Surviving the Data Tsunami: Best Practices Every Utility Can Deploy

The utility industry stands at the epicenter of a data revolution. Smart meters, IoT sensors and cloud-based platforms are generating a massive tidal wave of ever-increasing volume and variety of data. This never-ending data tsunami creates complexity and muddles productivity. Worse still, it generates waste and incurs avoidable costs. Staying afloat requires moving beyond merely collecting this torrent of data.

To survive and thrive, it’s essential to lean into true digital transformation, turning raw and unstructured information into actionable intelligence. By doing so, utilities can leverage a stream of solutions, from AMI 2.0 to modern geospatial software (ArcGIS Utility Network), field mobility, cloud computing, artificial intelligence (AI) and distributed energy resources. These solutions can work together to infuse every aspect of operations with data-driven decision-making that supports operational efficiency, customer satisfaction, grid reliability and innovation.

The Undercurrents of Data Overload

Utilities today must grapple with data inundation due to an expanding array of sources, including smart meters, substations, transformers, customer portals, drone imagery and third-party providers. This surge in data volume and diversity stretches the limits of traditional IT and OT ecosystems. And it’s not just the sheer size of data stores, but their heterogeneity: structured, unstructured and semi-structured data arrive in disparate formats, making integration and analysis a complex and time-consuming task.

Data fragmentation is a persistent issue, especially in organizations that have grown through acquisition or operate with a high degree of autonomy across business units or geographic markets. Each unit may have its own systems, standards and data governance practices, resulting in “dirty data” that is difficult to reconcile. Data silos hinder the creation of a unified network view, undermining situational awareness and operational agility.

The proliferation of alerts from smart meters and other sources adds another layer of complexity. Many alerts are challenging to diagnose and can generate false positives, leading to unnecessary field checks and wasted resources. Utilities often find themselves drowning in data and unable to see the currents that matter—overwhelmed by the sheer volume of signals and lacking the tools to distinguish genuine issues from noise.

Compounding these technical challenges are organizational and human factors. Utilities often lack sufficient data engineering and analytics talent, competing with other industries for a limited pool of skilled professionals. Legacy systems, manual processes, and a culture of reactive problem-solving further impede progress. As utilities modernize—especially when preparing data for integration into advanced models like the ArcGIS Utility Network—the need for high-quality, well-prepared data becomes even more acute.

The rise of external data sources, such as e-mobility programs that tap into automotive, transportation, and cloud-based systems, introduces new vulnerabilities and governance challenges. Data outside the utility’s direct control must be accessed, secured, and integrated—a task complicated by concerns related to privacy, regulations, and cybersecurity.

Ultimately, the shift toward real-time, data-driven decision-making in grid operations heightens the importance of data quality. Errors that might have been inconvenient in batch processing can have serious, even irreversible, consequences when they impact real-time dispatch, restoration, or customer service.

Challenges include:

• The overwhelming volume and variety of data from diverse sources
• Persistent data fragmentation and silos due to organizational structure and legacy systems.
• High rate of false positives and noise in alerts leads to inefficiency and wasted resources.
• Shortage of skilled data talent and reliance on outdated, manual processes.
• Increased complexity and risk from external data sources, regulatory requirements and real-time operations.

Best Practices for Managing the Data Tsunami

As utilities grapple with an unprecedented surge of data from smart meters, sensors and external sources, the ability to turn this raw and unstructured data into actionable insights becomes a strategic business enabler—the difference between utilities operating with lean business efficiency and those bogged down in a data deluge. 

But there are practical, proven and repeatable best practices that allow organizations to harness the full potential of their data. Developed over several years from integrating systems, implementing enterprise applications and connecting information and operational technologies, the following recommendations offer opportunities for innovation and improved service.

Invest in Scalable Data Ecosystems

The foundation of effective data management is a scalable, flexible data ecosystem. Utilities must invest in infrastructure that can ingest, curate and serve large volumes and varieties of data—structured, unstructured and time series—across both IT and OT environments. Cloud and hybrid cloud solutions are essential, offering the speed, resilience and cost efficiency needed to keep pace with evolving data demands. Modern ecosystems must support integration with edge devices, external data sources and advanced analytics platforms, enabling utilities to future-proof their operations and unlock new use cases.

A robust data ecosystem also facilitates interoperability between legacy and modern systems. Intelligent data pipelines can transform and clean data as it moves between platforms, ensuring that information is accurate, timely and actionable. This is especially critical as utilities seek to integrate distributed energy resources, e-mobility data and other emerging technologies.

Develop Strong Data Governance

Data governance is the backbone of data quality, security and compliance. Utilities must establish clear ownership, standards and controls for data across the organization. This includes formalizing data management as a business function, setting policies for accuracy, completeness and consistency, and automating quality controls wherever possible. Effective governance frameworks ensure that data is discoverable, trustworthy and accessible to those who need it while protecting sensitive information and meeting regulatory requirements.

Integrated data governance programs help break down silos, enabling a unified view of assets, customers and operations. They also support incident management, compliance monitoring and proactive issue detection, reducing downtime and risk. As utilities migrate more systems to the cloud and adopt real-time operations, the importance of automated, enterprise-wide governance continues to grow.

Think Continuous Strategy, Not One-Time Project

Data analytics and AI are not finite projects—they are ongoing disciplines that must be embedded in the utility’s strategy and culture. Rather than treating data initiatives as one-off efforts with a defined beginning and end, utilities should adopt a “living strategy” approach. This means continuously funding, evolving, and optimizing data capabilities in alignment with business goals such as customer experience, reliability, resiliency and workforce transformation.

A continuous strategy recognizes that data-driven decision-making is a core competency, not a temporary initiative. It requires cross-functional collaboration, ongoing investment in talent and technology and a commitment to innovation. By anchoring strategy in high-value use cases that challenge the organization’s data ecosystem, utilities can drive meaningful change and sustain momentum.

Build Data Talent and Promote a Data-Driven Culture

Competing for top-tier data scientists can be difficult. Still, utilities can succeed by cultivating “citizen data scientists” from within, including engineers, analysts and other professionals who understand the business and can be trained in data disciplines. Investing in training, change management and cross-industry recruitment helps build a workforce that embraces data-driven thinking and innovation, fostering a culture of innovation. Encouraging collaboration between IT, OT and business units ensures that data initiatives are aligned.

Beyond Best Practices: How AI and Machine Learning Can Help

AI and ML are powerful allies in the digital battle against data chaos. These technologies can automate the identification, correction and conflation of data errors, ensuring ongoing data accuracy without manual intervention. AI-driven pipelines can continuously transform and clean data as it moves between systems, reducing latency and human error.

Rules-based supervised learning models are particularly effective at detecting anomalies and patterns in smart meter and sensor data. For example, AI can distinguish between genuine equipment failures and environmental factors (such as high ambient temperature), reducing false positives and unnecessary field checks. By correlating data from meters, SCADA systems, and external sources, AI can infer network issues, pinpoint outages, and optimize asset performance.

Automating data pipelines with AI not only keeps data current but also frees up staff to focus on higher-value tasks. Intelligent systems can detect unusual consumption trends, anticipate equipment failures, and recommend proactive maintenance, enhancing grid reliability and customer service. As utilities scale up their use of AI, they must also invest in the necessary infrastructure, governance and talent to effectively operationalize these solutions.

Following these recommendations improves numerous business functions, including:

• Outage Management: AI-driven analytics enable faster detection, diagnosis and restoration of outages, reducing downtime and improving reliability.
• Demand Response: Real-time data integration supports dynamic load management and customer engagement, optimizing grid performance.
• Asset Management: Predictive maintenance and anomaly detection extend asset lifespans and reduce operational costs.
• Renewable Integration: Advanced analytics facilitate the seamless integration of distributed energy resources and renewables into the grid.
• Customer Service: Personalized insights and proactive communication enhance customer satisfaction and loyalty.
• AMI 2.0: Next-generation metering infrastructure leverages AI for advanced analytics, fraud detection and operational optimization.
• Agentic AI: Intelligent agents automate routine tasks, optimize workflows and support decision-making in both customer service and operations.

Turning the Tide: The Results of Taming the Data Tsunami

When utilities transform previously unwieldy data into insights results, the business benefits ripple across operations and customer service, including: 

• Enhanced grid reliability and resiliency through predictive maintenance, real-time monitoring and proactive issue resolution.
• Significant cost savings achieved by reducing manual interventions, optimizing resource allocation and preventing equipment failures.
• Improved customer satisfaction with faster response times, personalized services and transparent communication.
• Greater operational efficiency by automating routine tasks and enabling staff to focus on high-value activities.
• Increased agility and innovation, empowering utilities to adapt to regulatory changes, new technologies and evolving customer expectations.
• Strengthened data security and compliance through robust governance, which reduces risk and supports regulatory reporting.