CRUD [Create, Read, Update, Delete] (Database Management):
Create: The "Create" operation involves adding new data or records to a database. This operation typically corresponds to the SQL "INSERT" statement in database management systems. In web development, it often involves user interfaces for creating new items, such as adding a new user account, posting a new blog entry, or inserting a new product into an e-commerce catalog.
Read: The "Read" operation is about retrieving or fetching existing data from a database. It corresponds to the SQL "SELECT" statement. Reading data allows users and applications to view, search, and display information stored in a database. In web development, this is often associated with displaying data on web pages.
Update: The "Update" operation involves modifying or updating existing data in a database. This operation corresponds to the SQL "UPDATE" statement. Users can edit and make changes to existing records, such as updating a user's profile information, editing the content of a blog post, or changing product details.
Delete: The "Delete" operation is used to remove data or records from a database. It corresponds to the SQL "DELETE" statement. Deleting data is typically done when records are no longer needed or when users want to remove their own content, like deleting a social media post or removing an item from an online shopping cart.
Data Warehousing Lifecycle:
Data Extraction: Data is collected from various sources, often including operational databases.
Data Transformation: Data is cleaned, integrated, and transformed into a suitable format for analysis.
Data Loading: Transformed data is loaded into a data warehouse or data mart.
Data Usage: Data is accessed and queried by business users for reporting and analysis.
Data Archiving/Purging: Older data may be archived for historical purposes or deleted if no longer needed.
Data Integration Lifecycle:
Data Acquisition: Data is collected from multiple sources, which may have different formats and structures.
Data Transformation: Data is standardized, cleaned, and transformed to ensure consistency.
Data Consolidation: Data is merged into a single repository or data warehouse.
Data Distribution: Integrated data is made available to users and applications.
Data Monitoring and Maintenance: Ongoing monitoring and maintenance of integrated data to ensure accuracy.
Master Data Management (MDM) Lifecycle:
Data Collection: Gathering master data from various sources.
Data Cleansing: Cleaning and standardizing data to ensure consistency.
Data Integration: Integrating master data into a central repository.
Data Distribution: Making master data available to other systems and applications.
Data Maintenance and Governance: Enforcing data quality, security, and governance policies.
Data Retirement/Archiving: Archiving or retiring outdated or unused master data.
Data Quality Lifecycle:
Data Profiling: Analyzing data to understand its quality and characteristics.
Data Cleansing and Enrichment: Cleaning, standardizing, and enriching data to improve quality.
Data Monitoring: Continuously monitoring data quality and identifying issues.
Data Reporting and Alerting: Generating reports and alerts related to data quality.
Data Remediation: Taking corrective actions to address data quality issues.
Data Archiving and Retention Lifecycle:
Data Identification: Identifying data to be archived or retained based on legal, regulatory, or business requirements.
Data Storage: Storing archived data in a secure and accessible manner.
Data Retrieval: Providing mechanisms to retrieve archived data when needed.
Data Destruction: Ensuring secure and compliant data destruction when retention periods expire.
Direct URL : https://above.nasa.gov/implementation_plan/data_cycle.html
A modified version of the DataOne LIfe Cycle oriented around the Arctic-Boreal Vulnerability Experiment relating to climate change
Direct URL : https://www.healthit.gov/playbook/pddq-framework/data-operations/data-lifecycle-management/
Ensures that the organization understands, inventories, maps, and controls its data, as it is created and modified through business processes throughout the data lifecycle, from creation or acquisition to retirement.
. U.S. Department of Transportation (DOT)
Creating Data Management Plans (DMPs): This resource provides guidance on how researchers should handle digital data during and after a research project, ensuring compliance with DOT policies on data dissemination and sharing.
URL: https://ntl.bts.gov/ntl/public-access/creating-data-management-plans
2. National Institutes of Health (NIH)
Writing a Data Management & Sharing Plan: NIH offers detailed instructions on elements to include in a data management and sharing plan, such as data type, related tools, standards, and data preservation methods.
URL: https://sharing.nih.gov/data-management-and-sharing-policy/planning-and-budgeting-for-data-management-and-sharing/writing-a-data-management-and-sharing-plan
3. National Science Foundation (NSF)
Preparing Your Data Management and Sharing Plan: NSF provides an overview of requirements for data management and sharing plans, aligning with their Proposal and Award Policies and Procedures Guide.
URL: https://new.nsf.gov/funding/data-management-plan
4. Federal Data Strategy
Data Management & Governance Resources: The Federal Data Strategy offers a Data Governance Playbook to assist federal agencies in prioritizing data governance and assessing maturity, supporting the management of data as a strategic asset.
URL: https://resources.data.gov/categories/data-management-governance/
5. Data Management Association International (DAMA International)
Certified Data Management Professional (CDMP): DAMA International provides certification for data management professionals, demonstrating knowledge, skills, and experience in the field.
URL: https://www.dama.org/cpages/home
6. EDM Council
Global Advocates for Data & Analytics Management: The EDM Council is a global trade association offering best practices, standards, and education to data and business professionals, emphasizing data management and analytics.
URL: https://edmcouncil.org/
These resources provide comprehensive guidance on data lifecycle and data management plans across various government agencies and professional organizations.
