What Is Data Lifecycle Management?

Data lifecycle management (DLM) manages data throughout its lifecycle, from its point of entry to destruction. Using data lifecycle management tools in your organization can help organize, automate data migration, and ensure data points are efficiently managed.

Data lifecycle management is essential to a well-organized and structured business that enables better data security and availability. Both are critical to a business’s success over time. A solid DLCM can also help protect your business in case of a data breach or system failure.

Understanding Data Lifecycle Management (DLM)

Data lifecycle management is the practice of managing data throughout all stages of its lifecycle, which typically includes:

• Creation
• Storage
• Data usage
• Data sharing

There are some similarities between information lifecycle management (ILM) and data lifecycle management. However, remember that DLM/DLCM concerns raw data, like files and databases, along with their attributes like file type and age. ILM, on the other hand, goes a bit deeper and focuses more on how pieces of data are connected.

Components of Data Lifecycle Management

Data lifecycle management is a large concept, but it can be broken down into multiple phases or stages that provide a framework for how to work with data at various points throughout its lifecycle.

The way these phases are broken down can vary among resources and individuals, but generally, the data lifecycle follows this structure:

#1 Data Creation

The data lifecycle starts with data creation and collection. Data points are constantly being created by users, device applications, and other sources. Remember that you don’t need to collect every available piece of data to be successful or to truly harness your data’s capabilities.

It’s better to be more selective with the data you collect or create and base your choices on its quality and relevance to your business.

#2 Data Storage

Proper data storage is critical to ensure its integrity and security. Different types of data will be stored in different ways, but regardless, a stable environment is necessary. During this storage and management phase, the data is processed somehow, like through encryption, compression or being cleansed.

Data storage is also the phase in the lifecycle that ensures systems are in place to safeguard data reliability and redundancy and establish practices to recover after disasters like data breaches.

#3 Data Usage and Sharing

This is the phase where the data becomes usable to business members to complete tasks and carry out day-to-day operations. In other words, this is where the data starts to become truly valuable. At this stage, users can access and modify the data however needed to carry out operations like collaboration, analytics, and data visualization.

During the data usage and sharing phase, it’s also possible for new data to be created, allowing for a sort of revolving door for data.

#4 Archive Data

Eventually, the data you’re working with day-to-day will become outdated or simply no longer be needed. This is when that data can be archived in a secure, long-term storage system, like a cloud platform. Data archival allows you to access the data if needed for analyses or other reporting purposes while keeping it out of the way of daily operations.

It’s important that your organization has an archival policy built into its data lifecycle management strategy that determines when, where, and for how long data should be archived.

#5 Data Destruction

Destruction is the end of the data lifecycle. When the data reaches the end of its lifespan and is no longer valuable or relevant, it will be deleted or destroyed from the archives to make more storage space for new active data.

Remember that destroying data must be done securely to avoid violating any data protection regulations.

Data Creation and Acquisition

Data can be created and gathered in several different ways from various sources. However, the three most common ways include:

• Data Acquisition – Acquiring existing data that was created outside of the organization.
• Data Entry – Manually entering new data into the system.
• Data Capture – Capturing data generated by devices used within the organization.

There are best practices to consider with data acquisition to ensure you’re gathering quality data in an efficient way. Those guidelines include:

• Collect only the necessary data.
• Be mindful of legal or privacy restrictions on or collecting data.
• Consider the source of the data.
• Create new data only when appropriate to minimize duplication.

Data Storage and Management

Data storage and management are important to ensuring data integrity and accessibility. To make sure you’re storing and managing data in the most efficient way possible, you should keep these best practices in mind:

• Have strong file naming and cataloguing policies in place so stored data is easy to find.
• Use metadata to distinguish amongst data sets.
• Use multiple levels of documentation to contextualize different data sets.
• Commit to data culture to ensure you prioritize data experimentation and analytics.
• Take measures to preserve data quality and security.
• Invest in high-quality data lifecycle management software.

It’s also important to consider the best storage method for your organization’s data. Some storage locations to consider include:

• Desktop or laptop computers.
• External hard drives.
• Cloud storage.
• Flash drives.

You could also use the 3-2-1 methodology for data storage. This practice suggests you store three copies of your data and use two types of storage methods, with one of them being stored offsite. This is an easy way to ensure there is always a backup copy of the data in case it is lost or destroyed.

Data Usage and Processing

Data is abundant and can be extremely valuable to businesses in today’s digital-focused world. Data you gather from customers and other internal and external sources can uncover insights about your business and audience that can help drive better decision-making for your organization.

To have data you can rely on for efficient decision-making and general day-to-day operations, it needs to be processed properly. There are several ways to do this, and every organization will have its own set of policies and techniques. Some general tips or guidelines that can be useful include:

• Conduct regular data backups.
• Use automation tools to streamline tasks.
• Analyze errors through cluster and event analyses.
• Closely monitor any manual data entry and provide constant feedback.

Data Security and Privacy

Security is one of the major goals of data lifecycle management. A solid data lifecycle management process ensures that confidential information and personal data is always protected against breaches and theft, as it offers an end-to-end approach to security.

Data security and privacy concerns consumers and organizations. It’s critical that your business has practices in place that guarantee data security and follow data privacy regulations. Some strategies you can take to ensure compliance with data privacy regulations include:

#1 Learning the Data Regulations

Step one is gaining a solid understanding of the data privacy regulations that apply to your specific business. Different countries, states and regions can have different laws, so it’s important to learn what laws apply to you.

Some common data privacy regulations you may face include the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and the Health Insurance Portability and Accountability Act (HIPAA).

#2 Creating a Data Inventory

Once you grasp the regulations you must adhere to, you should start creating a data inventory. This is the process of identifying all the personal data your organization collects, processes and stores. It’s important to know what data you’re collecting, where it’s from, who can access it and what it’s being used for.

Not only is this information that your consumers will likely want to know as portions of the data you collect may pertain to them specifically, but it also helps you understand the risks associated with the data you’re collecting and how you can adjust to ensure compliance.

#3 Developing Data Policies

It’s also important to develop policies and procedures you and your team can follow to ensure all interactions with data within your business comply. These policies should clearly explain and govern how personal data is collected, processed, and stored in your organization. Once established, regularly review these procedures and policies to ensure they’re still relevant and effective as your business grows and changes over time.

#4 Educate Your Employees

Your employees are a large part of ensuring your business maintains compliance with data privacy regulations, as they interact with the data the most. That’s why it’s important to educate and train them on data privacy policies and procedures, their roles and responsibilities regarding compliance, and what the consequences of non-compliance could be.

#5 Conducting Regular Audits

With your policies in place and your employees trained, you should conduct regular audits to ensure you’re still maintaining compliance and you don’t need to make any changes or updates. Additionally, regular audits allow you to identify any gaps or weak points in your data privacy policies and procedures that could use attention.

#6 Implementing Other Technical Measures

Lastly, you should implement technical and organizational measures to add extra security to your data privacy policies. This could include measures like securing personal data through encryption or appointing a data protection officer to oversee compliance with regulations.

Data Archiving and Preservation

When you archive data, you move old data that you’re no longer using to a separate storage space. This practice ensures important data sets are preserved and still accessible long-term.

Organizations often archive data to meet compliance requirements. For example, doctor’s offices may be required to keep patients’ records on file for a certain amount of time. However, retention regulations will vary between industries. ?Thus, you should have a company-wide practice in place for data archival.

There are several ways to preserve your data, most of which are cloud-based or offline storage options. The method you choose for archiving your data will depend on the type and volume of the data, the size of your organization, and your general needs and capabilities.

Regardless, most data archiving is done with data archiving software that can automatically move old data to an archive based on an established archival policy. Still, methods for effective data archival generally include:

• Hard Drives
• Flash Storage, like USB, memory cards or solid-state hard drives
• Google Cloud
• Amazon S3 Glacier and S3 Glacier Deep Archive from Amazon Web Services
• Microsoft Azure Blob Storage

Data Destruction and Disposal

Data destruction occurs at the end of the data’s lifespan when it’s no longer useful and is really a way to safeguard both your business and customers. It’s important to have practices in place to safely dispose of data when it reaches the end of its lifecycle. This practice prevents unused data from falling into the wrong hands.

You can’t just delete data. However, depending on where and how you operate, there are likely legal regulations in place that dictate how data needs to be disposed of. While there are few regulations for disposing of data compared to those in place to protect data privacy and security, you may be able to find policies specific to your state, like California’s Disposal of Customer Records Civil Code.

There are several different ways to destroy or dispose of data, and the method you choose will depend on your business’s specific needs. Three common methods aside from deleting data and physically destroying the storage device include:

#1 Wipe

Wiping is the process of erasing data from a device so no one else can access it. On a hard drive, you can wipe the data by connecting it directly to a wiping device or software. Wiping will allow the device to be used again in the future since you’re not physically destroying it, but it is a rather time-consuming process. To do it thoroughly can take several hours, but it can take even longer if you have multiple drives to wipe that are full of data.

#2 Overwrite

Overwriting is like wiping as it uses software to wipe the data off a hard drive or digital storage unit, but it does so by overwriting the data with unreadable patterns of ones and zeroes. Like wiping, overwriting can be time-consuming if you have a lot of data to work through, especially if you’re working with high-risk data that may require multiple passes of the overwriting software to be fully disposed of.

#3 Degaussing

With degaussing, you destroy the data by demagnetizing the hard drive or storage medium to neutralize the device and destroy all its data. It’s a very quick and effective method, but when you degauss a piece of equipment, it becomes impossible to reuse the device. Because of that, you can’t verify that the data was destroyed by checking the drive.

FACT: The only way to check that the data was truly destroyed is by using an electron microscope, which can be both expensive and impractical for a lot of businesses.

The Role of Technology in DLM

Data lifecycle management is crucial to modern businesses existing in today’s tech and data-driven landscape. While data lifecycle management is something you could attempt to do manually, that opens the door for human error to harm your data, even making it unusable in the worst cases.

Luckily, though, there are several technological advancements impacting data lifecycle management, making it easier and more efficient by automating and enhancing all stages of the data’s lifecycle.

For example, in the data storage stage, you could deploy a Relational Database Management System (RDBMS) to store your data. These systems regularly retrieve data from sources, store it, and delete other data points and predetermined times to maintain its usefulness.

Commonly used RDBMS software includes:

• Oracle Database
• Microsoft SQL Server
• MySQL

There are also several tools and software that can help with data processing when you reach that stage of the lifecycle when the raw data you’ve collected is being transformed into useful information and insights. Some common data processing tools include:

• Hadoop
• Apache Spark
• Python
• JavaScript

Conclusion

Data lifecycle management is the process of managing data throughout its lifecycle, from its point of entry to data destruction. Using a data lifecycle management practice in your organization can help you organize data, automate data migration, and ensure your data is being managed as efficiently as possible.

Data lifecycle management is key to a well-organized business that enables better data security and availability. Having a solid data lifecycle management system can also help protect your business from a data breach or system failure. If you don’t already have some form of data lifecycle management in action, it’s not too late to get started.