Footprint Analytics

Footprint is an analysis platform to discover and visualize blockchain data.

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Inspiration

Market Size and Future Potential

  1. There were 77 million Bitcoin addresses by September 2021, which is a 42% yearly increase. Meanwhile, the DeFi market has exploded with a robust 6-times growth, and reached a cumulative total of 3.4 million users engaged in DeFi activities and investments.
  2. The global market for Big Data estimated at US$130.7 Billion in the year 2020, is projected to reach a revised size of US$234.6 Billion by 2026, growing at a CAGR of 10.2% over the analysis period.
  3. According to Statista, Global Big Data spending is forecast to reach almost 216 billion U.S. dollars in 2021
  4. We had the idea about establishing platform in June and formal releasing the data analysis information 2 months later, and currently is accumulating protocol data and covered ETH, BSC and Polygon so far.

What it does

The main features are following:

  1. 0 coding experience is needed for operation with drug and pull measures to finish the chart in a minute and 1k+ charts created by real users since August.
  2. All charts and dashboards are managed through labels that is counted as 18K+ in total that users can locate their content of interests easily.
  3. Data Analysis: 3 big data AI modeling analysis known as smart searching protocol pool model, smart decoding pool data and smart adjustment of the data modeling. The current AI modeling can reduce the time consuming from 3days to 3hrs with an accuracy over 95%.
  4. The API shall be completed by the end of the year that API is just a data acquiring method without any obstacles and the main emphasis are solving business owners problems as well as general users that 50+ new data indicator can be achieved within a month.

How we built it

  1. We establish the cooperation with protocols, institutions and developers that serve data analysis to expand the the influences for business owners and users such as we provide the project’s operational data services for curve and badger and they really appreciate our collaboration that we are working closely with Alpha and Lido for the moment.
  2. We started the strategic partnerships with various platforms for personalized service to expand the exposures as there are nearly 100 medias published our PR articles and the exploration tops 300K+ per month.

Challenges we ran into

  1. Not scalable when decoding protocols one by one, which brought about burden here
  2. First practicer into solving datas on multichain and NFT
  3. Indicators standardized in its correct and practicable ways

Accomplishments that we're proud of

By November 2021, Footprint covers 3 chains (ETH, BSC, and Polygon). Footprint has data of over 500 protocols with over 100 indicators.

  1. Build automatic and standardized model to parse the data 1.1 For parsing, Footprint directly get the on-chain logs and also call the contract 1.2 Specific process: log can be parsed into events & transactions, and then the on-chain data can be abstracted into a valuable data chart, which is more convenient to use
  2. Advantages of the model 2.1 Footprint data is not only in the perspective of protocol, but refining to the pool dimension to parse the operations indicators 2.2 Protocol parsing can be scaled up and automated by Footprint 2.3 One platform takes 1~3 days to parse before, but now it only takes 3 hours by using the model, while the accuracy reaches over 95%

What we learned

  1. our core is: to solve the problem of efficient production and efficient use of data on the blockchain chain
  2. Standardize and output the non-standard, non-semantic, non-business scenario-induced data on the chain.Solve the problems of standardization, semantics and scene of data. Let users see the relationship between data, analyze the causes of data generation, and build the analysis bridge of DeFi Lego.

What's next for Footprint Analytics

Cover Multichain NFT Data > Data's DAO community > WEB 3.0（Tokenized） Core competitiveness of our products:

  1. Not only build standards, but also develop standards into automated and intelligent models to improve the efficiency of data analysis and data quality verification.
  2. Not only provide raw data, but also abstract the data to make it more readable and meaningful.
  3. Provide a better UX and make it easier for users to use it.

Built With

  • databases
  • frameworks
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