Data Lifecycle Management and Data Retention Policies

DLM aims to manage data throughout its lifecycle to ensure efficient storage, retrieval, and disposal, while minimizing costs and risks.

Acquisition is the first phase of the data lifecycle, where data is initially created or captured from various sources.

Data retention policies are implemented to comply with legal and regulatory requirements, ensuring that data is retained for a specified period to meet legal obligations.

Data classification involves categorizing data into different classes based on its sensitivity, criticality, and regulatory requirements.

Data classification is the process of identifying, classifying, and labeling data according to its sensitivity and criticality, enabling organizations to manage and protect data effectively.

Processing is the phase where raw data is transformed into a more usable and meaningful format through various operations such as cleaning, filtering, and aggregation.

Data retention policies are primarily implemented to comply with legal and regulatory requirements, ensuring that data is retained for a specified period to meet legal obligations.

Storage is the phase where data is stored in a secure and accessible manner, enabling authorized users to retrieve it when needed.

Data lineage is the process of tracking the movement and transformation of data throughout its lifecycle, providing a comprehensive understanding of data provenance and dependencies.

Disposition is the phase where data that is no longer needed is systematically disposed of or archived, ensuring compliance with data retention policies and regulations.

Data governance aims to establish policies, procedures, and standards for managing data, ensuring its integrity, security, and compliance with regulatory requirements.

Quality Assurance is the phase where data quality and accuracy are monitored and maintained, ensuring that data meets the required standards and is fit for its intended purpose.

Data deduplication is the process of identifying and removing duplicate or redundant data from a dataset, optimizing storage space and improving data quality.

Analytics is the phase where data is analyzed to extract meaningful insights, patterns, and trends, enabling organizations to make informed decisions.

Data wrangling is the process of converting unstructured data into a structured format, making it easier to analyze and process, and enabling the extraction of valuable insights.