Utilities are experiencing significant pressure to optimize their operations due to regulations aimed at reducing waste and carbon footprints. To meet these obligations, they must adopt measures that promote greater control throughout their operations, improve reporting practices, and enable the rapid implementation of necessary changes. At the same time, demand for alternative energy sources is growing, forcing providers to invest in new asset classes and innovate new operating models.

To optimize the performance of both the legacy assets and new-age assets (and integrate them with a changing grid), utilities must start treating asset data as an asset in itself and ensure that this data is systematically enriched, managed, and governed throughout their networks and technology stacks.

The Trouble with Asset Data Management for Utilities

The utilities industry has witnessed remarkable advancements in its IT transformation, evolving from isolated and fragmented systems to enterprise-wide solutions prioritizing seamless integration, process orchestration, and user experience. This transformation has been augmented by smart connected devices and the Internet of Things, which have significantly enhanced the efficiency of monitoring and diagnosis. Meanwhile, augmented reality and AI-based tools have improved field services, predictive maintenance, and repairs.

These transformations require fit-for-use asset data that is structured, high-quality, and standardized. While utilities are more connected and data-driven than in the past, data management oversights throughout modernization have created unexpected challenges around asset data that limit the business value these changes can deliver.

Asset data interoperability is becoming more complex because many utilities still need to work on a standard connected data model. These systems' data structures and models tend to differ from industry standards, and the data is often stored across multiple non-connected IT systems.

In other words, although the business processes and enterprise systems have been transformed, the underlying data model still needs transformation.

A lack of standardization can prevent utilities from translating data into actions across various business processes and platforms. This prevents utilities from achieving the benefits of enterprise asset management and Geographical Information Systems.

To address these challenges, utilities must carry out data management initiatives alongside their business and digital transformation programs. They must uplift asset data according to industry standards such as CIM, Utility Network, and data/data quality management principles to achieve the desired business benefits and enhance data portability.

Data Management and Governance: How to Get Started

A significant first step for utilities is to conduct a maturity assessment of the current asset data management practices and a quality assessment of the existing asset data, models, and structures. The output of these assessments will illuminate the path forward.

Data management is an ongoing, iterative process. The cycle can be divided into three major segments:

1. Preparation: Establish a data model, conventions, and policies

Utilities should build a data governance process to establish principles, policies, and data standards, such as conventions for storing data so that it is easily accessible. Structured and unstructured data (documents, drawings, 2D/3D models) must be considered while developing data governance.

After examining current business processes, a data governance team can refine the data model, architecture, flows, and use cases that must be aligned with industry standards. The data model needs to consider the structure, hierarchy, and network model of utility assets and create a  taxonomy/nomenclature that will enable swift identification of assets in the network.

A water utility, for example, will need a data model that includes a provision to define linear assets to manage pipelines. An electricity company will need provisions to define parameters relevant to assets like wooden poles and metal poles, pylons, and transformers; for every asset type — whether a tower, a transformer, or a building — utilities need to decide what attributes they want to capture and what will be required to collect that data. Along with a tower’s electrical attributes (conductivity, resistance, etc.), for example, a company might want to know structural characteristics such as the height and material of the tower.

Building a data model based on business processes and in accordance with industry standards can improve compatibility with market participants and partners while ensuring high data quality. Data architecture must ensure holistic business process-driven integration and separation of duties in terms of system of records, system of engagement, and system of insights.

The data governance team can also work with other teams to develop a framework for measuring and improving data quality through data profiling and cleansing rules across various quality dimensions, such as accuracy, completeness, integrity, and consistency.

2. Operation: Operationalize asset data

The data governance team focuses on data operations (CRUD operation) and workflows in the operation stage. Data stewards ensure a complete, consistent view of asset data across the organization. The data governance team should establish quality checkpoints/gates to provide a suitable baseline quality for asset data, particularly if the data is received from third parties or contractors. To ensure privacy and security, the data governance team must continuously enforce data policies related to data sharing, purging, and archiving.

3. Analytics: Employ data products, findings, and reports to generate insights

The data governance team should regularly measure data quality using the frameworks and metrics outlined by the governance board. This might include undertaking regular field surveys, drawing reviews, and other quality assurance projects to check asset quality and ensure standards are being upheld.

The team should also ensure that regular security checks are completed and that all individuals handling data take proper protective measures (audit trails, encryption, etc.).

Most importantly, the data should create insights that drive organizational decision-making and operational improvements.

No single IT solution or software tool can adequately address the complexities of data quality in the utilities industry. Instead, achieving a reliable single source of truth for asset health requires focusing on process maturity. By enhancing data architecture, implementing robust data standards, prioritizing data quality management, and establishing effective data governance, utilities can unlock the true potential of efficient asset management.

A mature approach to data quality empowers utilities to leverage advanced analytics and data-driven insights, optimize their asset management strategies, and realize significant cost savings. Moreover, it enables utilities to enhance their networks' overall reliability and performance, ultimately delivering improved services to their customers.

About the Authors

Praveen Agrawal 
Consulting Partner, Utilities Consulting 

Praveen is a consulting partner with 25 years of global experience and subject matter expertise in work and asset management. A recognized industry thought leader, he is regularly published in international industry forums and provides frequent input to global analysts on industry trends. Drawing on a background that spans across functions and industries, he architects large and complex projects for energy, utilities, and manufacturing companies around the globe. 

Ganesh Nayak 
Managing Consultant, Utilities Consulting 

Ganesh is a managing consultant with 22 years of professional experience. He is a subject matter expert in enterprise asset and work management, field force management, and geospatial information management, and he leverages this domain expertise to lead consulting engagements with global clients.