Data schema standardisation is the process of creating consistent rules and formats for how data is organised, stored, and named across different systems or teams. This helps everyone understand what data means and how to use it, reducing confusion and errors. Standardisation ensures that data from different sources can be combined and compared more easily.
Category: Data Governance
Data Pipeline Monitoring
Data pipeline monitoring is the process of tracking and observing the flow of data through automated systems that move, transform, and store information. It helps teams ensure that data is processed correctly, on time, and without errors. By monitoring these pipelines, organisations can quickly detect issues, prevent data loss, and maintain the reliability of their…
Automated Compliance Monitoring
Automated compliance monitoring uses software tools to check if an organisation is following rules, laws, and internal policies. Instead of manual reviews, it relies on technology to scan records, activities, and systems for any signs of non-compliance. This approach helps organisations spot problems quickly and ensures they meet regulatory standards without needing constant human oversight.
Private Data Querying
Private data querying is a way to search or analyse sensitive data without exposing the actual information to others. It uses specialised techniques to keep the content of the data hidden, even from the person or system performing the query. This helps maintain privacy and security while still allowing useful insights to be gained from…
Schema Evolution Strategies
Schema evolution strategies are planned methods for handling changes to the structure of data in databases or data formats over time. These strategies help ensure that as requirements change and new features are added, existing data remains accessible and usable. Good schema evolution strategies allow systems to adapt without losing or corrupting data, making future…
Data Quality Monitoring
Data quality monitoring is the process of regularly checking and assessing data to ensure it is accurate, complete, consistent, and reliable. This involves setting up rules or standards that data should meet and using tools to automatically detect issues or errors. By monitoring data quality, organisations can fix problems early and maintain trust in their…
ETL Pipeline Design
ETL pipeline design is the process of planning and building a system that moves data from various sources to a destination, such as a data warehouse. ETL stands for Extract, Transform, Load, which are the three main steps in the process. The design involves deciding how data will be collected, cleaned, changed into the right…
Forensic Data Collection
Forensic data collection is the process of gathering digital information in a way that preserves its integrity for use as evidence in investigations. This involves carefully copying data from computers, phones, or other devices without altering the original material. The aim is to ensure the data can be trusted and verified if presented in court…
Privacy-Preserving Data Sharing
Privacy-preserving data sharing is a way of allowing people or organisations to share information without exposing sensitive or personal details. Techniques such as data anonymisation, encryption, and differential privacy help ensure that shared data cannot be traced back to individuals or reveal confidential information. This approach helps balance the need for collaboration and data analysis…
Differential Privacy Guarantees
Differential privacy guarantees are assurances that a data analysis method protects individual privacy by making it difficult to determine whether any one person’s information is included in a dataset. These guarantees are based on mathematical definitions that limit how much the results of an analysis can change if a single individual’s data is added or…