Kyvos Insights Case Studies E-waste Recycler Transforms Operations by Analyzing 4 Years of Kiosk Data

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	E-waste Recycler Transforms Operations by Analyzing 4 Years of Kiosk Data Kyvos Insights

E-waste Recycler Transforms Operations by Analyzing 4 Years of Kiosk Data

Kyvos Insights

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

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.

Impact #1	Query response times were almost instant, enabling users to deepen analytics across several dimensions.
Impact #2	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.
Impact #3	Ad hoc queries returned in less than 5 seconds, even on four years of data.

Impact #1

Query response times were almost instant, enabling users to deepen analytics across several dimensions.

Impact #2

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.

Impact #3

Ad hoc queries returned in less than 5 seconds, even on four years of data.

Benefit #1	5-second response time for ad hoc queries
Benefit #2	Significant reduction in querying costs on Snowflake
Benefit #3	Instant query response times

Benefit #1

5-second response time for ad hoc queries

Benefit #2

Significant reduction in querying costs on Snowflake

Benefit #3

Instant query response times

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Overview

E-waste Recycler Transforms Operations by Analyzing 4 Years of Kiosk Data

Operational Impact

Quantitative Benefit