Industry use cases

IBM Environmental Intelligence offer an extensive range of geospatial-temporal data layers, providing businesses with critical knowledge to maintain a competitive edge in the age of machine learning and AI. The repository includes hundreds of harmonized, analytics-ready data layers such as satellite imagery, weather, demographic, land, sea, and agricultural data. Businesses can further extend this library by efficiently ingesting, curating, and integrating their proprietary data with these layers to gain deeper insights and make scalable, cost-efficient decisions.

Industry use cases

Environmental Intelligence enable diverse applications across various industries:

• Governments can derive actionable insights on health, disease, and weather to better advise the public during natural disasters or pandemics.
• Electricity utilities can predict disruption risks more accurately to optimize maintenance and crew schedules.
• Insurers can project risk more effectively and alert policyholders before issues occur, enhancing efficiency.
• Lenders can improve credit risk scoring methodologies to reduce bad loan placements.
• Food companies can respond to consumer demand for higher quality and meet sustainability goals.

All industries can optimize data queries and quickly retrieve relevant information, even when dealing with datasets of varying spatial and temporal resolutions.

Data ingestion

Environmental Intelligence streamlines data ingestion from various sources (FTP, AWS, APIs) while managing provider licensing and curating datasets for data scientists and organizations. This process simplifies data scientists' work during querying and model building.

Querying

After data ingestion and normalization in Environmental Intelligence, cross-domain querying becomes more efficient, accelerating data science workflows. The API's query language allows for "push-down" data reduction and algorithms to be executed within the platform, reducing the need for local data processing and handling large datasets.

Overall value proposition

• Access to a growing number of analytics layers
• Accurate and consistent results
• Reduced time to value