What is Data Lifecycle Management, and why do we care about it?
Meaning of Data Lifecycle Management
I googled looking for the meaning of Data Lifecycle Management and came up with this definition – “Data life cycle management (DLM) is a policy-based approach to managing the flow of an information system’s data throughout its life cycle: from creation and initial storage to the time when it becomes obsolete and is deleted.”
Why do we care about it?
From an individual’s perspective, we care about the treatment of our personal data. When we give our personal information to a business e.g. bank, hospitals, retail store, etc., we want that business to have policies, governance, and processes in place to ensure that our data is:
- Used only as we want it to be used
- Transportable so that we can request our data and move it to another business if we desired
- We also want to know that our data will be erased if we want to disassociate ourselves from that business or the way our data was used.
From an enterprise perspective data is critical to running day to day operations, to competing in the market place, and to comply with regulatory requirements.
Business care because sloppy management of data is expensive. If data never dies, data volume will continually expand which means more infrastructure will be needed to store old or unused data. If data is data is inaccurate, conflicting, or not timely, the business will suffer and lose its competitive edge. If data is misused or deleted without proper governance, regulatory authorities will impose heavy fines.
Providing transparency to the life cycle of data is key to enabler to efficiently managed data, provide value to the business, while maintaining compliance with regulatory requirement.
Transparency through out Data Lifecycle
Capture: how does data enter the enterprise? Capture involves understanding how “new” data comes into the environment. How many ways does data enter? Email, Virtual Data Rooms, SMS, PDFs, data entry into online form, are just some of the ways data enter the enterprise. Do we have a process to manage the many ways date enter the environment? Do we have a process to tag data from regulated users from non-regulated users
That’s it for this post… we will continue with expansion of Capture, the 1st phase of Data Lifecycle Management on the next blog post.
Leave your thoughts on the comments section. Or feel free to contact me at email@example.com
Happy to send an editable version of the 7 phases of a data lifecycle images, just send me an email with your request.
About the author:
Juliana Carroll is a problem solver, strategic thinker with 15+ years of Fortune 500 consulting experience delivering measurable results that align with both business competitive requirements and regulatory compliance.
Juliana have delivered exceptional results for organizations including Morgan Stanley, Prudential, Merck, Guardian Life Insurance, Blue Cross Blue Shield of Florida, and Deutsche Bank.