Water utilities company manages billions of data points with Elastic

Complex data management drains teams

As the organization scaled its reach and technological footprint, the sheer volume and complexity of its operational data introduced new obstacles. Meeting the demand for sustainable water management, reliable service, and regulatory compliance requirements became increasingly difficult with existing systems. The primary challenges included:

• Unprecedented data growth: The adoption of smart meters, sensors, and wireless technologies generated over a billion data points every day — volumes the legacy SQL-based systems simply could not handle at scale.
• Manual infrastructure strain: Legacy databases required constant manual configuration, including data partitioning and scaling read capacity, which placed extra pressure on engineering teams already stretched thin.
• Operational bottlenecks: Instead of focusing on analysis and insights, teams spent time bogged down on ongoing maintenance and infrastructure upkeep, stalling innovation and slowing responsiveness.
• Disconnected data silos: Operational silos limited visibility for teams needing unified data to detect leaks early, optimize water distribution, and quickly address customer inquiries.
• Compliance and regulatory complexity: Rapidly evolving federal and state requirements around water loss reporting meant operations needed to adapt and generate precise, audit-ready data for compliance.

Empower smarter, faster water management with Elastic

To address these mounting challenges and unlock the value hidden within its vast data streams, the company turned to the Elasticsearch Platform to provide real-time water usage insights to customers. The solution allowed the company to:

• Unify massive, disparate data sources: The Elasticsearch Platform serves as the foundation to collect, index, and unify data streams from millions of smart meters, sensors, and operational systems — turning high-volume data into a single source of truth for every team.
• Enable natural language search and AI insights: Users can ask natural language questions of the data using retrieval augmented generation (RAG) through an integration with Elastic’s vector database using OpenAI's large language model (LLM). When a user wants to understand the source of a leak, they can just ask: "What are the potential sources of this leak?" The AI then analyzes usage patterns and identifies possible causes, such as an outdoor leak or a gradual increase in consumption.
• Deliver real-time, actionable dashboards: Elastic Observability — an out-of-the-box solution built on the Elasticsearch Platform — powers interactive dashboards that provide deep visibility and customizable filters. Utilities can analyze trends, pinpoint high-volume consumers, and visualize industrial usage in a few clicks.
• Adopt future-ready data architecture: Early adoption of Elastic’s time series data streams (TSDS) allows efficient storage and rapid search of large-scale time series data, removing bottlenecks while supporting growth and new AI use cases.

Meeting regulations and exceeding customer expectations

After implementing the Elasticsearch Platform and Elastic Observability, the company transformed its operations, moving beyond simple data storage to extract meaningful insights, automate processes, and enhance service delivery across the board. The impact extends from real-time decision-making in the field to elevated customer trust and robust compliance readiness. Key results include:

• Predictive, data-driven decision-making: With Elastic, the company moved from reactive operations to a proactive, predictive approach, delivering measurable results for utilities and customers alike. For example, it shifted to early leak detection and real-time resource optimization, reducing water loss.
• Improved customer service experiences: The company made it possible for utilities providers to deliver transparent, evidence-based customer service by pinpointing the exact day of high water usage when bill disputes arise.
• Simplified compliance reporting: Elastic simplifies regulatory reporting, supporting accurate loss figures for federal and state agencies and incorporating data from advanced sensors for audit-ready compliance.
• Operational efficiency and scalability: By consolidating disparate datasets and automating manual maintenance tasks, engineering talent is freed to focus on innovation, not infrastructure.
• Predicting issues and detecting trends: The company can perform system-wide calculations fast by efficiently aggregating large volumes of structured data. Teams can seamlessly generate custom charts to observe industrial water use or pinpoint high-volume consumers like data centers.

Scalable, flexible platform that adapts to future needs

Choosing a partner to support such a significant transformation requires trust in both the technology and the team behind it. The water utilities company selected Elastic after evaluating its ability to address not only today’s pressing challenges, but also to provide a scalable, adaptable platform for future needs. Elastic delivered a comprehensive platform that empowered engineers and positioned the company as a leader in data-driven water stewardship.

Elastic stood out as the strategic backbone for the company’s transformation for key reasons, including:

• Effortless scalability and observability: Elasticsearch Platform delivers at enterprise scale, helping to eliminate tool sprawl and reduce operational costs.
• Seamless integration and flexibility for AI innovation: Elastic’s ecosystem flexibility — including integrations with LangChain and other tools as well as the ability to use any model — lets teams explore new use cases without introducing additional point solutions.
• Unified, future-proof foundation: By bringing together all data into a single platform, Elastic makes it possible for AI models and operations teams alike to accurately and confidently query, analyze, and act on information.
• Data aggregation capabilities that enable real-time insights: By efficiently aggregating large volumes of structured data, the company can perform system-wide calculations at scale with rapid response times.