Elevating data quality and management in the energy industry

Good quality data are fit-for-purpose and can be trusted, especially when used as a basis for critical decision-making. As the energy industry goes through a digital transformation where data are increasingly used in decision-making, data quality is of particular importance. Data quality is of particular importance as it underpins efficient operations, regulatory compliance, cost reduction, sustainability efforts, customer satisfaction, strategic planning, and risk management.

Data governance is establishing the frame conditions for successful data management, including roles and responsibilities on the strategic, tactical and operational level. Data governance and management are enablers for creating business value and innovation by use of data.

Considering the business value of good quality data enables an understanding of why data quality should be in focus. Achieving this initial understanding will help sell in the job that needs to be done to ensure that data is of good quality.

Key use cases include scenarios where data are used to make decisions. An asset may be fully digital, where asset monitoring is carried out remotely using sensor data recorded locally at the asset. This data is used to make key decisions and must be trusted. The quality of the data is therefore paramount. The asset may be a remote windfarm, a subsea installation or an unmanned hydroelectric power station.

Key benefits of data quality in the energy industry include:  

• Risk reduction: Trust in good quality data enables moving away from manual processes that can be unreliable and increase risk. 
• Real-time decision-making: Quality data enable accurate analysis, and energy companies can make informed decisions more quickly. 
• Energy efficiency optimization: High-quality data enable precise analysis and optimization of energy consumption. 
• Trust and effectiveness: Reliable, good quality data build trust among stakeholders. 
• Reduced energy costs: In buildings, prioritizing data quality through intelligent systems leads to reduced energy consumption and costs.

Risks and challenges of poor data quality in the energy industry include: 

1. Complex data sources: Ensuring consistent data quality across a range of data sources with varying formats, structures and quality levels can be challenging
2. Data duplication and inconsistency: Data duplication, outdated records, incomplete information, and inconsistent formats are common issues and using these data can result in costly errors when basing decision-making on these.
3. Legacy systems and silos: Many companies rely on legacy systems that were not designed for modern data requirements. Integrating data from these siloed systems can be challenging.
4. Data governance and compliance: The industry operates under strict regulations and compliance standards. Ensuring data quality while adhering to these rules requires robust governance frameworks.
5. Data security and privacy: Protecting sensitive data is crucial. Ensuring data quality while safeguarding privacy requires strict cyber security and compliance with privacy laws.
6. Asset information: Incorrect asset information directly impacts reliability maintenance. Good quality data is essential for safety, compliance and efficient maintenance programs. Automation relies on trustworthy data, making data quality critical.

Defining what good quality data, measuring the actual quality of data help in understanding what the quality of data is. The risk of using poor quality data can then be considered by domain experts. Proactive and reactive methods to fix data quality issues can be implemented.

Data quality is a measure of the “health condition” of data, a measure of how well the data meets the requirements and expectations for its intended use within acceptable risk, cost and business needs. Data management provides the foundation to ensure good data quality and is the process of following the rules and procedures to ensure data is accurate, complete, consistent and up-to-date. Processes such as data validation and cleansing are part of the data management efforts to maintain high-quality data in the lifecycle. 

The regulatory landscape for data quality in energy industry varies with geographies and industry segments. The ISO 8000 data quality standard provides a generic approach to data quality which is applied to the energy industry. This can be applied using the best practices from Data quality management | Recommended Practice | DNV-RP-0497

Testing data quality starts with defining for a particular use-case what is meant by good data quality. This definition can be expressed as data quality requirements (e.g. that there are no blank records in data), which when achieved assures users that data are fit for purpose. Data quality can then be measure according to these requirements using for example simple checks in a database or using a data quality as a service.

Data maturity shows how well data are managed. Well-managed data are accessible to users, have good data quality and in a data-driven organization contribute to good, trustworthy decisions. Assessing the current maturity of data assets provides a baseline and guides organizations on improving the management of their data by applying best practices. You can test your data maturity with our free data management maturity self-assessment tool on Veracity.

Data assurance is an integral part of digital trust, ensuring good quality data throughout its lifecycle. It ensures that data are trustworthy, accurate and fit for purpose. Data assurance is essential for building trust, making informed decisions and maximizing the value of data.