Data quality checks are processes that help ensure the information in a dataset is accurate, complete, and reliable. They involve looking for errors such as missing values, duplicate records, or values that do not make sense. By performing these checks, organisations can trust that their decisions based on the data are sound. These checks can…
A Data Consolidation Guide is a set of instructions or best practices for combining data from multiple sources into a single, organised dataset. This process helps to remove duplicates, standardise formats, and ensure that all relevant information is stored together for easier analysis. Data consolidation is essential for making informed decisions, as it provides a…
Data literacy is the ability to read, understand, create, and communicate data as information. It means knowing how to find, interpret, and use data to answer questions or solve problems. A person with data literacy can spot trends, recognise errors, and make informed decisions using data.
Data analytics is the process of examining raw information to find patterns, draw conclusions, and support decision-making. It involves collecting, cleaning, and studying data to answer questions or solve problems. This helps organisations and individuals make better choices based on evidence rather than guesswork.
Multi-Cloud Data Synchronisation is the process of keeping data consistent and up to date across different cloud platforms. This means that if data changes in one cloud, those changes are reflected in the others automatically or nearly in real time. It helps businesses use services from more than one cloud provider without worrying about data…
Edge data caching strategies refer to methods used to store frequently accessed data closer to users, typically on servers or devices located near the edge of a network. This approach reduces the distance data needs to travel, resulting in faster access times and less strain on central servers. These strategies are important for applications that…
Data cataloguing solutions are tools or systems that help organisations organise, manage and find their data assets. They create an inventory of data sources, including databases, files and reports, and describe each item with useful information, such as where it is stored, what it contains and who can access it. These solutions make it easier…
Data fabric orchestration is the process of managing and coordinating the flow of data across different systems, platforms, and environments. It ensures that data moves smoothly and securely from where it is created to where it is needed, regardless of its location or format. This involves automating tasks such as data integration, transformation, governance, and…
Data Mesh implementation patterns are structured approaches for organising and deploying data mesh principles within an organisation. They guide teams on how to design, build, and manage decentralised data platforms, making sure data is treated as a product and managed by domain-specific teams. These patterns help organisations share data reliably and securely, while ensuring teams…
Data partitioning best practices are guidelines for dividing large datasets into smaller, more manageable parts to improve performance, scalability, and reliability. Partitioning helps systems process data more efficiently by spreading the load across different storage or computing resources. Good practices involve choosing the right partitioning method, such as by range, hash, or list, and making…