Confidently erase data in active environments and from used IT assets.
Boost services throughout the device lifecycle—from first sale to end-of-life.
Expedite processes, recover more marketable product, and increase services.
Home » Resources » What is Data Lifecycle Management?
Data lifecycle management (DLM) has been defined in many ways—so much so that it’s causing quite a bit of confusion in the technology space.
For example, TechTarget defines data lifecycle management as “…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. DLM products automate the processes involved, typically organizing data into separate tiers according to specified policies, and automating data migration from one tier to another based on those criteria…”
Gartner, for its part, defines data lifecycle management as “…[the] process of managing business information throughout its lifecycle, from requirements through retirement. The lifecycle for data crosses different application systems, databases and storage media. The cycle is made up of phases of activity including create, use, share, update, archive, store and dispose. Data management best practices indicate a need for each phase to be governed by a framework that provides for the most effective enterprise business decisions.”
These definitions are very similar, though Gartner better outlines the specific steps required in each phase of the data management lifecycle and doesn’t put as much emphasis on DLM products. So why is there so much confusion about this term? Because ‘lifecycle management’ can take many forms, and data lifecycle management is often confused with information lifecycle management.
Information lifecycle management (ILM) and data lifecycle management (DLM) are often used interchangeably, but they do have some major differences.
Information lifecycle management is a comprehensive approach to managing the flow of an information system’s data and associated metadata from creation and initial storage to the time when it becomes obsolete and is destroyed.
But here’s the difference: Products that support DLM manage general attributes of files (i.e. type, size and age), whereas ILM goes beyond these general attributes to search for various types of stored files (i.e. specific piece of data, such as a customer number).
In addition, information lifecycle management should be used to describe both physical and digital information, while data lifecycle management should be used to only describe data management.
This distinction is becoming more important, as the upcoming EU General Data Protection Regulation: Right to be Forgotten comes into effect in May 2018. With this regulation, customers will be able to request their information be erased upon request and be provided proof that it has been. More companies will need to understand the technology that supports ILM to comply with this regulation to address these requirements.
Download the whitepaper: Enterprise Data Protection: What You Need to Know to Protect Corporate Data Throughout Its Lifecycle.
ESG regulations are ramping up. Enterprise data use is skyrocketing. And 39% of businesses are missing the connection. See survey results from 1,800 leaders.