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What Is Data Efficiency?

Data efficiency refers to the optimal use of data, ensuring that storage, processing, and retrieval are conducted in the most resource-effective manner. By maximizing data efficiency, organizations can reduce costs, improve performance, and enhance decision-making. How does your data strategy measure up, and what steps can you take to boost its efficiency? Join us as we examine the keys to data optimization.
T.S. Adams
T.S. Adams

Data efficiency is the process of making data easier to use, manage, and access. It is typically a concern of larger scale businesses, whose extensive networks and records can easily render the process of finding and using a specific piece of data a bit like finding a needle in a haystack. Although data efficiency is largely a configuration and setup concern — in other words, arranging data in such a way to make it easier to locate and obtain — it also has a significant hardware component to it as well. Outdated and inefficient hardware can make the process of pulling data from a hard drive or network far more cumbersome than it need be. Due to this, data efficiency is a tradeoff; it requires striking the right balance between cost and effectiveness.

The location where data is stored has a good deal to do with its overall efficiency. While solid state hard drives are often the most responsive locations to store data because they can produce and open requested files faster than most any other storage medium, their cost per gigabyte of storage is relatively high. Older storage mediums, such as tape backup drives, are quite inexpensive per gigabyte of storage, but the tradeoff is that their access speed is fairly slow. This cost versus benefit tradeoff is the crux of creating efficient storage systems.

Woman doing a handstand with a computer
Woman doing a handstand with a computer

Data efficiency aims to make the most frequently used data on the network easier to access by placing it on high-cost, high-power storage devices, while transferring older archive data to slower, less expensive alternatives. By doing this, individuals working on the network have faster access to vital data without crippling the organization's resources and budget. Other techniques involved in making data storage more efficient include data compression, which is the process of shrinking files down to their smallest possible size, and deduplication, which is using software algorithms to eliminate duplicate files from the network.

Compression and deduplication can free up valuable drive space on a network, further enhancing efficiency. Like people, computers have a far easier time completing a search when the number of files being searched is relatively small and the average size of the files is correspondingly modest. Through regular culling of unnecessary files and the elimination of excess space within the files themselves, the data efficiency of the network is further enhanced.

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Discussion Comments


Our company data badly needs deduplication. There are so many copies of documents and it's very difficult to keep track of it all.


There are storage optimization products out there that businesses can use for more efficient data storage. There is actually a new product on the market almost every month and they keep getting better and better. We did not have these options when I started my business in the late 90s. Data center efficiency is much easier to achieve now.


Data efficiency is difficult, especially in large businesses. The amount of data that business have to store is increasing every single day. Of course, all of it can't be stored in one system. They are stored in different systems depending on the type of data and the need for accessibility like the author of the article said. This makes data storage a little bit more efficient, but the entire system is far from its potential efficiency.

The truth is, every data storage method cannot be efficient at the same time. So businesses have to make the call about where to increase efficiency and where to reduce it. Unfortunately, there is no win-win situation in data storage. It's difficult to cut down costs of data storage without negatively affecting efficiency in some operations.

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