Data reduction – Filter your data with efficient data processing.
We’re having to process an ever-growing volume of data through our network-connected devices. The result? A tidal wave of data. Against this backdrop, data processing has become a top priority, but how do you filter out the most important data?
Carry on reading to find out how you can leverage smart software to efficiently process and reduce your data.
IT managers are facing the challenge of processing data. We’re having to process an ever-growing volume of data through our network-connected devices and the development of the Internet of Things (IoT) has created a flood of information.
The expectation is that the volume of unstructured data being saved by businesses will be three times bigger by 2024, which is why it’s important to find the right approach to dealing with it. Automated data processing is essential for filtering out relevant information, but how do you make it happen? By utilising intelligent software for processing and minimising data.
What are the specific benefits of intelligent data processing? Efficiently minimising data lowers costs per GB, storage and energy consumption, allowing you to save large data volumes in the same rack space giving you more physical capacity with less memory.
It all adds up to transparent savings you can predict. Your data is loaded into the system, preventing unexpected storage bottlenecks in your data centre. With structured data, you can innovate more efficiently, differentiate, and bring products to market faster.
Alongside these practical benefits, data reduction also has another important function—it prevents harmful data leaks. The risk of a data leak is significantly reduced when you reduce the volume you have saved and ensure it is in no more than three locations, making your data storage not only more efficient but also more secure.
Filtering critical data
How exactly does data reduction work?
Data reduction is an umbrella term for technologies that reduce storage capacity to process a specific dataset through deduplication and compression. Data processing occurs between measuring the data and assigning a specific meaning to that data.
Every organisation is different, as is the need to reduce data, which is why it can be achieved using a handful of approaches. There are two types of data reduction—data compression and data deduplication.
Data compression
This technique codes information with fewer data bits. Compression algorithms can be lossy (information is lost and the resolution of the data decreases) or lossless (everything is preserved, as statistical redundancy is removed).
Data deduplication
Eliminates data duplicates within a storage volume or across an entire system. Deduplication uses pattern detection to identify redundant data which is replaced by references to a single saved copy.
Data reduction through deduplication and compression makes Flash affordable
All-flash storage is almost always more expensive than traditional spinning disks, especially when it comes down to raw capacity. However, deduplication, compression, and other data reduction techniques, make flash storage solutions significantly more affordable as the cost per stored gigabyte is considerably lower compared to disk storage.
A modern data centre is essential, but what should you consider when implementing one? And which storage solution is the best option for you?
