# Band Protocol upgrades BandChain network to phase 2

“# Band Protocol upgrades BandChain network to phase 2 ”
The project hopes to allow for access to more data sources and richer data types for traditional enterprises by simplifying access to decentralized apps, or dApps. The upgrade also seeks to allow institutional data providers partnered with Band Protocol to expand their data coverage and offerings to on-chain developers.
Soravis Srinawakoon, CEO and Co-Founder of Band Protocol, explained:
“After prioritizing the onboarding of decentralized applications, we are directing our focus to bridge the gap between mainstream enterprises and the blockchain industry. There is enormous potential for traditional enterprises to create new business models and revenue streams by integrating their valuable data with decentralized ecosystems enabled through Band Protocol,”
According to the release, BandChain 2 will also allow smaller data providers the opportunity to create new revenue streams, while enhancing the quality of the data by simplifying the onboarding process.
Related: Oracle-focused tokens rally as DeFi searches for trusted data providers
The project stated that BandChain has served more than 7.7 million data requests, and Phase 2 will allow support for eight times more requests per block and the execution of oracle scripts which are 10-15 times faster. Greater interoperability will also be coming with Phase 2. This is achieved through the implementation of the IBC protocol allowing any dApps implementing the IBC standard to request data from BandChain without the need for additional validation.
Since its launch in 2018, Band Protocol has stated that it has secured more than $10 billion of value locked in smart contracts without incident.
In April Google Cloud incorporated technology from Band Protocol to enable “immediate and accurate analysis of financial time series data,” according to Kevin Lu, head of business development at Band. Google Cloud translates financial time series data into real time analytics through machine learning.
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