategically positioned in the market, the research introduces four key consumer personas within the cryptocurrency and blockchain data space.
Financial Institutions
During bear markets, a significant portion of funding for crypto-data products comes from financial institutions. These clients harbor complex data requirements, often necessitating a detailed sales process to determine costs. Their demand extends beyond pre-trade decisions to encompass compliance and taxation requirements. Companies like Amberdata are well-positioned to cater to these customers. However, cracking into this market often requires founders with backgrounds in institutional finance or well-funded teams, given the high barriers to entry.
Developers
In the Web 3.0 paradigm, the concept of composability means that applications often rely on data from various sources, necessitating rapid data access. This data is stored across different blocks on chains like Ethereum and Solana. Indexing services like Covalent, Graph, Chainlink, and Powerloom step in to ensure that data is stored in the desired format for developers, accessible via simple API calls. An emerging segment in this persona involves tools designed to understand user behavior. For instance, ARCx allows developers to bridge off-chain data (like browser behavior) with on-chain data (like wallet addresses), capturing demographic information of users interacting with decentralized applications (dApps).
Researchers and Publications
Collaborating with researchers and publications is a significant distribution channel for data products. Researchers often rely on data products to save time and effort in data collection, cleaning, and curation. Some products, such as Dune, build communities of analysts who compete to rank higher on their lists, while publications like The Block and Delphi leverage data from third-party providers to create informative dashboards. However, smaller researchers may face budget constraints, which can be a challenge for data providers.
Retail Investors
Products catering to retail investors typically offer less granular and frequent data. Still, they have the potential for high profitability due to economies of scale. Many retail-focused products are either free or supported by advertisements, serving a broad user base. New delivery mediums, such as data delivered as notifications through platforms like Telegram, open up new market categories, even within the domain of data products.
B2B vs. B2C Orientation
The research categorizes data companies into two orientations: B2B and B2C. B2B companies like Amberdata serve sophisticated actors, offering granular, frequent, and detailed data for a fee. This caters to demands like building and testing models, pre-trade analysis, post-trade reporting, taxation, and compliance. Data is provided in a format that empowers customers to perform proprietary analyses and create visualizations according to their preferences. These companies typically offer their products behind a paywall.
Cost is generally a function of granularity due to infrastructure requirements, the nature of the clientele involved, and the length of the sales cycle
Revenue Generation and Market Gaps
Retail-focused companies typically have lower revenue per customer and rely on a large user base to sustain their business, aligning with the freemium model commonly seen in the internet industry. The top of the funnel, with a large number of free customers, is crucial for generating revenue, even if the conversion rate is relatively low. Companies can also consider generating revenue from advertisements when their websites attract many visitors, as observed with CoinGecko.
In conclusion, this research offers a comprehensive examination of the cryptocurrency and blockchain data product landscape. It emphasizes the crucial role of context in shaping the value of data products and provides valuable insights into the distinct user personas, the dynamics of on-chain and off-chain data, and the evolving market landscape, including the identification of gaps and opportunities for more specialized products. This understanding is indispensable for companies operating in this domain as they navigate a rapidly evolving and fiercely competitive environment.
Amberdata's recognition as the leader in terms of depth and granularity in the cryptocurrency data product industry is well-founded. This distinction is highlighted by our dedication to preserving historical order book data and tailoring our services to meet the complex demands of financial institutions. By providing detailed, real-time, and tick-by-tick data, Amberdata has cemented its position as the go-to solution for those who require in-depth and granular insights into the digital asset space.
Learn more about the crypto data landscape as this is just a subset of the decentralised.co report. The full report covers many more topics including, the start of the digital age, moulding raw data, economics moats, and financial speculation.
Read the complete research report HERE:
