E-waste Recycler Transforms Operations by Analyzing 4 Years of Kiosk Data
Company Size
1,000+
Region
- America
- Europe
Country
- Other
- United States
Product
- Kyvos
- Smart OLAP™
- Smart Semantic Layer™
Tech Stack
- Snowflake
- AWS
- Tableau
- S3
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Real Time Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Recycling & Waste Management
- Retail
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Predictive Maintenance
- Predictive Quality Analytics
- Supply Chain Visibility
Services
- Data Science Services
- System Integration
About The Customer
The customer is a leading e-waste recycling company that aims to reduce electronic waste by making it easier for users to sell their unused devices. They have placed automatic kiosks across thousands of retail outlets in the US and Europe, allowing users to drop their devices, negotiate prices, and receive on-the-spot payment within a few minutes. The company is committed to creating a greener planet and improving business efficiency. They wanted to analyze four years of kiosk transaction data to support decisions such as where to place the next kiosk, how to reduce wait times, and how to tailor quotes based on user acceptance rate.
The Challenge
The e-waste recycling company was facing several analytical challenges on their Snowflake / AWS / Tableau platform. They were experiencing severe degradation in query performance while analyzing multiple years of data in Snowflake. There was no semantic layer to define consistent data models and standardize KPI calculations. The company was also dealing with unpredictable querying costs on their Snowflake data warehouse, with bills running very high at times. They found it difficult to model their data and deal with multi-level hierarchy and one-to-many joins between facts.
The Solution
The company chose Kyvos to build OLAP on AWS with Snowflake as the data source. Using its Smart OLAP™ technology, Kyvos built a high-performing Smart semantic layer™ directly on their S3 platform. It pulled data from Snowflake and created optimized OLAP models with complex schemas and advanced calculations. Once Kyvos was deployed, query response times were almost instant, enabling their users to deepen analytics across several dimensions. A unified semantic layer helped define all their business logic in one central place, and all BI tools could connect to this layer instead of everyone doing their own calculations. As all the aggregates were calculated in advance, ad hoc queries returned in less than 5 seconds, even on four years of data. Kyvos’ build-once-query-multiple-times approach helped them control budget and reduce querying costs on Snowflake.
Operational Impact
Quantitative Benefit
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