For professionals in Life Sciences, maintaining data integrity at every stage of the Data LifeCycle is vital. This guide delves into the five fundamental stages of Data Lifecycle Management (DLM) and the critical actions necessary at each phase. DLM is a policy-driven approach to managing data from its creation to its eventual disposal.
The Five Phases of Data Lifecycle Management
Data Lifecycle Management (DLM) structures the journey of data from its inception to its final deletion. Each phase, while varying slightly depending on the context, includes essential processes that can be summarized as follows:
1. Data Creation
The data lifecycle begins with the creation or capture of data, which can be in various forms such as PDFs, images, Word documents, or database entries. Data generation occurs through:
- Data Acquisition: Acquiring data that was created externally.
- Data Entry: Manual input of new data by internal personnel.
- Data Capture: Using devices to automatically collect data during organizational processes.
Technological solutions for DLM assist in automating the management and migration of data across different storage tiers based on predefined policies.
2. Storage
After data creation, it must be securely stored and maintained with strong security measures. This includes a comprehensive backup and recovery system to protect data throughout its lifecycle. Proper storage ensures data remains intact, secure, and accessible, with additional processing such as encryption and cleansing being part of this phase.
3. Usage
During this stage, data is accessed, processed, and modified to support organizational functions. It’s crucial to maintain an audit trail for critical data to track any changes made. Data sharing and collaboration, both within and outside the organization, are facilitated during this phase, enhancing functionalities like analytics and visualization.
4. Archival
Archival involves storing data in a dormant state, away from active environments. This ensures the data remains accessible for future needs without the regular maintenance or active usage, meeting compliance and analysis requirements.
5. Destruction
As data accumulates and storage costs increase, it becomes necessary to securely purge unnecessary data, following all regulatory and compliance requirements, to maintain data hygiene and cost efficiency.
Effective Data Lifecycle Management Practices
A well-documented DLM process is essential for robust Data Governance, ensuring data security, privacy, and uninterrupted access. Key focuses include maintaining data integrity—ensuring the accuracy and reliability of data—and ensuring data availability for authorized use without disrupting existing workflows.
At Dataworks, our team of expert CSV & Software Engineers provides top-tier Data Integrity services, including assessments, remediation, and validation. Our strategies incorporate a mix of procedures, best practices, and technological applications to optimize DLM, making data management more efficient and compliant.
| Stage | Description |
|---|---|
| Data Creation | Acquisition, entry, or capture of data in various forms. |
| Storage | Protecting and securing data with backup and recovery processes. |
| Usage | Viewing, processing, modifying, and saving data with audit trails. |
| Archival | Copying data to a storage environment for future use if needed. |
| Destruction | Removing all copies of unnecessary data after the retention period. |