Data Lifecycle: Where Do Data Bytes Go When They Die?

Did you know that only 3% of data held by businesses is high quality, and that bad data accounts for 12% of all revenue?

Or that when it comes to working with data, 70% of company executives' time is spent actually finding the data they need?

Data may be the most valuable asset at your disposal, but structuring it in a way that maximizes utility is essential. 

Doing so means understanding and managing the data lifecycle. This makes it easier for businesses to understand what policies, processes, and tools they need at each stage.

If you’re looking to get to grips with the data lifecycle, then this blog is a great place to start. In it, you’ll learn about all the different stages involved, as well as the technologies that exist to optimize data governance throughout the process.

Let’s dive in!
 

Try thinking about the data management lifecycle as the lifespan of a living thing:

• Data is born → or created,
• It’s put to work → or used,
• It travels → or is shared between businesses,
• It’s eventually retired or is kept for the future → or archived,
• Finally, it dies → by being deleted or destroyed,

And like any person, it requires different things during each stage of life.

Let’s move from analogy to practice and look at the six main stages of the data life cycle:
 

"The goal is to turn data into information, and information into insight.”

- Carly Fiorina,  Former Chief Executive Officer, Hewlett-Packard

Data and information are terms that are often used interchangeably, but they’re actually two distinct things:

Data is a raw, unprocessed fact or set of facts. 

It can be numbers, figures, names, or other details but it lacks context or meaning and doesn’t provide much value by itself.

When this data is processed, organized, or interpreted to have meaning, it becomes information.

Information is insight. It’s actionable, useful, and often tied to a specific purpose or decision-making process.
 

The Data Lifecycle: Focused on Raw Data

As discussed earlier, the data lifecycle provides structure to the path that raw data takes from its creation to its eventual destruction.

It exists to help businesses ensure that data is handled accurately, stored securely, and given the care it deserves during each of the six stages of its life.
 

The Information Lifecycle: Focused on Adding Value

On the other hand, the information management lifecycle deals with data that has been processed and turned into useful information.

It focuses more on how meaningful information is used, shared, and managed.

The information lifecycle starts once data has been turned into insights, through analysis, process, or another type of contextualization.
 

The Duality: Why Both Lifecycles Matter

Data and information might seem like digital twins so similar that there’s no need to get to know them separately.

However, they perform different but complementary functions in the digital ecosystem.

→ The data lifecycle ensures that raw data is properly managed and stored, and in a way that respects legal obligations.

→ The information lifecycle maximizes the value of this data by turning it into useful insights.

Put another way, data is the fuel, and the information lifecycle is the engine that turns that energy source into power.

Both are crucial, but they operate at different levels and understanding this distinction will prove crucial when managing digital assets effectively.
 

Lifecycle Stage: Creation

Purpose: ETL stands for Extract, Transform, and Load. These tools gather data from various sources, transform it into a consistent format, and integrate it into one place. This process ensures that data is ready for analysis and use across different systems.

Examples:

• Talend: An open-source data integration tool that helps with ETL processes by enabling data extraction, transformation, and loading from a variety of sources.
• Informatica: A widely used data integration platform that provides robust tools for ETL, data quality, and data governance, supporting complex data integration needs.
   

Lifecycle Stage: Storage

Purpose: Cloud storage solutions provide scalable and secure online storage for data. They allow organizations to store large volumes of data, ensure its availability, and provide easy access from anywhere, reducing the need for physical storage infrastructure.

Examples:

• Google Cloud: Offers a range of cloud storage solutions, including scalable object storage and integrated data management services, suitable for diverse data needs.
• AWS: Amazon Web Services provides flexible cloud storage options such as Amazon S3 for object storage and Amazon EBS for block storage, known for its reliability and scalability.
   

Lifecycle Stage: Usage

Purpose: Master Data Management tools ensure that critical data, such as customer or product information, is consistent and accurate across all systems. They help maintain a single, reliable source of truth for business data.

Examples:

• Informatica MDM: A leading MDM solution that provides comprehensive data management capabilities, ensuring data consistency and quality across the enterprise.
• SAP Master Data Governance: A robust MDM solution that integrates with SAP’s ecosystem to manage and maintain accurate master data, offering features for data quality, governance, and compliance.
   

Lifecycle Stage: Usage

Purpose: These tools are designed to monitor, assess, and improve the accuracy and reliability of data. They help identify and correct data errors or inconsistencies, ensuring that decisions are based on high-quality information.

Examples:

• Talend Data Quality: A tool that integrates with Talend’s data integration suite to profile, cleanse, and monitor data, ensuring it meets quality standards.
• IBM InfoSphere Information Server: A comprehensive suite of data quality tools that provides capabilities for data profiling, cleansing, and monitoring, helping organizations ensure the accuracy and integrity of their data.
   

Lifecycle Stage: Usage

Purpose: Data cataloging tools organize and document data assets, making it easier for users to discover and understand the data available to them. They provide metadata, data lineage, and usage information to facilitate data management and governance.

Examples:

• Alation: A data cataloging platform that helps organizations manage data assets by providing a searchable inventory and detailed metadata, promoting better data usage and governance.
• Collibra: A comprehensive data governance and cataloging solution that offers tools for data discovery, classification, and stewardship, enabling organizations to manage data assets effectively and ensure compliance.
   

Lifecycle Stage: Sharing

Purpose: These tools safeguard sensitive data by implementing access controls, encryption, and monitoring. They help ensure that data is shared securely and in compliance with regulations, protecting it from unauthorized access or breaches.

Examples:

• Varonis: A data security platform that provides advanced threat detection, data classification, and monitoring to protect sensitive data and ensure compliance.
• BigID: A data privacy and protection solution that helps organizations discover, classify, and manage sensitive data, offering features for data mapping, access controls, and compliance with privacy regulations like GDPR and CCPA.
   

Lifecycle Stage: Archiving

Purpose: Backup and archiving solutions create copies of data for disaster recovery and long-term storage. They ensure that important data is preserved and can be restored in case of data loss or corruption.

Examples:

• Veeam: A backup and disaster recovery solution known for its reliable data protection and recovery capabilities, suitable for both virtual and physical environments.
• Commvault: A comprehensive data management solution that provides robust backup, archiving, and recovery capabilities, offering features like automated backups, cloud integration, and long-term data retention.
   

Lifecycle Stage: Usage

Purpose: Data analytics and visualization tools help analyze complex data sets and present insights in a user-friendly format. They enable organizations to generate reports, dashboards, and visualizations to support data-driven decision-making.

Examples:

• TWIPLA: A privacy-perfect website intelligence solution, with intuitive data visualizations, custom dashboards, and powered by advanced cookieless tracking technology.
• Power BI: Microsoft’s data visualization tool that integrates with various data sources to create detailed reports and dashboards, offering extensive analytical capabilities.
   

Lifecycle Stage: Usage

Purpose: Metadata management tools track and manage information about data assets, such as their origin, structure, and relationships. They help users understand data context and lineage, facilitating better data governance and utilization.

Examples:

• Informatica Metadata Manager: A tool that provides comprehensive metadata management, allowing organizations to track data lineage, understand data relationships, and support data governance initiatives.
• Microsoft Purview: A data governance and compliance solution that offers metadata management capabilities, helping organizations discover, catalog, and manage their data assets while ensuring data governance and regulatory compliance.
   

Lifecycle Stage: Usage

Purpose: These tools automate and streamline data workflows and processes. They ensure that data movement, transformation, and integration tasks are executed efficiently and consistently, reducing manual effort and errors.

Examples:

• Apache Airflow: An open-source platform that manages and schedules complex data workflows, allowing users to automate data pipelines and improve operational efficiency.
• Azure Data Factory: A cloud-based data integration service from Microsoft that orchestrates and automates data workflows across various sources and destinations, providing tools for data movement, transformation, and monitoring.
   

Lifecycle Stage: Sharing

Purpose: Data migration tools facilitate the transfer of data between different systems or environments. They help organizations move data seamlessly during system upgrades, cloud transitions, or mergers and acquisitions, ensuring minimal disruption.

Examples:

• AWS Data Migration Service: A tool that simplifies the process of migrating databases to AWS, offering support for various database engines and minimizing downtime.
• Snowflake Data Transfer Service: A solution that supports the transfer of data to and from Snowflake's cloud data platform, providing tools for data migration, integration, and synchronization across different data environments.