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Databricks > Case Studies > Cleanaway's Transformation: Leveraging IoT for a Smarter and Cleaner Australia
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Cleanaway's Transformation: Leveraging IoT for a Smarter and Cleaner Australia

Technology Category
  • Analytics & Modeling - Machine Learning
  • Sensors - GPS
Applicable Industries
  • Recycling & Waste Management
  • Utilities
Applicable Functions
  • Maintenance
  • Sales & Marketing
Use Cases
  • Fleet Management
  • Predictive Maintenance
Services
  • Data Science Services
The Challenge
Cleanaway, Australia’s leading waste and recycling services provider, faced a significant challenge in managing data from its diverse operations. The company's operations, including waste collection, sorting, and logistics, involved different IT systems, leading to data being scattered across multiple, disjointed sources. This data sprawl was a major obstacle as the complexity and variability of Cleanaway’s services generated large volumes of operational and service data from various sources, including GPS and connected fleet systems, and structured and unstructured data from customer transactions, sales, marketing, and more. The company's ambition to become an efficient and profitable data-first business as part of its Blueprint 2030 objectives was hampered by these data silos, which impeded the sharing of actionable insights and resulted in unreliable insights due to inconsistent data quality.
About The Customer
Cleanaway is Australia’s leader in delivering efficient waste and recycling services daily to millions of households and facilities across the country. The company is committed to reducing the environmental impact of waste as part of its mission to provide a cleaner, safer planet for future generations. Cleanaway's diverse operations include waste collection, sorting, and logistics, and the company is dedicated to using data to support sustainable practices. As part of its Blueprint 2030 objectives, Cleanaway aims to become an efficient and profitable data-first business, leveraging data to drive responsive and well-informed decisions.
The Solution
To overcome these challenges, Cleanaway turned to Databricks to implement a lakehouse architecture that could function as a single source of truth by unifying disparate and siloed data sources. The Databricks Lakehouse was chosen for its strong reputation of unifying data silos at speed, scaling analytics and AI at cost, and providing exemplary technical support. The company was able to deploy Databricks Lakehouse on Azure quickly and easily, enabling it to roll out modern, automated use cases for business intelligence in less than a year at lower costs. Cleanaway operationalized its first advanced analytics and machine learning models using MLflow and Delta Lake together with Power BI to better analyze changes to business performance metrics and to leverage key insights to improve decision-making across all aspects of the business. One of its first models in production was used to assess and optimize the most efficient daily routes possible for Cleanaway’s fleet of over 2,800 solid waste services collection vehicles.
Operational Impact
  • The implementation of the Databricks Lakehouse has transformed Cleanaway's operations, eliminating the silos that traditionally separated and complicated data engineering, analytics, and machine learning. This shift has not only accelerated its time to insights and ability to innovate but also boosted efficiencies and profitability in less than a year after it implemented analytical and machine learning capabilities built on Databricks Lakehouse. The transformative impact of operationalizing advanced AI models for its fleet management systems has also encouraged Cleanaway to expand its route optimization abilities across its entire fleet of 5,000 vehicles. Looking ahead, Cleanaway plans to extend the implementation of Databricks Lakehouse in every aspect of the business, transforming the company toward being truly data and AI driven by 2030.
Quantitative Benefit
  • Cleanaway was able to roll out modern, automated use cases for business intelligence in less than a year at lower costs.
  • One of the first models in production optimized the most efficient daily routes for Cleanaway’s fleet of over 2,800 solid waste services collection vehicles.
  • Cleanaway was able to achieve an aggregated view of each customer from across multiple systems, enhancing its ability to respond to customer demands and highlight new growth opportunities.

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