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Data Management

Data management covers the full lifecycle of information, from creation and use to storage and retirement, and has never been more critical given today's growing privacy regulations. Understanding each stage of the data lifecycle is essential to building a comprehensive and compliant data management process.

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About this video

Data management has grown into one of the most critical disciplines in modern IT, driven in large part by the rapid expansion of privacy regulations and compliance requirements affecting organizations of all sizes. As laws governing how data is collected, used, and protected continue to evolve, the pressure to manage data responsibly and systematically has never been greater. A structured approach to data management is no longer optional — it is a foundational requirement for any organization handling sensitive or regulated information. Central to effective data management is the concept of the data lifecycle, which frames data not as a static asset but as something that moves through distinct stages over time. Data is created or collected, then actively managed through use, sharing, storage, transfer, copying, and backup, with versioning adding another layer of complexity throughout. Each of these stages carries its own risks and responsibilities, requiring deliberate policies and controls to ensure data remains accurate, accessible, and secure. One of the most commonly overlooked phases of the data lifecycle is retirement — the point at which data is no longer needed and must be properly archived or destroyed. Neglecting this stage can expose organizations to unnecessary legal, security, and compliance risks. A truly comprehensive data management process accounts for every phase of the lifecycle, from initial ingestion through final disposition, ensuring that no stage is left unaddressed.

What you'll learn

What's covered

Data Management

Aligned to

ISC2 CISSP
2.4 Manage data lifecycle
NIST CSF
ID.AM-08 Systems, hardware, software, services, and data are managed throughout their life cycles.
NIST 800-53
SI-12 Information Management and Retention
CompTIA Security+
4.2 Explain the security implications of proper hardware, software, and data asset management.

Key terms

Data Lifecycle
The stages data passes through from creation and active use through archiving and final destruction.
Data Integrity
The assurance that data has not been altered or corrupted during storage or transmission.
Data Loss Prevention
DLP
A set of tools and processes designed to detect and prevent unauthorized access, use, or transmission of sensitive data.
Configuration Management
The process of tracking and controlling changes to hardware, software, and documentation throughout a system's lifecycle.
Data Archival
The process of moving data that is no longer actively used to long-term storage for retention and compliance purposes.
Data Destruction
The process of permanently eliminating data from storage media through methods such as wiping, degaussing, shredding, or incineration to prevent recovery.

Topics

Data Lifecycle Data Management Data Privacy Privacy Regulations Information Governance Data Classification

Transcript

The Data Life Cycle

Data management is a huge topic, and with today's privacy concerns and all the laws and regulations that are coming out, there is more attention paid to data management than there ever has been before. So it's very critical that we management.

There are a lot of aspects to data management, too much to cover right now, but for the most part you just have to think about data as having a life cycle.

Within here we create data: data is entered in, it's collected, it's compiled. We manage that data: we use it, we share it, we store it, we transfer it, we move it, copy it, we have backup, we have versioning of it. So we have to manage this data.

And then we have to retire the data, which often gets overlooked. We need to archive or destroy this data.

So we just have to think holistically when it comes to the data life cycle, to make sure that we're thinking about every aspect along the way and create a comprehensive data management process.

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