Mastering Data Lifecycle Management in 2025 with Metadata Activation & Governance

Gartner’s Inaugural Magic Quadrant for D&A Governance is Here #

In a post-ChatGPT world where AI is reshaping businesses, data governance has become a cornerstone of success. The inaugural report provides a detailed evaluation of top platforms and the key trends shaping data and AI governance.

Read the Magic Quadrant for D&A Governance

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What are the six stages of data lifecycle management? #

Summarize and analyze this article with 👉 🔮 Google AI Mode or 💬 ChatGPT or 🔍 Perplexity or 🤖 Claude or 🐦 Grok (X) .

Although there are no set rules or patterns for the stages of the lifecycle of data, here is what a typical data data lifecycle might look like.

The six broad stages of data lifecycle management (DLM). Image by Atlan.

1. Creation and capture #

The data gets created in-house or acquired from outside systems via integrations and APIs.

2. Storage and organization #

The data is then stored in a database, a data lake, or a data lakehouse. It’s also structured for discovery, ownership, access control, and classification.

After this, data becomes available for processing, which may include cleansing, transformation, reshaping, remodelling, etc.

3. Usage and enrichment #

Data is processed through pipelines (ETL/ELT), cleaned, transformed, and enriched for downstream use.

This prepared data is then consumed by analytics tools, dashboards, AI/ML models, and business applications (and users) to drive decisions and outcomes.

4. Sharing and access #

Data is shared internally or externally with proper permissions, masking, auditing, and security controls to prevent unauthorized use.

5. Archival and retention #

Inactive or infrequently accessed data is archived according to business, legal, and compliance retention requirements.

Archival can happen due to degrading quality, outdated information, cost optimization, etc. In most cases, after a data asset serves its use case, it is moved to a cheaper, less frequently accessed storage layer, which saves cost and reduces the risk of confusion.

In some cases, if certain regulations apply to an organization, the data assets may need to be archived (for long-term storage for compliance purposes, which means you cannot delete the data asset permanently)

Alternatively, it may need to be destroyed (because the regulation prohibits you from keeping a copy of the data), which is the final stage of the DLM.

6. Deletion #

In some cases, the data needs to be fully, securely, and completely destroyed from all the systems, again, owing to regulatory compliance, cost reduction, or reducing exposure risks.

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These are the broad stages, although more stages can be added aligned with specific functions like data governance, sharing, analysis, review, among other things.

The movement of data from one stage to the next is the primary goal of having these stages, and the best way to do it is by using automation. Let’s look at how data lifecycle management can be automated.

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What role does automation play in data lifecycle management? #

Automation plays a pivotal role in managing the lifecycle of data assets.

But before automation drives lifecycle management, it needs the rules and conditions to do that, which are stored in data lifecycle management policies.

These policies include:

• Data storage tiering for retention, archival, disposal, and destruction of data.
• Data freshness or staleness to check if the data asset is still valid or should be marked deprecated.
• Data usage and popularity metrics for lowering the storage or availability tiers.
• Data quality and integrity metrics for changing the certification of the data asset.
• Data access control changes for moving data to a less or more protected area of availability.

Next, let’s examine how data lifecycle management can be integrated with data governance.

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How can you integrate data lifecycle management with data governance? #

Data lifecycle management goes hand in hand with data governance. Data governance lays the foundation for managing your organization’s data assets effectively.

Using the governance foundations and associated metadata, you can implement data lifecycle management for achieving the following outcomes.

Ownership, custodianship, and stewardship-led data lifecycles #

Data lifecycles might be different for different teams, business units, owners, and custodians within the same organizations. This is not necessarily a problem as long as the lifecycle management process is automated and properly documented.

Tags, categories, and other metadata #

Governance frameworks use techniques of tagging data for protection, certification, etc. These tags can be used to trigger lifecycle movements.

For example, a deprecated certification for a data asset can trigger its archival to a deprecated storage area.

Handling sensitive data, especially PII, PHI, and financial data #

One of the key goals of having a data governance framework is to address the security and protection of sensitive data. This is usually done using a range of techniques that include data classification, access controls, auditing, and observability.

Data lifecycle management can tie into this process and take actions based on monitoring events, classification status changes, among other things.

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What are the benefits of effective data lifecycle management? #

A strong data lifecycle management (DLM) strategy delivers clear value across business, security, and compliance priorities:

• High data quality: Ensures accurate, up-to-date, and trustworthy data for analytics, AI, and business decisions.
• Security and reduced risk: Minimizes risk exposure by controlling access, enforcing retention policies, and reducing data sprawl.
• Availability: Ensures the right people can access the right data at the right time.
• Privacy and compliance: Supports compliance with privacy and data protection laws like GDPR, CCPA, and HIPAA.
• Cost optimization: Moves cold or redundant data to lower-cost storage tiers and deletes what’s no longer needed. Also, reduce compute costs by not processing stale and outdated data.
• Operational efficiency: Maintains a cleaner, more manageable data estate, improving discoverability and governance.

Next, let’s look at some of the key challenges in implementing data lifecycle management for an organization.

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What are the key challenges in implementing data lifecycle management? #

Across all the stages of the data lifecycle, numerous challenges arise, most of which are related to the spreading out of data or its growth, and many others are associated with the lack of visibility of data assets in an organization.

It begs the question - if an organization doesn’t know where, how, and why all of its data is stored, processed, and used, how can it implement a data lifecycle management framework effectively?

Let’s take a closer look at specific issues:

• Data siloes and scale: When data growth is accompanied by rising data siloes, it leads to poor visibility and governance gaps.
• Unavailability of data quality metrics: Data assets can be moved to different stages based on their quality and usability. If this information isn’t available, DLM can’t be implemented properly.
• Lack of accountability and auditing: Governance and compliance-based automation is possible when you can track data access activities, lineage, etc using metadata. Lack of auditable, lineage metadata prevents you from implementing DLM consistently.
• Insufficient infrastructure for policy enforcement: Without the right infrastructure for policy enforcement, i.e., even if they have the metadata but cannot activate that metadata for policy enforcement automation, DLM is ineffective.

These challenges underscore why governance and metadata activation are foundational to DLM success. Let’s see how a unified metadata control plane helps solve these problems.

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How does a metadata control plane enable effective data lifecycle management? #

A metadata control plane is a single point of access for all your organization’s metadata. It is built on a metadata lakehouse pattern, driving data discovery, lineage, governance, quality, and automation use cases.

In other words, it provides the foundation for metadata activation, which is crucial for implementing a data lifecycle management framework.

Atlan is one such platform built on the core principle of having such a metadata control plane, with the following capabilities:

• Data cataloging and discovery to make the data easily searchable and discoverable across your data ecosystem.
• Active data governance to ensure that governance processes shift left to improve the security of your data assets.
• Data quality metrics to ensure all the data assets have quality certifications and other associated metadata.
• Fine-grained data lineage to understand dependencies, relationships, and associations between data assets.

With Atlan, you can search and interact with your organization’s metadata via a natural language interface and leveraging the latest AI technologies.

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Real stories from real customers: Automating governance across the various stages of an asset’s lifecycle #

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Ready to automate data lifecycle management for your data estate? #

Data lifecycle management (DLM) is crucial for any organization that deals with various data systems, lots of data assets, and regulatory compliance across jurisdictions.

A key benefit of implementing a data lifecycle management framework is strengthened data privacy and protection posture, with lower storage and consumption costs.

However, achieving this state is difficult if your organization doesn’t have a solid foundation of metadata that can be leveraged to enforce lifecycle policies automatically.

A metadata control plane fills this void, becoming the one place where all metadata is available and can be activated for data lifecycle management automation.

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FAQs about data lifecycle management #

1. What is data lifecycle management (DLM)? #

Data lifecycle management is an approach for managing data in an organization from its inception to its destruction–throughout its entire journey within and, in some cases, outside an organization.

DLM typically uses policies and automation to move data through various stages of its lifecycle. These stages are usually related to usage patterns, retention and disposal requirements, exposure risk mitigation, and overall cleanliness of the data ecosystem.

If implemented properly, DLM helps reduce storage and consumption costs, while supporting more accurate and timely business decision-making.

2. What are the six core stages of data lifecycle management? #

The stages of data lifecycle management are subject to different organizations’ processes and motivations. Usually, any data asset in an organization goes through the following lifecycle:

1. Data creation and capture
2. Storage and organization
3. Usage and enrichment
4. Sharing and access
5. Archival and retention
6. Deletion and disposal

This lifecycle is also very much dependent on the tools, technologies, cloud platforms, and data platforms you use in your data ecosystem.

3. What are the goals of data lifecycle management? #

The top goals of data lifecycle management are:

• Security: Protect data confidentiality by managing access and preventing breaches.
• Integrity: Keep data accurate, consistent, and trustworthy throughout its lifecycle.
• Availability: Ensure the right users can access data when they need it.
• Compliance: Meet data privacy and retention requirements like GDPR and CCPA.
• Cost optimization: Lower storage costs by archiving or deleting data no longer needed.

4. What are the benefits of data lifecycle management? #

There are several benefits of implementing data lifecycle management for your organization:

• Reduce the cost of data storage and compute.
• Ensure compliance with data privacy laws.
• Reduce the surface or attack area for data breaches and leakages.
• Improve data quality across the organization.

5. What are the risks associated with the lack of data lifecycle management? #

There are three key risks associated with the lack of data lifecycle management:

1. Data leakage and breaches: If there’s no lifecycle management for your organization’s data, all data (especially PII data) is likely to be stored in a single place accessible using the same authorization and authentication methods.
2. Data quality and integrity: One of the key reasons for moving data to its next stage is its deprecation due to degrading quality, relevance, and datedness. If that’s not happening, your business and data teams are at a risk of producing reports and dashboards that are incorrect or, if not outright wrong, are outdated.
3. Governance and compliance: DLM is crucial for complying with various industry or region-specific data protection and privacy laws and regulations.

6. How does DLM tie into AI readiness? #

By enforcing stages like data freshness, lineage, and deletion, DLM ensures that AI models train on reliable, relevant data and reduces biases or stale inputs.

7. What metrics should firms track to assess DLM effectiveness? #

Key metrics include data freshness/staleness, percentage of data with defined lineage, number of obsolete data assets, storage cost per TB, and compliance audit pass rate.

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