For any asset-intensive organisation, understanding data quality is a key component of the digital driven, informed decision-making process
Good data quality is a key enabler for making informed decisions and is, therefore, integral to business success. If an organisation is to make good asset management decisions, it requires knowledge about its assets and the asset management activities it undertakes.
For any asset-intensive organisation, understanding data quality is a key component of the digital driven, informed decision-making process. Indeed, it is accepted as one of the most valuable of assets that enhances business decisions and confidence.
Data quality and its assessment defines the degree to which data can be a trusted source for all its required uses. Data quality is also a key contributor to understanding and managing the corporate risk process and, as such, is an essential and fundamental component to any successful digital-driven organisation. Organisations should use data quality measures such as timeliness, reliability, completeness and accuracy (TRAC) to determine whether information is of a suitable quality for use.
Binnies’ proven methodology utilises criteria from the Institute of Asset management’s subject specific guidance document “Asset information, strategy, standards and data management”. This guidance is complementary to ISO 55000 and notes that understanding data quality has four assessment criteria, TRAC, to which we have added integrity and existence.
Data are assessed according to the required, potential and actual frequency of capture within an agreed time tolerance, and, if not within tolerance, then how far out of that tolerance is the data capture time frame.
Each data capture method is assigned a level of reliability based on the assumption that automated data gathering by sensors that are within calibration is more likely to be more reliable than that of subjective processes.
The likelihood that data within a data storage area are accurate. The data calibration rules are demonstrable and used consistently across a data set.
Expressed as a percentage, completeness is the total number of available data points against the total number of data points required to achieve a complete data set.
- Accuracy of data understood leading to better asset identifications thereby removing the need for physical inspection
- Ensures the correct application of operating and maintenance activities (tasks, operating parameters, statutory and regulatory requirements, RAMS, etc.) through better data awareness
- Less manual manipulation and subjective interpretation of data during data cleansing
- Reduction in data engineering labour by automation of extraction, transformation and loading activities
- Improved ability to meet reporting and analysis requirements through data confidence ratings