Data Management

Data Archiving: The Basics and 5 Best Practices

July 26, 2021

Topics: Elementary 5 minute read Cloud Data Sense

Data archiving is the practice of shifting infrequently accessed data to low-cost storage repositories. It is an important part of a data management  strategy. The goal is to reduce costs on warm storage while retaining old data needed for future reference or analysis, and information needed for regulatory compliance.

A data archive is often built using cold storage tiers, which can hold large amounts of data at a low cost. There are many types of archival architecture and features, each meeting different needs. However, the minimum requirements are indexing and searching capabilities, which ensure files remain easily accessible.

In this article, you will learn:

Why is a Data Archiving Important?

A data archiving plan is an important part of your data lifecycle management policy, providing you with a way to retain information while staying within a reasonable storage budget. A data archiving implementation typically involves supporting tooling and automation, which help drive efficiency into the process. Here are key functions of a data archiving solution:

  • Data discovery—a data archiving solution can help admins and end-users to easily find files, including spreadsheets, documents, and presentations.
  • Data management—a data archiving solution can help you locate redundant data and remove it, or remove aging files from your files servers.
  • Data visibility—a data archiving solution analyzes, classifies and indexes data before storing it on your servers. This process ensures you can easily perform searches and gain insights.
  • Data compliance—a data archiving solution automatically prepares responses to requests, including compliance audits, business queries, and litigation.

To learn more, read our detailed guides to:

  • Data classification
  • Data classification policy
  • Data discovery

Data Archiving Considerations

The main advantages of data archiving is the ability to significantly reduce storage costs. However, a data archive is not ideal for all use cases. You should not, for example, use a data archive instead of a backup solution.

Data Archiving vs Backup

While data archives and data backups are used as secondary storage repositories, the two do not provide the same value.

A data backup is a copy of data created for the purpose of protecting and recovering data. Backups of data contain critical information needed for quick recovery during data loss or other disasters.

A data archive contains data kept for the purpose of retaining the information for the long term. Data archives often retain infrequently accessed data, which is not critical for recovery or needed for ongoing business continuity.

Data Archiving and Data Lifecycle Management

A data archive and a data lifecycle management perform different functions.

Data lifecycle management processes manage the entire lifecycle of data, from the time a piece of data is created and until it is deleted. Organizations create data lifecycle management policies, which are enforced by administrators and management tools.

Data archiving is often created as part of an overall data lifecycle management program. Organizations create data archiving policies, and the process is then fully automated by an archiving software.

Data Archive and Compliance

Organizations can use data archives to retain general information and to retain information needed for compliance.

A data archive can be used to retain information needed for compliance purposes. This data is often retained for future compliance audits. In this case, the data needs to be easily accessible, to ensure regulatory entities can quickly reference the data.

Organizations often use data archives to story aging data of all kinds. Some data, like corporate information, can be kept according to company policies. Other types of data, like private information, needs to be compliant with relevant regulations. In this case, there are certain regulations and standards the archive must meet in order to ensure compliance.

5 Data Archiving Best Practices: Creating a Strong Data Archiving Strategy

Here are several best practices to consider when creating your own data archiving strategy.

Identify and Sort Data Before Archiving

Take a look at your data and create an inventory. Categorize data into types and then prioritize, carefully considering which data is needed for ongoing operations and which can be moved to the archive. If you need to archive both structured and unstructured data, determine whether you want them stored in separate repositories or in one centralized archive.

Synchronize Data Archival and Data Lifecycle Management

A data archive has an impact on the data lifecycle. When you create your archival plan, consider the lifecycle of the archived data. For example, ask yourself for how long you want to retain archival data and when archival data should be purged. Update your data lifecycle management strategy accordingly. Be as specific as possible.

Plan for Regulatory Compliance

Make sure that your archive strategy is fully compliant with relevant compliance regulations. If you need to enforce strict compliance standards on half of the archival data, but the other archival data can be easily stored, then you might want to consider creating two separate archives.

Select the Right Archiving Tool

There are many types of solutions that can help support your data archiving strategy. Some tools are dedicated to data archiving, while other solutions offer end-to-end data solutions with built-in archiving capabilities. The most important feature of any tool is an efficient search engine.

Develop a Data Archiving Policy

Once you have assessed all the requirements for your data archive strategy, you can create a comprehensive data archiving policy that clearly documents all relevant processes and procedures. The policy should include all data archiving criteria and mechanisms, as well as specific roles and circumstances that determine when and who can access archived data.

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