Venture: IPE
IPE uses information transparency to drive reductions in industrial pollution across China.
Expert: Ruby Goel
Lead Data Architect at Visa
• Sr. Data Architect, Verifone
• Sr. Data Architect, Paypal
IPE partnered with Rippleworks to manage their growing environmental database
The Institute of Public and Environmental Affairs (IPE) uses information transparency to drive reductions in industrial pollution across China. Their Blue Map Database aggregates environmental data, illustrating the supply chains and specific factories that are contributing to air and water pollution; with it, IPE lobbies large corporations with manufacturing interests in the county to encourage better compliance with environmental standards. To date, IPE has worked with nearly 100 multinational brands and motivated more than 14,000 factories to improve environmental performance.
IPE’s top priority was improving their data infrastructure to enable a doubling of annual impact. IPE partnered with Rippleworks to learn the skills and tools needed for managing increased data volume.
The team worked with experts to identify opportunities to optimize our database for more stable performance and better user experience.
The discussions and dialogues are especially precious as it allows us to look beyond our day to day tasks, reflect and think beyond.
It also helped boost our confidence and the team is more ready than ever to achieve our mission of facilitating green development for the protection of blue sky and clean water.
—IPE Director Ma Jun
IPE partnered with Rippleworks Expert Ruby Goel, a leading data architect with experience building and managing complex and large database environments at Ebay, Cisco, Paypal, Verifone, and Visa. Together they:
• Learned how to manage increasing data volume through data storage and archiving strategies, and built a long term plan for system upgrades.
• Assessed IPE’s data system, highlighting possible bottlenecks and weak points, and other impediments to growth
• Learned techniques to reduce IOPS/cost, data storage and archiving strategies, and techniques to remove and prevent duplicate data
• Learned best practices for scaling IPE’s data architecture in a cost effective and reliable way in the long term
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