Rethinking data protection strategies


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Build a data protection ecosystem
The next step is to build a data protection ecosystem, or continuum, that spans the 3DR framework. This is achieved by creating classes of data based on the value of the data to the organization at any given point in time and then creating policies for these data types based on the age of the data (or its "currency"), its frequency of access and so forth. Once you've done that, you can match tools (hardware and software) and services accordingly.

Classifying data is a difficult, but necessary, task. It's one that will become increasingly important over time as data volumes increase, corporate governance and regulatory compliance guidelines become stricter, and organizations look to differentiate themselves from their competitors by fully leveraging the data they generate.

Over the past year or so, a new category of management tools has emerged which, among other things, can help companies categorize their data (usually at creation) into information groups to which policies and rules can then be applied. ESG refers to this category as "intelligent information management." The key point is to make sure the right data protection tools are applied to the right data at the right time.

This type of data categorization or classification can have several important benefits, and can help organizations achieve the following:

  • Better meet service-level agreements

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  • by making sure RPO and RTO objectives are not only aligned with the criticality (or value) of the data at hand, but are also achievable.
  • Keep data protection costs down by ensuring that critical and non-mission-critical data isn't treated equally.
  • Avoid or minimize potentially stiff regulatory penalties if data isn't recoverable when it needs to be.
  • In general, provide insight into data repositories, which could lead to new business strategies, etc., that go beyond data protection.
The "Data classes and RPOs" chart illustrates the data protection ecosystem or continuum. It plots data class against RPO, and then maps technologies accordingly. As the value of the data (i.e., the class) increases, the tolerance for data loss (or RPO) decreases. Data that's categorized as less mission critical would have significantly more relaxed RPOs and other data protection requirements.

Build as you grow
In an ideal world, organizations would implement all three of these disaster recovery levels from the get-go, but that's generally not practical from an end-user standpoint. There are many considerations, including budgeting and investment protection of existing tape-based infrastructures.

The good thing is that because 3DR is an adaptive and flexible framework, organizations don't have to rip out existing tape-based infrastructures to realize immediate benefits. In fact, many 1DR technologies, such as VTLs, complement tape-based infrastructures nicely. They leverage existing backup and recovery applications and, in many cases, backup processes. Of course, this assumes the organization has categorized its data into data classes or information groups as described earlier. VTLs and other disk-based backup technologies are great backup targets for many, but not all, and they're becoming more affordable options thanks to data deduplication, which can reduce the backup capacity footprint (and disk requirements) significantly.

As an example of this build-as-you-grow strategy, we've seen organizations insert 1DR technologies like VTLs into their existing backup environments and then change their backup policies so they're backing up daily to disk and monthly to tape, rather than daily to tape. The change is nondisruptive and has immediate benefits, including better backup performance, which means fewer backup window-induced headaches and faster, more granular recoveries (i.e., improved RTO and RPO).

A good next step would be to add a 2DR component--that is, some type of remote replication capability for disaster recovery purposes.

This was first published in September 2006

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