Web Development Company Melbourne | Web Development Services

Company Background

The client is a publicly listed Australian utility company involved in the generation and retailing of electricity and gas for residential and commercial use. The company operates a significant generation capacity of 10,984 MW, with 85% of this capacity derived from coal-fired plants. This dual-role as both generator and retailer places the company in a unique position within the market, necessitating robust and efficient management of both operational and customer-facing activities.

Problem Statement

The company faced multiple challenges impacting its operations and customer service:

Debt Management

A significant portion of customers were unable to make timely payments for their energy usage, leading to increased debt. This non-payment resulted in financial strain, causing revenue losses due to bad debt.

Data Management

The existing data management systems were siloed and inefficient. The need to streamline and centralise data processing was critical to improve decision-making, forecasting, and operational efficiency.

Operational Efficiency

The disparate data systems and lack of centralised data storage led to inefficiencies in handling operational data critical for market settlements and metering decisions.

Machine Learning Integration

Existing ML models were used in isolation, lacking integration and streamlining, thus reducing their effectiveness in improving business operations.

Solution

To address these challenges, a comprehensive solution was implemented leveraging advanced data management and machine learning technologies:

Centralised Data Platform
- Data Integration:
  Created a centralised data lake using Azure Storage and Databricks to aggregate data from multiple source systems.
- Streaming Framework:
  Implemented a Spark streaming framework to handle data ingestion, ensuring real-time data availability for critical operational reports.
- Data Layers:
  Organised data into different layers (staging, raw, integrated, and transformed) for efficient processing and retrieval.
- ML Platform:
  Established an enterprise ML platform with a feature store accessible to all ML engineers and data scientists, minimising feature duplication and fostering collaboration.
Early Debt Detection with AI/ML
- AI/ML Models:
  Utilised machine learning algorithms to predict customers with a high propensity to fall into debt, based on transactional and behavioral data from the ERP system.
- Data Privacy Compliance:
  The solution was hosted within the company’s environment to comply with stringent data privacy policies.
- Causal Analysis:
  Conducted thorough causal analysis to understand the underlying factors leading to debt, enabling targeted interventions.
- Specialist Teams:
  Formed specialist teams to investigate and address potential issues identified by the AI/ML models, such as billing errors or faulty meters.
Operational Improvements
- Performance Optimisation:
  Leveraged Databricks' optimisation features such as adaptive query execution and dynamic partition pruning to enhance processing speed and resource utilisation.
- Data Governance:
  Implemented centralised data governance using Databricks Unity Catalog and Collibra to ensure compliance and data integrity.
- MLOps Integration:
  Developed end-to-end MLOps pipelines using Databricks and Azure DevOps to automate model deployment and monitoring.

Benefits

The implementation of these solutions yielded significant business outcomes:

67%

Debt Reduction

$135 Million

Revenue Realisation

Debt Reduction:
- Achieved a 67% precision in predicting potential bad debts, leading to a potential revenue realisation of $135 million by preventing customer debt.

Operational Efficiency:
- Streamlined data processing and reduced compute costs through optimised data management and processing frameworks, delivering near real-time data availability.

Cost Savings:
- Reduced storage costs by efficiently managing vast amounts of historical data without purging, using cost-effective storage solutions.

Enhanced Decision-Making:
- Provided a single source of truth for business forecasting and analytical reporting, improving decision-making processes.