A data life cycle refers to the various stages that data goes through during its existence. It encompasses the entire lifespan of data, from its creation or acquisition, through its usage and storage, to its eventual disposal or archiving. It be interpreted in terms of [[The Eight Stages]] of data cleasning. # The Eight Stages: one possible sequence It would also be meaningful to explicitly define the tasks and requirements in a sequential order. Like most [[DevOps]] or [[MLOps]] activities, the stages can be listed into eight items: 0. [[No Attention, Yet!]], or [[exploratory data collection]] 1. [[Coaching]] or [[Subjective Interpretation]] 2. [[Lean Operations]] or [[Logical Test Preparation]] 3. [[Software Delivery]] or [[Labeled Test Event]] 4. [[BDD ready]] or [[Labeled Interpretation]] 5. [[Effective Change]] or [[Documented Computation]] 6. [[DevOps Qualified]] or [[Computed Interpretation]] 7. [[DNA Stage]] or [[Publicly Interpreted Data]] I intentionally drew a diagram that shows these eight stages in a reverse sequence. ![[EightStagesOfMLOps.excalidraw.svg|800px]] [[EightStagesOfMLOps.excalidraw|Go to the diagram]] # Other things to consider The data life cycle typically consists of several key task areas: 1. Data Creation/Acquisition: This stage involves the initial creation or acquisition of data. Data can be generated through various sources such as sensors, devices, user input, or acquired from external sources like databases or APIs. 2. Data Storage: Once created or acquired, the data needs to be stored in a suitable location or system. This stage involves deciding where and how the data will be stored securely and efficiently. 3. Data Processing/Analysis: In this stage, the collected data is processed and analyzed to extract meaningful insights and derive value. This can involve transforming raw data into a more usable format, performing calculations, applying algorithms, or conducting statistical analysis. 4. Data Usage/Sharing: After processing and analysis, the resulting information is used for various purposes such as decision-making, reporting, visualization, or sharing with relevant stakeholders within an organization. 5. Data Archiving/Retirement: Over time, certain data may become less relevant or outdated but still needs to be retained for legal or compliance reasons. In this stage, data is archived in a secure and accessible manner while ensuring it remains compliant with relevant regulations. 6. Data Disposal: When data reaches the end of its useful life and is no longer required to be retained, it needs to be securely disposed of to protect privacy and prevent unauthorized access or misuse. Proper disposal methods may involve deleting digital files permanently or physically destroying physical storage media. Throughout these stages, organizations need to consider factors such as data quality assurance, security measures (e.g., encryption), backup and recovery procedures, as well as adherence to regulatory requirements (e.g., [[GDPR]]). There are a total of six possible ways to chose these sequences. See [[@martinCountingArtEnumerative2010|Counting: the art of enumerative combinatorics]]. # Conclusion Managing the entire life cycle of data is crucial for organizations to ensure efficient and secure handling of data, maintain data integrity, and derive maximum value from their data assets. # References ```dataview Table title as Title, authors as Authors where contains(subject, "Data Life Cycle") ```