The Seven Stages of Data Lifecycle Management
Learn about each of the seven stages of data lifecycle management and how it can reduce costs and increase data quality.
Craig Dennis
May 1, 2023
9 minutes
Improved data quality: Data lifecycle management helps improve data quality as at all stages of the lifecycle, there are clear processes to follow and more focus on ensuring everything is running correctly.
Single source of truth: Data lifecycle management ensures you have one location where your data is stored, so you can be confident that data is the same across your company, no matter where it ends up or who uses it.
Security: Important security jobs get done when needed because each stage has a dedicated person to manage it. Situations like not revoking access to someone who has left the business aren’t forgotten about or delayed. With different stages contained in different tools, it helps to mitigate risk, as if there is a breach in a single tool, it doesn’t impact the entire ecosystem.
Costs: Regular management of the data lifecycle can reduce costs. It could be removing data sources or syncs that are no longer needed. Reviewing each data lifecycle stage can show you what is no longer in use and can be stopped to reduce computing and storage costs.
Governance and compliance: Depending on the industry you are in, you have a set of rules and policy regulations to be in accordance with. Data lifecycle management lets you be confident that data is handled efficiently and securely, ensuring you comply with data laws and regulations and your organization’s overall data protection strategy.
Improved decision-making: Data lifecycle management helps keep your data organized, maintained, and easy to access when required. Accessing this data faster means that decisions can be made with practically live data that’s up-to-date.
Data Lifecycle Management Best Practices
Regardless of the stage of the data lifecycle, there are a number of best practices that should be kept in mind that are advisable to follow.
- Roles and permissions: Data security should always be a priority. Managing the roles and permissions is key to maintaining healthy data security. Users should have minimal permissions required to do their role. And if someone leaves the company, their accounts should be shut down immediately.
- Training: Training should be a never-ending process. Each stage of the data lifecycle has different tooling. And will launch new features in the future. Not knowing how to properly use these tools can mean you may not be using them effectively.
- Standardization: It’s important that processes and procedures are well documented so people can follow them. Doing so will help standardize the management of the data lifecycle and means that data is consistent across all the company.
- Best-in-breed tools: Thinking about the architecture of your data lifecycle is important. Taking a Composable CDP approach where you can select the best-in-breed tools helps to break down your data flow into various stages and provides you with the best tooling for each stage.
- Governance: Having your data flows broken down into various components makes it easier for you to maintain end-to-end visibility, as you know exactly what tool you need to go to if you are looking for something specific.
- Monitor and audit data usage: A part of data lifecycle management is monitoring and auditing data. It can help to see if data is being misused and driving up compute costs in the warehouse and helps to ensure people are following the policies and procedures that have been established.
- Continuously review and update data lifecycle management processes: Data lifecycle management isn’t something that happens once. It should be regularly reviewed and updated as business needs, technology, and regulatory requirements change.
Final Thoughts
Data lifecycle management is an important part of any data strategy because it gives you granular control over every part of your data stack – from collection, storage, transformation, analytics, and activation.
Breaking each component of your data stack into its own independent layer removes single failure points so that you can efficiently interchange components in your architecture as needed with relatively low amounts of friction. Ultimately, all architectures will be different. However, the data life cycle management framework is relevant at any scale when managing your data.
