Cecil provides a secure SQL database that standardizes and harmonizes global nature datasets, enabling users to perform time-series and spatial analysis efficiently. By consolidating data from multiple providers into a consistent format, Cecil eliminates compatibility issues and accelerates the process of deriving actionable insights from environmental data.
Funding
$4.4M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Analyzing global nature datasets is challenging due to inconsistencies in data formats, measurement units, and spatial resolutions across different providers. This lack of standardization hinders efficient time-series and spatial analysis, delaying the extraction of actionable insights.
Solution
Cecil provides a secure SQL database that standardizes and harmonizes global nature datasets, enabling users to perform time-series and spatial analysis efficiently. By consolidating data from multiple providers into a consistent format, Cecil eliminates compatibility issues and accelerates the process of deriving actionable insights from environmental data. The platform consolidates nature datasets into relational database models, harmonizes measurement units, and reprojects pixel grids to a baseline CRS and spatial resolution. Users gain secure SQL access to a database optimized for time-series and spatial analysis at scale, allowing them to join large datasets from multiple data providers and perform complex aggregations and computations.
Target Audience
Cecil's primary customers are organizations and researchers requiring efficient analysis of global nature datasets, including those focused on environmental monitoring, conservation, and sustainability.
Features
- Standardized SQL database for nature datasets
- Harmonized measurement units across datasets
- Reprojection of pixel grids to a baseline CRS and spatial resolution
- Secure SQL access optimized for time-series and spatial analysis
- Python SDK for integration with data operations
- Support for plant aboveground biomass, land use, and land cover datasets