Unified Data Solution Boosts Agricultural Insights & Leads to 70% CSAT Increase
Our client is a private investment company based in the United States that specializes in agriculture and food-related investments. It was founded by a group of seasoned agriculture and finance professionals with a vision to empower and develop sustainable agriculture businesses worldwide.
The company invests in various agriculture-related assets, including farmland, livestock, aquaculture, agribusinesses, and food companies.
Business Analysis, Project Management, Experience Design, Software QA and Testing, Cloud & DevOps Solutions, Software Architecture
Business Challenges
Lack of a unified data storage
Due to the lack of unified data storage, the Client’s Team decided to make an extra effort and create a platform that would manage such data and, most importantly, be able to visualize it for data-based decision-making. There are plenty of public agricultural data sources, such as NASS, USDA, and NAV, that they can use in such a solution.
How we worked
1. Coordinated sharing responsibilities with the client
Sombra Team proposed a dedicated development team engagement model to the client. This model involves both sides sharing duties as follows:
| Client Team | Sombra Team | Shared responsibility |
|---|---|---|
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2. Implemented the best project management practices
Our dedicated development team operates in a location with a seven-hour time difference from our client. To take advantage of the time zone difference, we implemented the most efficient project management standards and communication practices.
3. Reducing the gap between the idea and the product
The Client required Sombra’s professional support in testing the proof of concept to see whether we could scale the solution to work with bigger, complicated data. Then, we’d advise and participate in architecture creation based on the Client’s requirements. We brought in a business analyst and UX/UI Designer to provide a more holistic approach for the client, crystalize the vision, and prioritize the requirements.
4. Approach to technical implementation of the solution
Tech Stack:
Delta Lake on S3 (Medallion architecture), Data Catalog on DynamoDB;
Spark on Amazon EMR for processing;
Data clients: Plotly
- Captured data from both private and public sources, including NASS, USDA, and NAV.
- Implemented near-real-time distributed data processing using Apache Spark on Amazon EMR.
- Ensured data quality by utilizing a medallion architecture (three-level data architecture), providing normalized analytical data to Data Clients for business intelligence (BI) and reporting.
- Enabled access to historical and transactional data by applying a Delta Lake architecture, ensuring consistency.
- Implemented tenant-level secured data access and enforced strict access policies to maintain data security.
Business Value
With the implementation of a new solution, Sombra’s Client achieved a 70% increase in customer satisfaction (CSAT) by:
- Strengthening collaboration with investors through improved and comprehensive data access.
- Establishing a single source of truth for agricultural asset data tailored for each stakeholder.
- Enhancing customer experience with automated calculations, report generation, and advanced data visualization.
- Integrating with other internal tools.
As we continue working with the client, we focus on developing a data permission architecture to provide security filters and data access sets to determine what data users can see in the solution.
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