Data quality assurance is the process of making sure that data is accurate, complete, and reliable before it is used for decision-making or analysis. It involves checking for errors, inconsistencies, and missing information in data sets. This process helps organisations trust their data and avoid costly mistakes caused by using poor-quality data.
Data privacy compliance means following laws and rules that protect personal information collected, stored, or used by organisations. These rules are set by governments to make sure companies handle data responsibly and do not misuse or expose private details. Compliance often involves getting permission from individuals, securing information, and being transparent about how data is…
Data labelling is the process of adding meaningful tags or labels to raw data so that machines can understand and learn from it. This often involves identifying objects in images, transcribing spoken words, or marking text with categories. Labels help computers recognise patterns and make decisions based on the data provided.
Data cleansing is the process of detecting and correcting errors or inconsistencies in data to improve its quality. It involves removing duplicate entries, fixing formatting issues, and filling in missing information so that the data is accurate and reliable. Clean data helps organisations make better decisions and reduces the risk of mistakes caused by incorrect…