What is data lifecycle management, and why do you need it? Find this out with Iron Carrot
Data lifecycle management (DLM) is an approach to managing data from its creation (or capture) at data entry, where and how it is used until it is finally destroyed. The data lifecycle is separated into stages and moves through them as it completes different tasks or meets specific requirements or criteria.
Why does your firm need a clear definition?
If you do a Google search, you’ll find that various experts define data lifecycles as having 5, 6, 7 or even 8 phases. As with all things data-related, these principles and frameworks must be adapted for your firm’s purposes.
Most people in the firm will only see data at a specific phase of the lifecycle: when the data is relevant to them. They need to be able to trust the data that they see, and following the data management lifecycle best practice is a way to ensure that they are looking at quality data.
If you want everyone who plays a part in managing data to understand their role, you’ll need to clearly set out data lifecycle management phases or stages specific to your firm’s needs.
Assuming that there isn’t a data lifecycle in your firm already
Just because it’s not written down in one place doesn’t mean that a data lifecycle doesn’t exist. It might not exist as a fully formed data lifecycle for each vital piece of data, but it probably exists in part somewhere. There are probably guides and some knowledge (tacit or explicit) for each stage of the data lifecycle on a team-by-team or system-by-system basis.
By assuming that there isn’t anything in place, you will risk alienating key stakeholders and reinforcing silos instead of bringing key stakeholders together under the banner of the data governance framework or Centre of Excellence.
When building data lifecycle documentation, remember to ask data stewards about any existing processes.
Letting each department set their own lifecycle standards
It’s tempting to accept documentation on a system-by-system basis, but this will result in each department setting its own data lifecycle standard.
Data governance tries to break down silos and manage data holistically across the firm. That means looking at the critical data assets through their whole lifecycle, wherever that data asset goes.
One of your data governance goals should be a single data lifecycle standard for all departments.
Leaving it all to the IT Security team
It is tempting not to worry about why, when, and where the data goes by letting the IT Security team worry about it. However, their focus will be on the technology rather than the business processes or usages.
Leaving the management and security of data flows to IT Security does nothing to break down functional silos and create better-managed data firmwide.
Inability to provide feedback to upstream systems
A big part of the data lifecycle is making sure that the quality of the data is high. Errors, omissions, and problems are most likely found by the people who know the data best – and that’s often the end users in the practice and business services teams.
If your data lifecycle process does not include a straightforward feedback loop, you will miss out on many inputs that will increase your data quality and make it more useable.
Not including retention or deletion of data
Most law firms have historically been packrats when it comes to data. There is a fear of deleting something in case it’s needed later.
Of course, stricter laws and regulations around assets such as personal data have helped change this approach. But retention and deletion strategies need to be carefully thought through.
It is no good automatically deleting something after 7 years when your reporting requirements are for 10 years’ worth of data.
Data Lifecycle Management, the Iron Carrot way
Data Lifecycle Management is the sequence of events, underpinned by robust protections, that every piece of data goes through on its journey from collection to eventual archival or deletion.
- Protect: Legislation, regulation, client requirements and common sense are applied to the data.
- Create: A piece of useful or relevant data arrives in or is created in the firm. It is clear what this piece of data is and where it belongs.
- Store: The data is filed or stored in the relevant place with the appropriate metadata. It may also be enriched with information from a third-party data service. A quality assurance process makes sure that it has been done correctly.
- Use: The data is used directly by an end user from the system it is stored in or shared externally and internally, including a data repository for reporting or analysis purposes. Analysis can be done by a human or machine (AI or automated process).
- Destroy or Preserve: The data has reached the end of its retention period and has no legal, fiscal, or administrative value, so it is securely destroyed or preserved in an archive for historical reference in compliance with applicable firm policies.
Data management lifecycle documentation
Most firms do not have a single policy which covers data lifecycle management. What they create instead is a data management lifecycle best practice document. These vary in scope from a single-page ‘aide memoir’ to a multiple-page guide.
This lifecycle is visualised through cross-functional data lineage and business process documentation.
Conclusion
Having a clear data lifecycle management for the firm’s critical data assets is an integral part of being able to do more with data. Leverage what’s already happening to document and share a cross-functional best practice, taking care to fill in any gaps that are barriers to successful adoption.
