Best Digital Asset Datasets for Optimal Use Cases
Digital asset datasets refer to collections of structured and organized data that pertain to various types of digital assets. These datasets encompass a wide range of information related to digital assets such as cryptocurrencies, tokens, NFTs, and other digital representations of value. They provide valuable insights into the performance, market trends, historical data, and other relevant metrics of these assets. Digital asset datasets are essential for investors, traders, researchers, and businesses seeking to analyze and understand the dynamics of the rapidly evolving digital asset ecosystem. By leveraging these datasets, users can make informed decisions, develop strategies, and gain a competitive edge in the digital asset market.
Recommended Digital Asset Datasets
Digital Asset Data - Kaiko CeFi and DeFI Market Data. Cryptocurrency prices | 1000's of instruments | Cryptoasset performance tracking | Investments
B2C Digital Audience|Identity Graph|ConsumerWatch Network (CWN) Consumer Household Data|284MM B2C Contact|80MM IP Address|300MM Consumer Hashed Email
Shopify, Woocommerce Data | Global Shopify Woocommerce Customers | 1M+ Contacts | (Verified Email, Direct Dials) | Decision Makers | 20+ Attributes |
Finance Continuum from Solution Publishing | 5.4m US B2B Professional Contact Data Set | B2B Contact Data | Verified Safe to Email
Endless Google Map Data Scraping Service
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NetWise Syndicated Programmatic/Digital Audience Segments available via preferred DMP/DSP and on-boarding platforms.
Andrews Wharton | Identity Graph Data | 1.8 billion Consumer Email database to power Identity Graph, Identity Linkage, and Customer Recognition
BatchService | Voter Data + Campaign & Election Data | 155M US Contacts | Political Campaigns Made Easy
Consumer Behavior Data | DrivenIQ | US Online Consumer Behavior Database
The Data Group's Hashed Email Data|Over 8 Billion US Records|Up to 90% Match Rates|Boost Onboarding & Indentity Graph Linkage|4 Bilion MAIDs Available
What are digital asset datasets?
Digital asset datasets refer to collections of structured and organized data that pertain to various types of digital assets. These datasets encompass a wide range of information related to digital assets such as cryptocurrencies, tokens, NFTs, and other digital representations of value.
What kind of information do digital asset datasets provide?
Digital asset datasets provide valuable insights into the performance, market trends, historical data, and other relevant metrics of digital assets. They can include information such as price data, trading volumes, market capitalization, historical price charts, transaction data, and more.
Who can benefit from using digital asset datasets?
Digital asset datasets are essential for investors, traders, researchers, and businesses seeking to analyze and understand the dynamics of the rapidly evolving digital asset ecosystem. By leveraging these datasets, users can make informed decisions, develop strategies, and gain a competitive edge in the digital asset market.
How can digital asset datasets be used?
Digital asset datasets can be used for various purposes, including market analysis, trend identification, backtesting trading strategies, risk assessment, portfolio management, and research. They provide users with the necessary data to evaluate the performance and potential of different digital assets.
Where can I find digital asset datasets?
Digital asset datasets can be found on various platforms and data providers specializing in digital assets. These platforms may offer free or paid access to datasets, depending on the level of detail and features provided. Some popular sources include cryptocurrency exchanges, data aggregators, and specialized data providers.
Are digital asset datasets updated in real-time?
The frequency of updates for digital asset datasets can vary depending on the data provider and the specific dataset. Some datasets may be updated in real-time, providing users with the most up-to-date information, while others may have a delay of a few minutes or hours. It is important to consider the data freshness when using digital asset datasets for time-sensitive analysis or trading purposes.