At Jeskell, we understand that data is one of your organization’s most valuable assets. That’s why our data lifecycle management solutions encompass the entire spectrum of data management processes, from acquisition and storage to analysis and archival. Leveraging our deep technical capabilities and industry expertise, we design and implement tailored strategies to optimize data usage, ensure compliance, and minimize risks. With Jeskell Systems managing your data lifecycle, you can unlock hidden insights, drive informed decision-making, and extract maximum value from your data assets.

Understanding the Data Lifecycle

Data lifecycle management (DLM) is an approach to managing data throughout its lifecycle, from data entry to data destruction. Data is segmented into phases based on various criteria, with each phase serving different tasks or meeting specific requirements. A robust DLM process provides structure and organization to an organization’s data, enabling key goals such as data security and data availability. As data becomes increasingly integral to business operations, DLM policies and processes become essential for preparing against data breaches, data loss, or system failures.

Key Phases of the Data Lifecycle

  1. Data Creation: The lifecycle begins with data collection from various sources such as web applications, IoT devices, forms, and surveys. It’s crucial to evaluate the quality and relevance of collected data to ensure its value to the business.
  2. Data Storage: Structured and unstructured data require different storage solutions. Data undergoes processing, encryption, and transformation to safeguard against security vulnerabilities and comply with regulations like GDPR. Data redundancy ensures protection against data deletion or corruption.
  3. Data Sharing and Usage: Organizations define data access and usage permissions during this phase. Data is leveraged for analyses ranging from exploratory data analysis to advanced machine learning techniques, aiding in decision-making and stakeholder communication.
  4. Data Archival: Data that is no longer actively used but may be needed for litigation or investigation purposes undergoes archival. Clear policies define when, where, and how long data should be archived, ensuring redundancy and accessibility.
  5. Data Deletion: In the final stage, data is securely purged from records. Businesses delete data that no longer serves a meaningful purpose, creating space for active data and ensuring compliance with data retention regulations.

Benefits of Data Lifecycle Management

Implementing a robust Data Lifecycle Management (DLM) strategy offers numerous benefits to organizations. By maintaining data quality throughout its lifecycle, DLM enables process improvement and increases efficiency, ultimately driving strategic initiatives forward. Valuing data at each stage allows for effective cost control, as organizations can leverage solutions like data backup, replication, and archiving to optimize resource allocation. Consistently tagged metadata improves data accessibility, enhancing the agility and efficiency of company processes. Moreover, a sound DLM strategy ensures compliance with industry regulations, mitigating risks associated with non-compliance and bolstering overall governance practices.