Understanding the Stages of Data Lifecycle Management
Content

Our Newsletter

Get Our Resources Delivered Straight To Your Inbox

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
We respect your privacy. Learn more here.

TL:DR

This article covers the foundational aspects of data lifecycle management (DLM). As a valuable part of your business, data must be handled appropriately at all stages to extract its maximum value and avoid legal and financial penalties. By integrating data governance with DLM, you can develop a comprehensive strategy for handling data efficiently and effectively, from creation to destruction. 

Introduction

Data Lifecycle Management (DLM) is the process of managing data from the time you create or acquire it until you archive or delete it. DLM includes all data lifecycle stages: creation, storage, usage, sharing, archiving and destruction. Each stage requires specific strategies and technologies to manage your data effectively to be useful, secure and compliant with relevant regulations and policies. The three main goals of data lifecycle management are confidentiality, integrity and availability. 

Data governance is your overarching policy for managing and controlling your data assets. It establishes the policies, standards and practices for promoting data quality and security while facilitating effective data sharing to drive decision-making. The relationship between DLM and data governance is symbiotic. Data governance policies guide effective data lifecycle management practices and both have a role in managing data to align with your company’s objectives, regulatory requirements and ethical considerations. Together, they create a structured environment for managing data throughout its lifecycle, improving your ability to leverage data as a strategic asset.

Key Takeaways

  1. Companies must manage data across all lifecycle stages to maximise its usefulness, adhere to security and compliance regulations and build trust with customers and partners. 
  2. Data lifecycle management and data governance are intertwined and following best practices in each will help you leverage data as a strategic asset while adhering to ethical and regulatory standards.
  3. Future advancements in DLM and data governance will be driven by AI and ML technologies, requiring businesses to adopt flexible and comprehensive strategies to manage data privacy, security and compliance effectively.

The Significance of Data Lifecycle Management

As organisations generate and collect data at an unprecedented rate, they need to manage that data efficiently for maximum value. Data lifecycle management gives you a structured framework for handling data throughout its lifecycle.

With a DLM system in place, you can optimise your data storage costs, improve data accessibility and ensure your data handling processes are compliant with increasingly stringent data protection regulations. It allows you to systematically review and delete obsolete or redundant data, which reduces your storage requirements and costs. DLM lets you accurately classify, store and protect your data against unauthorised access or breaches.

DLM is critical for managing the risks and leveraging the opportunities presented by the vast amounts of data generated daily. It provides a structure for handling data responsibly and efficiently to support your business objectives and regulatory compliance.

Brief Overview of Data Governance 

Data governance includes the practices, processes and frameworks for responsible data management within your organisation. It supports Data Lifecycle Management by establishing the policies, standards and procedures that guide how you collect, store, use and dispose of data. 

Data governance provides the strategic framework and guidelines that oversee DLM, so you know your data activities support your business objectives and comply with legal and regulatory requirements. It defines data quality standards, privacy regulations, and access controls for managing data securely and efficiently throughout its lifecycle. With efficient data governance policies, you can maximise the value of your data assets, minimise risks related to data breaches or non-compliance and create a corporate culture of data literacy and responsibility. 

Stages of Data Lifecycle Management

At all of the stages of data processing, there are specific requirements for handling that data. 

Creation and Acquisition 

All of your data is either created or acquired. The creation and acquisition stage is where data enters your ecosystem. Data is generated internally or collected from external sources during this stage. Internal data creation can occur through transactions, employee data entry, automated system logs, or sensor data from IoT devices. Internally created data can capture valuable insights into operations, customer behaviour and market trends. Externally acquired data involves sourcing data from third parties, public datasets, or purchases. It can give you additional perspectives or improve your existing datasets.

The quality, relevance and integrity of the data you collect or create sets the tone for its subsequent use and value to your business. You need to validate, clean and verify your data to meet set standards. To effectively manage data at this stage, you must establish clear guidelines and processes for data collection, consent and compliance with relevant data protection regulations. This is where you lay a solid foundation for data integrity throughout the data lifecycle.

Storage and Maintenance

Once you have data, you need to securely store it and guarantee its accuracy, availability, and relevance. You have to store your data in a way that balances accessibility with security. You can do this through infrastructure such as databases, data warehouses, or cloud storage solutions. You need to categorise data based on its sensitivity, usage frequency and regulatory requirements, which informs the choice between hot — immediately accessible — and cold — less accessible, long-term — storage solutions.

Maintaining data requires you to perform regular data audits, updates and cleaning to protect its quality. Data backup and recovery plans serve to protect against data loss due to hardware failures, cyber-attacks, or other disasters. Security measures, such as encryption and access controls, protect your data from unauthorised access and breaches. Proper storage and maintenance practices keep your data reliable and secure so leaders can use it to guide decision-making.

Use and Sharing

Your data is most valuable when it’s being used and shared. At this point in the data lifecycle, data is a dynamic asset, driving decision-making, operations and collaboration within and outside your company. During this phase, teams analyse, process, and apply data to solve problems, make informed decisions and innovate products and services. 

Sharing data is an inherent security risk. If you plan to share data, whether internally among departments or externally with partners, clients, or regulatory bodies, you need to follow strict governance protocols to safeguard privacy, guarantee security and comply with legal frameworks. You need strong mechanisms for data access control, secure data transfer protocols and adherence to standards and regulations such as GDPR or HIPAA. During this phase, transparent policies define who can access data, under what conditions and for what purposes. These policies let you leverage data ethically and responsibly so you can maximise its value while minimising its risks.

Archiving and Preservation

You remain legally obligated to protect data even when you’re no longer actively using it. Archiving and preserving data focuses on the long-term storage of data you’re not actively using but that still holds value for future reference, regulatory compliance, or historical significance. During this stage, you can keep your data secure and accessible for extended periods, often over years or decades. Data archiving involves transferring data to a dedicated storage system designed for low-frequency access but high durability, such as magnetic tape, archival-quality disks, or cloud-based archival services.

Preservation maintains the integrity and usability of archived data over time. It includes strategies to protect data from technological obsolescence so your data formats remain readable and the hardware and software you need to access the data are available. These practices safeguard your digital assets so valuable data remains accessible and usable for future needs.

Disposal and Deletion

Disposal and deletion mark the end of data's useful life and is the final stage in the data lifecycle. At this point, you need to securely remove data you no longer need, either because it has outlived its purpose, is redundant, or must be deleted in compliance with data retention regulations. Proper disposal and deletion practices also help manage storage costs and reduce risks associated with data breaches.

Data should be irrecoverably destroyed or deleted at the end of its life. You can do this by physically destroying hardware for sensitive data, using software tools for digital shredding, or using secure deletion protocols that overwrite data. You need to thoroughly document these processes to mitigate risks of unauthorised access or recovery. 

The Implications of Data Governance for DLM

Data governance establishes the rules and standards for data quality, security and privacy at each stage of the DLM process. By following data governance best practices, you can guarantee your data is accurate, reliable and secure.

When you define roles and responsibilities for data management, data governance fosters a culture of accountability and stewardship within your company. Clarifying who is responsible for managing data at each stage of the data lifecycle facilitates effective data handling.

Data governance also underpins compliance and risk management efforts. It outlines policies for handling data using methods that comply with legal and regulatory standards so you can avoid the hefty fines and disastrous consequences of noncompliance.

Finally, data governance protects data assets, guaranteeing they’re identified, managed and used effectively. The ultimate goal of effective data governance is leveraging your data for a competitive advantage, driving innovation and growth.

Integrating Data Governance with DLM

Effective integration begins with establishing a governance framework that outlines roles, responsibilities and processes for managing data. This framework serves as the backbone for DLM activities so that data-handling practices line up with your organisational objectives and applicable regulatory obligations.

You need to set up cross-functional governance bodies, such as a data governance council, to oversee the implementation of governance policies across different departments and stages of the data lifecycle. Security and privacy governance policies protect sensitive data against unauthorised access and breaches, while compliance measures guarantee your data-handling practices adhere to laws and regulations.

Best Practices for Data Lifecycle Management

Following best practices for DLM helps you manage data more efficiently and effectively.  These practices emphasize data quality, security, and compliance.

  • Establish clear policies and procedures: Develop and document DLM policies that cover every stage of the data lifecycle by defining data retention schedules, access controls and disposal methods.
  • Implement data governance: Integrate data governance to establish accountability, define roles, and set standards for data quality, data security and data privacy. A strong governance framework supports effective DLM through consistent application of policies across all data.
  • Prioritise data security and privacy: Implement strong security measures, including encryption, access controls and regular security audits, to protect data against unauthorised access, breaches and leaks. Align compliance measures with relevant data protection regulations, such as GDPR, COPPA, or HIPAA.
  • Perform regular data quality checks: Conduct regular audits and quality checks of your data for accuracy, completeness and relevance. Cleanse and update data regularly to maintain its value for decision-making and operations.
  • Efficiently store and archive data: Optimise data storage by categorising data based on its importance, usage frequency and sensitivity. Implement archiving strategies to preserve valuable data, reduce storage costs and keep your data accessible.
  • Dispose of data properly: Follow secure data deletion practices to dispose of data you no longer need, or that has surpassed its retention period to prevent unauthorised access.

The Future of DLM and Governance

We’ve come a long way since the mathematician Clive Humby declared, “Data is the new oil” in 2006. As businesses continue to recognise data as a strategic asset, the focus will likely shift towards more sophisticated, automated and integrated approaches to DLM and data governance.

Artificial Intelligence (AI) and Machine Learning (ML) technologies are transforming all aspects of society and will automate many aspects of DLM and governance, from data classification and quality management to security and compliance monitoring. As in other applications, automation will improve efficiency, reduce human error and allow real-time data management and decision-making.

The rise of the Internet of Things ( IoT) devices and the spread of 5G will lead to an exponential increase in data generation, so businesses will need stronger DLM and governance frameworks to manage this deluge of data effectively. Because of this, companies will need to be flexible and quickly scale data management practices.

Concerns over privacy and security will continue to increase and consumers will insist on transparent, ethical data management practices. The rise in data breaches and stringent data protection regulations will drive organisations to adopt a privacy by design approach, integrating data protection into all aspects of DLM and governance processes.

You’ll also likely see an increase in data sovereignty, the concept that data must be handled by the laws of the country where it is located. This will require businesses to implement agile DLM and governance strategies to comply with the complex web of global data regulations.

Conclusion

Maintaining high data quality is a strategic imperative for any data-driven organisation. Implementing best practices can safeguard the integrity and reliability of data assets. Adopting a comprehensive approach to data quality management protects the value of your data. 

Zendata's suite of products embeds Privacy by Design principles across the data lifecycle by emphasising data privacy and compliance from the outset. Automating these processes helps you meet regulatory requirements and protect sensitive information while maintaining high data quality from collection to deletion. With Zendata's solutions, you can handle the complexities of data privacy management with confidence and rest assured that data privacy and quality go hand in hand.

Our Newsletter

Get Our Resources Delivered Straight To Your Inbox

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
We respect your privacy. Learn more here.

Related Blogs

A Guide to Data Quality Tools: The 4 Leading Solutions
  • Data Governance
  • March 20, 2024
Check Out Our Guide To Data Quality Tools
Integrating Privacy by Design Into Your Data Governance Framework
  • Data Governance
  • March 20, 2024
Learn How To Integrate Privacy By Design Into Data Governance Frameworks
Data Quality Management Best Practices: A Short Guide
  • Data Governance
  • March 19, 2024
Discover Data Quality Management Best Practices In This Short Guide
More Blogs

Contact Us For More Information

If you’d like to understand more about Zendata’s solutions and how we can help you, please reach out to the team today.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.





Understanding the Stages of Data Lifecycle Management

March 4, 2024

TL:DR

This article covers the foundational aspects of data lifecycle management (DLM). As a valuable part of your business, data must be handled appropriately at all stages to extract its maximum value and avoid legal and financial penalties. By integrating data governance with DLM, you can develop a comprehensive strategy for handling data efficiently and effectively, from creation to destruction. 

Introduction

Data Lifecycle Management (DLM) is the process of managing data from the time you create or acquire it until you archive or delete it. DLM includes all data lifecycle stages: creation, storage, usage, sharing, archiving and destruction. Each stage requires specific strategies and technologies to manage your data effectively to be useful, secure and compliant with relevant regulations and policies. The three main goals of data lifecycle management are confidentiality, integrity and availability. 

Data governance is your overarching policy for managing and controlling your data assets. It establishes the policies, standards and practices for promoting data quality and security while facilitating effective data sharing to drive decision-making. The relationship between DLM and data governance is symbiotic. Data governance policies guide effective data lifecycle management practices and both have a role in managing data to align with your company’s objectives, regulatory requirements and ethical considerations. Together, they create a structured environment for managing data throughout its lifecycle, improving your ability to leverage data as a strategic asset.

Key Takeaways

  1. Companies must manage data across all lifecycle stages to maximise its usefulness, adhere to security and compliance regulations and build trust with customers and partners. 
  2. Data lifecycle management and data governance are intertwined and following best practices in each will help you leverage data as a strategic asset while adhering to ethical and regulatory standards.
  3. Future advancements in DLM and data governance will be driven by AI and ML technologies, requiring businesses to adopt flexible and comprehensive strategies to manage data privacy, security and compliance effectively.

The Significance of Data Lifecycle Management

As organisations generate and collect data at an unprecedented rate, they need to manage that data efficiently for maximum value. Data lifecycle management gives you a structured framework for handling data throughout its lifecycle.

With a DLM system in place, you can optimise your data storage costs, improve data accessibility and ensure your data handling processes are compliant with increasingly stringent data protection regulations. It allows you to systematically review and delete obsolete or redundant data, which reduces your storage requirements and costs. DLM lets you accurately classify, store and protect your data against unauthorised access or breaches.

DLM is critical for managing the risks and leveraging the opportunities presented by the vast amounts of data generated daily. It provides a structure for handling data responsibly and efficiently to support your business objectives and regulatory compliance.

Brief Overview of Data Governance 

Data governance includes the practices, processes and frameworks for responsible data management within your organisation. It supports Data Lifecycle Management by establishing the policies, standards and procedures that guide how you collect, store, use and dispose of data. 

Data governance provides the strategic framework and guidelines that oversee DLM, so you know your data activities support your business objectives and comply with legal and regulatory requirements. It defines data quality standards, privacy regulations, and access controls for managing data securely and efficiently throughout its lifecycle. With efficient data governance policies, you can maximise the value of your data assets, minimise risks related to data breaches or non-compliance and create a corporate culture of data literacy and responsibility. 

Stages of Data Lifecycle Management

At all of the stages of data processing, there are specific requirements for handling that data. 

Creation and Acquisition 

All of your data is either created or acquired. The creation and acquisition stage is where data enters your ecosystem. Data is generated internally or collected from external sources during this stage. Internal data creation can occur through transactions, employee data entry, automated system logs, or sensor data from IoT devices. Internally created data can capture valuable insights into operations, customer behaviour and market trends. Externally acquired data involves sourcing data from third parties, public datasets, or purchases. It can give you additional perspectives or improve your existing datasets.

The quality, relevance and integrity of the data you collect or create sets the tone for its subsequent use and value to your business. You need to validate, clean and verify your data to meet set standards. To effectively manage data at this stage, you must establish clear guidelines and processes for data collection, consent and compliance with relevant data protection regulations. This is where you lay a solid foundation for data integrity throughout the data lifecycle.

Storage and Maintenance

Once you have data, you need to securely store it and guarantee its accuracy, availability, and relevance. You have to store your data in a way that balances accessibility with security. You can do this through infrastructure such as databases, data warehouses, or cloud storage solutions. You need to categorise data based on its sensitivity, usage frequency and regulatory requirements, which informs the choice between hot — immediately accessible — and cold — less accessible, long-term — storage solutions.

Maintaining data requires you to perform regular data audits, updates and cleaning to protect its quality. Data backup and recovery plans serve to protect against data loss due to hardware failures, cyber-attacks, or other disasters. Security measures, such as encryption and access controls, protect your data from unauthorised access and breaches. Proper storage and maintenance practices keep your data reliable and secure so leaders can use it to guide decision-making.

Use and Sharing

Your data is most valuable when it’s being used and shared. At this point in the data lifecycle, data is a dynamic asset, driving decision-making, operations and collaboration within and outside your company. During this phase, teams analyse, process, and apply data to solve problems, make informed decisions and innovate products and services. 

Sharing data is an inherent security risk. If you plan to share data, whether internally among departments or externally with partners, clients, or regulatory bodies, you need to follow strict governance protocols to safeguard privacy, guarantee security and comply with legal frameworks. You need strong mechanisms for data access control, secure data transfer protocols and adherence to standards and regulations such as GDPR or HIPAA. During this phase, transparent policies define who can access data, under what conditions and for what purposes. These policies let you leverage data ethically and responsibly so you can maximise its value while minimising its risks.

Archiving and Preservation

You remain legally obligated to protect data even when you’re no longer actively using it. Archiving and preserving data focuses on the long-term storage of data you’re not actively using but that still holds value for future reference, regulatory compliance, or historical significance. During this stage, you can keep your data secure and accessible for extended periods, often over years or decades. Data archiving involves transferring data to a dedicated storage system designed for low-frequency access but high durability, such as magnetic tape, archival-quality disks, or cloud-based archival services.

Preservation maintains the integrity and usability of archived data over time. It includes strategies to protect data from technological obsolescence so your data formats remain readable and the hardware and software you need to access the data are available. These practices safeguard your digital assets so valuable data remains accessible and usable for future needs.

Disposal and Deletion

Disposal and deletion mark the end of data's useful life and is the final stage in the data lifecycle. At this point, you need to securely remove data you no longer need, either because it has outlived its purpose, is redundant, or must be deleted in compliance with data retention regulations. Proper disposal and deletion practices also help manage storage costs and reduce risks associated with data breaches.

Data should be irrecoverably destroyed or deleted at the end of its life. You can do this by physically destroying hardware for sensitive data, using software tools for digital shredding, or using secure deletion protocols that overwrite data. You need to thoroughly document these processes to mitigate risks of unauthorised access or recovery. 

The Implications of Data Governance for DLM

Data governance establishes the rules and standards for data quality, security and privacy at each stage of the DLM process. By following data governance best practices, you can guarantee your data is accurate, reliable and secure.

When you define roles and responsibilities for data management, data governance fosters a culture of accountability and stewardship within your company. Clarifying who is responsible for managing data at each stage of the data lifecycle facilitates effective data handling.

Data governance also underpins compliance and risk management efforts. It outlines policies for handling data using methods that comply with legal and regulatory standards so you can avoid the hefty fines and disastrous consequences of noncompliance.

Finally, data governance protects data assets, guaranteeing they’re identified, managed and used effectively. The ultimate goal of effective data governance is leveraging your data for a competitive advantage, driving innovation and growth.

Integrating Data Governance with DLM

Effective integration begins with establishing a governance framework that outlines roles, responsibilities and processes for managing data. This framework serves as the backbone for DLM activities so that data-handling practices line up with your organisational objectives and applicable regulatory obligations.

You need to set up cross-functional governance bodies, such as a data governance council, to oversee the implementation of governance policies across different departments and stages of the data lifecycle. Security and privacy governance policies protect sensitive data against unauthorised access and breaches, while compliance measures guarantee your data-handling practices adhere to laws and regulations.

Best Practices for Data Lifecycle Management

Following best practices for DLM helps you manage data more efficiently and effectively.  These practices emphasize data quality, security, and compliance.

  • Establish clear policies and procedures: Develop and document DLM policies that cover every stage of the data lifecycle by defining data retention schedules, access controls and disposal methods.
  • Implement data governance: Integrate data governance to establish accountability, define roles, and set standards for data quality, data security and data privacy. A strong governance framework supports effective DLM through consistent application of policies across all data.
  • Prioritise data security and privacy: Implement strong security measures, including encryption, access controls and regular security audits, to protect data against unauthorised access, breaches and leaks. Align compliance measures with relevant data protection regulations, such as GDPR, COPPA, or HIPAA.
  • Perform regular data quality checks: Conduct regular audits and quality checks of your data for accuracy, completeness and relevance. Cleanse and update data regularly to maintain its value for decision-making and operations.
  • Efficiently store and archive data: Optimise data storage by categorising data based on its importance, usage frequency and sensitivity. Implement archiving strategies to preserve valuable data, reduce storage costs and keep your data accessible.
  • Dispose of data properly: Follow secure data deletion practices to dispose of data you no longer need, or that has surpassed its retention period to prevent unauthorised access.

The Future of DLM and Governance

We’ve come a long way since the mathematician Clive Humby declared, “Data is the new oil” in 2006. As businesses continue to recognise data as a strategic asset, the focus will likely shift towards more sophisticated, automated and integrated approaches to DLM and data governance.

Artificial Intelligence (AI) and Machine Learning (ML) technologies are transforming all aspects of society and will automate many aspects of DLM and governance, from data classification and quality management to security and compliance monitoring. As in other applications, automation will improve efficiency, reduce human error and allow real-time data management and decision-making.

The rise of the Internet of Things ( IoT) devices and the spread of 5G will lead to an exponential increase in data generation, so businesses will need stronger DLM and governance frameworks to manage this deluge of data effectively. Because of this, companies will need to be flexible and quickly scale data management practices.

Concerns over privacy and security will continue to increase and consumers will insist on transparent, ethical data management practices. The rise in data breaches and stringent data protection regulations will drive organisations to adopt a privacy by design approach, integrating data protection into all aspects of DLM and governance processes.

You’ll also likely see an increase in data sovereignty, the concept that data must be handled by the laws of the country where it is located. This will require businesses to implement agile DLM and governance strategies to comply with the complex web of global data regulations.

Conclusion

Maintaining high data quality is a strategic imperative for any data-driven organisation. Implementing best practices can safeguard the integrity and reliability of data assets. Adopting a comprehensive approach to data quality management protects the value of your data. 

Zendata's suite of products embeds Privacy by Design principles across the data lifecycle by emphasising data privacy and compliance from the outset. Automating these processes helps you meet regulatory requirements and protect sensitive information while maintaining high data quality from collection to deletion. With Zendata's solutions, you can handle the complexities of data privacy management with confidence and rest assured that data privacy and quality go hand in hand.