As organizations continue to collect and analyze larger volumes of data, it’s essential that they have strong data management practices in place to ensure that their data remains a valuable asset and doesn’t become a liability. This has led to a growing emphasis on data lifecycle management.
What is data lifecycle management (DLM)?
Data lifecycle management refers to the process of managing data throughout its entire lifespan, from creation to deletion. As it relates to data governance, data lifecycle management involves ensuring that data is used and managed in a way that meets legal, regulatory, and ethical requirements, while also fulfilling the needs of the organization.
How it works
The process of data lifecycle management typically begins with data creation. This involves defining the data that needs to be collected and determining how it will be collected. Once the data is collected, it needs to be stored and organized in a way that makes it easy to access and analyze.
Data is also subject to change over time, so data lifecycle management involves implementing processes for data updates and modifications. It’s important to ensure that any changes to the data are properly tracked and documented, so that the data remains accurate and reliable.
As data ages, it may become less useful or even obsolete. In such cases, data lifecycle management involves determining when the data is no longer needed and should be deleted. This requires taking into account legal requirements, such as data retention laws, as well as the organization’s own policies and procedures.
Why is data lifecycle management important?
Data lifecycle management is critically important in relation to data governance because it ensures that data is properly managed throughout its entire lifespan. By implementing a comprehensive data lifecycle management strategy, organizations can maintain the accuracy, reliability, and usefulness of their data, while also complying with legal and regulatory requirements.
One of the main benefits of data lifecycle management is that it helps organizations to identify and eliminate redundant or outdated data. This reduces the risk of using inaccurate or unreliable data, which can lead to poor decision-making and other negative outcomes.
Data lifecycle management also ensures that data is stored and managed securely, which is particularly important in today’s era of increasing cyber threats. By implementing strong security measures at every stage of the data lifecycle, organizations can minimize the risk of data breaches and other security incidents.
Moreover, data lifecycle management can help organizations to maximize the value of their data. By implementing processes for data analysis and data-driven decision-making, organizations can leverage their data assets to gain insights and make more informed business decisions.
Data lifecycle management goals
The goals of data lifecycle management for governance are aimed at ensuring that data is managed effectively throughout its entire lifespan. Here are some key goals:
- To ensure data accuracy and reliability: By implementing strong data lifecycle management practices, organizations can ensure that their data is accurate, reliable, and consistent. This helps to minimize errors and improve decision-making based on the data.
- To ensure data security: Strong data lifecycle management practices can help to ensure that data is stored, processed, and accessed securely. This helps to minimize the risk of data breaches and other security incidents that could compromise sensitive data.
- To ensure compliance with legal and regulatory requirements: Data lifecycle management helps organizations to comply with legal and regulatory requirements related to data management, such as data privacy and retention laws. This helps to minimize the risk of legal penalties and reputational damage.
- To improve data accessibility: Data lifecycle management practices help to ensure that data is organized in a way that makes it easy to access and analyze. This helps organizations to leverage their data assets to drive better business outcomes.
- To optimize data storage and management costs: By implementing data lifecycle management practices, organizations can minimize data storage and management costs by eliminating redundant or outdated data.
- To enable effective decision-making: Data lifecycle management helps to ensure that data is properly managed and organized, enabling organizations to make more informed and effective decisions based on their data assets.
Recent research and statistics show that data governance is increasingly emphasizing the importance of data lifecycle management. Here are some key findings:
- According to a study by Gartner, by 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset and data lifecycle management as a critical competency.
- A survey by Deloitte found that 65% of executives believe that data management is a critical challenge for their organization, and 59% believe that data governance is a high priority.
- Another study by Experian found that data quality and data governance were among the top challenges facing organizations when it comes to data management.
- The COVID-19 pandemic has also highlighted the importance of data lifecycle management, as organizations have had to rapidly adapt to changing business conditions and manage large volumes of data related to health and safety protocols.
In summary, the rise of big data, artificial intelligence, and other advanced technologies has made data lifecycle management even more critical. As organizations increasingly rely on data to make strategic decisions, it’s essential that they have strong data governance and data lifecycle management practices in place to ensure that their data is accurate, reliable, and secure.