Reducing Data Volume: The Value of Deduplication

The world is experiencing an explosion of data like never before and organizations must find new, more efficient ways to store, manage, secure, access, and use that data. A lot of valuable insights lie hidden within the types of data being generated today, and those insights can help organizations identify production bottlenecks, improve the customer experience, streamline processes to increase agility and much more.

At the same time that data volumes are skyrocketing, the costs of storage infrastructure and management tools are diminishing. These factors often drive organizations to embrace the strategy of storing all of their data for long periods of time—or forever—no matter what it is or where it came from.

Just because you can store more data more cheaply today, it doesn’t necessarily mean you should do so indiscriminately. Not all data is created equal, and some types of information contain much more value than others.

There can also be a lot of redundancy in data stores. If you have information pouring in from your customer relationship management platform, sales, technical support, human resources, product marketing and so on, there can be overlap. Duplicate data can also be generated through regular backups, file sharing, data entry or import/export errors, inaccurate data input by customers and so on.

This redundancy can bloat your stored data volumes and make it harder to pinpoint the information you need in the moment you need it. In addition, it can drive up storage costs. While storage is cheaper now than it was before, there’s still no reason to pay for more than you really need.

Data reduction techniques allow organizations to reduce the overall size of their data, which reduces their storage footprints and costs and improves storage performance. One of the valuable tools in the data reduction toolkit is deduplication.

Read: How Hyperscalers Can Maximize Data Storage Capabilities

What is data deduplication and how does it work?

Data deduplication is a type of data compression that deletes redundant information on a file or subfile level. In a large global enterprise, for instance, that redundant data can take up a lot of space in the company’s storage systems. By eliminating duplicate information, that enterprise’s systems will retain just one copy of that data.

To dedupe data, an application or service will analyze entire datasets at the level of files or blocks. It is often done in combination with other data compression techniques to significantly reduce data size without compromising its accuracy and authenticity.

File-level data deduplication was the first type of dedupe and it involved deleting redundant copies of files. In place of those deleted files, the system would create a sort of digital “pointer” that would point to the original, retained file in the repository.

File-level dedupe is a bit limiting, however. Consider how people share documents today and make changes and updates. Different versions of the same document, containing minor differences only, weren’t considered duplicate.

Block-level data deduplication is more granular. It goes deeper into the data and is therefore more effective at rooting out duplicated data within a file. It works by assigning a “hash” to each block of data—blocks being smaller chunks of information within a file—and that hash acts as a unique identifier or signature of the block. If the system detects two identical hashes, one is deleted as duplicate.

So, for a document file that has been changed, instead of saving the entire document again with minor changes, the system will only save the blocks that have changed in the new document—retaining the original as well as the minor changes.

Depending on the system, there are two approaches to data deduplication:

• • • Inline dedupe – the system analyzes, deduplicates and compresses the data before it is written to storage. This approach can save wear and tear on the storage drive because less data overall is written.
    • Post-process dedupe – all data is written to storage and then the system is set up to do regular dedupe/compression tasks as desired. This approach is often referred when it’s not clear how capacity optimization would affect performance.

Deduplication can be beneficial across an entire organization, but there are some use cases and workloads where it really shines. One of those is virtual environments, such as virtual desktop infrastructure (VDI), because a high amount of data is duplicated in these desktops. It can also be ideal for sales platforms, where accurate, clean data is a must and informational errors have the potential to affect customer relationships.

Read: Data Retention Policy: What to Keep and What to Delete

Why should organizations care about deduplication?

Data is a critical part of any modern organization’s success. While it’s possible to retain more data than ever, it’s important that that information be clean, accurate and usable. Only then can an organization extract its hidden value. The following are some other reasons organizations should dedupe their data.

Increased productivity – eliminating the bloat can make it faster and easier for employees to find the information they need.

Improved network performance – duplicated data can drag down the performance of networks and storage applications.

Reduced storage costs – free up room on storage drives and store more vital data within a smaller footprint.

Decreased management burden – smaller data volumes are easier to update and manage.

Better customer experiences – duplicated or outdated versions of data can cause customer frustration or errors in orders, etc.

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Choose Phison as part of your data management strategy

Data reduction techniques, such as deduplication, can help keep your business-critical information accurate and up-to-date. However, they’re only one part of a smart data management strategy.

Another important factor in optimal data management is choosing the right storage solutions and tools. As an industry leader in NAND flash storage IP, Phison SSDs and other products can be vital components in today’s storage environments. Whether you need high-performance, high-capacity storage for AI/machine learning projects and massive data analytics operations or low-power-consumption solutions to save on energy costs in the data center, Phison can help.