In today’s fast-paced digital era, organisations heavily rely on data to make informed decisions and drive operational efficiency. For asset intensive organisations, accurate and reliable data plays a pivotal role in enterprise asset management (EAM) to optimise asset performance, minimise downtime, and maximise return on investment.
However, maintaining high data quality within an EAM system is not a trivial task. In this article, we will delve into the significance of data quality in EAM and explore strategies to ensure its integrity, consistency, and usefulness.
Understanding Data Quality in EAM
Data quality refers to the accuracy, completeness, timeliness, consistency, and relevance of data within an EAM system. High-quality data forms the foundation for effective asset management practices, as it enables organisations to make well-informed decisions, plan preventive maintenance, and allocate resources efficiently. Conversely, poor data quality can lead to incorrect asset evaluations, unreliable predictions, and ineffective maintenance strategies, ultimately impacting the bottom line.
The Impact of Poor Data Quality
A recent Gartner report confirmed that every year, poor data quality costs organisations an average US$12.9 million and highlighted that in addition to the immediate impact on revenue, over the long term, poor quality data increases the complexity of data ecosystems and leads to poor decision making.
Yet many organisations persist in prioritising technology solutions or systems upgrades rather than addressing the fundamental issues of data quality and governance. The idea that the next technology program will ‘fix the data issues’ persists, regardless of evidence to the contrary.
Low data quality carries a high cost, particularly for EAM processes where the most damaging consequences include:
- Inaccurate Asset Information: Incomplete or inconsistent data can result in inaccurate asset identification, leading to mismanagement, delayed maintenance, and unnecessary costs. Without reliable information, organisations may struggle to track asset lifecycles, monitor warranties, or plan for equipment upgrades.
- Reduced Maintenance Efficiency: Data inconsistencies can hinder the maintenance process, making it difficult to prioritise tasks, identify critical assets, and schedule maintenance activities. This can lead to inefficient resource allocation, increased downtime, and a higher risk of equipment failures.
- Ineffective Decision-Making: When decision-makers lack confidence in the accuracy and completeness of their data, they may make suboptimal choices regarding asset investments, budget allocations, and performance improvement initiatives. Poor data quality undermines the organisation’s ability to optimise asset utilisation and mitigate risks effectively.
Strategies for Improving Data Quality
For organisations to succeed, data must be treated as a mission critical asset. It is the single biggest success factor in a digital transformation journey, and most organisations are ill prepared due to many islands of disconnected data that is of unknown and/or poor quality.
At COSOL, we are experts in the enterprise asset information ecosystem, uniting systems, data, people and processes to unlock asset potential. The strategies we’ve found to be most effective for improving data quality include:
- Data Governance: Establishing a robust data governance framework is crucial for ensuring data quality within the EAM system. This involves defining data ownership, creating data standards, implementing data validation processes, and establishing data stewardship responsibilities. Clear guidelines and accountability structures promote data accuracy and consistency.
- Data Integration and Validation: Integrating data from various sources, such as maintenance records, IoT sensors, and financial systems, enhances data completeness and reliability. Implementing automated validation checks, including duplicate detection, format validation, and range validation, helps identify and rectify data errors or inconsistencies.
- Regular Data Cleansing: Conducting periodic data cleansing exercises eliminates redundant, outdated, and irrelevant data. This process involves identifying duplicate records, correcting data errors, standardising data formats, and validating data against predefined rules. By keeping the data clean and up-to-date, organisations can make more accurate asset-related decisions.
- Training and User Adoption: Educating employees on the importance of data quality and providing training on data entry protocols contributes significantly to maintaining high-quality data. Organisations should encourage user adoption of data entry best practices, including thorough documentation, accurate measurement recording, and timely updates.
- Continuous Monitoring and Measurement: Implementing data quality metrics and regularly monitoring them enables organisations to track data quality performance. Key performance indicators (KPIs) such as data completeness, accuracy, and timeliness can help identify areas for improvement, prioritize data quality initiatives, and ensure ongoing compliance with data standards.
Data quality is a critical aspect of successful enterprise asset management. Organisations must prioritise efforts to enhance data quality within their EAM systems to optimise asset performance, reduce downtime, and achieve operational excellence. By implementing robust data governance practices, integrating data sources, regularly cleansing data, providing user training, and continuously monitoring data quality metrics.
At COSOL, we’re experts at unlocking enterprise data potential and we thrive on the complex challenges it presents. Contact us to learn more about the proprietary tools we can offer to assist in archiving, assessing quality, and cleansing your data to bring it in line with technology and business process.
Find out how COSOL's enterprise data management expertise and proprietary tools can help improve your data quality.
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