Data Lifecycle Management

The importance of the Data Lifecycle Management (DLM) process 

Introduction 

Today where there is so much talk of digital transformation, data is a critical asset driving business decisions and operations.  This process provides a structured approach to handling data from its creation to its disposal.  It is involves policies and procedures to ensure that data remains valuable, secure and is aligned with regulatory compliance.  

Data Lifecycle Management and its importance to Business 

A proper and focused Data Lifecycle Management (DLM) process establishes clear processes that ensure security, regulatory compliance, operational efficiency and improved decision-making.  

The importance of Data Lifecycle Management 

Implementing a robust DLM strategy is crucial for organizations for several key reasons:  

1. Mitigation of risk.  Protects against data breaches, corruption of data.  Failure to address the risks related to poor management of data can and does lead to serious financial and reputational damage. 
2. Regulatory Compliance.  This is key.  In an ever increasingly regulated business environment, the ability to adhere to regulations is paramount for an organization.  These include data privacy regulations which focus on data subjects and their data.  DLM with its focus from creation to removal is well aligned with data protection regulations like GDPR. 
3. Efficiency.  A well-tailored DLM process will more effective use operations, reduction in operational cost.  DLM processes result in better quality data.  It ensures more data accuracy and more reliable data to be used in informing strategic decisions.   
4. Decision making.  As per above, high quality data that is secure, accurate, reliable as the basis for decision-making will be of significant benefit to the organization. 
5. Following DLM will result in more secure data when its framework is applied – encryption, secure access, throughout the entire existence of the data.  

The DLM stages 

• Data creation/collection.  implement clear standards, classify according to sensitivity.  Automated tools are of benefit here to ensure data quality. 
• Storage and maintenance.  When it comes to storing data, again, according to sensitivity and value to the organization, care must be taken to ensure that it is secure.  Secure at rest (similarly, secure in transit).  Perform regular backups, encrypt, mask, access controls. 
• Usage.  Data must be accessed and used only by authorized users and in an authorized manner consistent with the value to the organization. 
• Sharing/communication.  There must be clear policies around sharing of data and how it is communicated.  Classifying data would provide the basis for how data is shared and if it can be shared.  It will also guide conversations around how it is communicated – SSL, SFTP, etc. 
• Archiving.  A key stage.  All organizations would love to have all the data forever, however, with regulations like GDPR, this is now not permissible.  There must be clear policies around the archiving of data, its retention bearing in mind the risks that holding onto data can bring. 
• Destruction/disposal.  Following the above is the removal of data.  Bear in mind, there can be other regulations that can supercede data privacy regulations so they must be considered.  However, a properly instituted DLM process would ensure that data is destroyed in a manner that makes it impossible to recover.  This should be documented for compliance purposes. 

 Applying CIA triad  

The core of DLM ensures the CIA triad – confidentiality, integrity and availability.  

1. Confidentiality ensures that data is only seen, modified and processed by authorised person. 
2. Integrity ensures that data is accurate, consistent and has a high level of trustability. 
3. Availability ensures data is accessible when needed (by authorized users of course).  

At each stage of the DLM process, the triad can be applied.  Without going into detail, confidentiality in the creation/collection stage is about ensuring that data is only seen by those whom it should after going through the data classification process.  In terms of the Integrity part of CIA, it is about ensuring accuracy and consistence and finally under Availability, it is about ensuring that the data collected is made available for authorized users by applying the necessary processes, infrastructure around that data.  This can be done across all stages of the DLM processes.  

Data Classification 

This process involves the organization and tagging of data based on sensitivity, value and other characteristics.  An effective and thorough classification ensures that organizations can effectively protect and manage their data.  

Conclusion  

With data privacy and security being of the highest priority throughout the lifecycle, DLM ensures a well maintained data classification, privacy by design, data minimization, access controls, use of encryption, perform regular audits and establish a robust incident response plan.  In addition to the above, there is the International Data Management Body of Knowledge (best practices and covers all stages of the DLM) and COBIT.  

Through the development of clear policies and practices covering each stage, use of automation everywhere possible and an alignment with business goals, the DLM ensures that organization protect an asset that is of significance.