Data annotation standards are agreed rules and guidelines for labelling data in a consistent and accurate way. These standards help ensure that data used for machine learning or analysis is reliable and meaningful. By following set standards, different people or teams can annotate data in the same way, making it easier to share, compare, and…
Category: Data Governance
Data Labeling Strategy
A data labelling strategy outlines how to assign meaningful tags or categories to data, so machines can learn from it. It involves planning what information needs to be labelled, who will do the labelling, and how to check for accuracy. A good strategy helps ensure the data is consistent, reliable, and suitable for training machine…
Model Governance Framework
A Model Governance Framework is a set of processes and guidelines for managing the development, deployment, and ongoing monitoring of machine learning or statistical models. It helps organisations ensure their models are accurate, reliable, and used responsibly. This framework typically covers areas such as model design, validation, documentation, approval, and regular review.
Data Audit Framework
A Data Audit Framework is a structured set of guidelines and processes used to review and assess an organisation’s data assets. It helps identify what data exists, where it is stored, how it is used, and whether it meets quality and compliance standards. The framework is designed to ensure that data is accurate, secure, and…
Data Reconciliation
Data reconciliation is the process of comparing and adjusting data from different sources to ensure consistency and accuracy. It helps identify and correct any differences or mistakes that may occur when data is collected, recorded, or transferred. By reconciling data, organisations can trust that their records are reliable and up to date.
Data Deduplication
Data deduplication is a process that identifies and removes duplicate copies of data in storage systems. By keeping just one copy of repeated information, it helps save space and makes data management more efficient. This technique is often used in backup and archiving to reduce the amount of storage required and improve performance.
Data Enrichment
Data enrichment is the process of improving or enhancing raw data by adding relevant information from external sources. This makes the original data more valuable and useful for analysis or decision-making. Enriched data can help organisations gain deeper insights and make more informed choices.
Data Cleansing Strategy
A data cleansing strategy is a planned approach for identifying and correcting errors, inconsistencies, or inaccuracies in data. It involves setting clear rules and processes for removing duplicate records, filling missing values, and standardising information. The goal is to ensure that data is accurate, complete, and reliable for analysis or decision-making.
Data Validation Framework
A data validation framework is a set of tools, rules, or processes that checks data for accuracy, completeness, and format before it is used or stored. It helps make sure that the data being entered or moved between systems meets specific requirements set by the organisation or application. By catching errors early, a data validation…
Data Quality Monitoring
Data quality monitoring is the ongoing process of checking and ensuring that data used within a system is accurate, complete, consistent, and up to date. It involves regularly reviewing data for errors, missing values, duplicates, or inconsistencies. By monitoring data quality, organisations can trust the information they use for decision-making and operations.