The data lifecycle describes the stages involved in a data project – from generating the data records to interpreting the results. While there are slight variations between definitions, lifecycle stages might include: data generation, collection, processing, storage, management, analysis, visualization, and interpretation.
Managing data throughout its lifecycle helps ensure its accuracy, timeliness, and availability. Understanding the way data is processed, stored, and accessed – by people and by information systems – is also important for security and disaster recovery purposes. Managing data governance, classification, and retention policies can all be seen as part of a broader data lifecycle management effort.