Friday, July 12, 2024

What is Data Compression & How Does it Work?

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Data compression is the process of using encoding, restructuring, and other modifications to reduce the size of digital data files without changing their fundamental properties. By reducing the size of files, data compression minimizes the network bandwidth required to share them and the capacity needed to store them, lowering costs. This guide offers an in-depth exploration of how data compression works and why it is valuable as well as the most common methodologies, advantages, challenges, applications, and more.

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How Does Data Compression Work?

At a high level, data compression works by encoding the original, target data in fewer bits, reducing the size of the data. When needed, the data can be uncompressed, or decoded and retrieved.

The process involves two algorithms—one for compression and one for reconstruction. The original data is reduced into an encoded representation of itself. When accessing or retrieving the information, the reconstruction algorithm decompresses the data representation into a newer format to make it as similar to the original as possible.

Data compression is valuable because raw data is not ready to use. Noise and redundancy can inflate the footprint data occupies needlessly, requiring more storage capacity to retain it and more bandwidth to transmit it over a network. Noisy data refers to data that is distorted, corrupted, or unreadable, while redundant data refers to information that is repeated multiple times within the same dataset.

Data compression is aimed at eliminating redundancy and noise, improving the overall quality of the available information as well as reducing its size.

Types of Data Compression

Generally speaking, there are four different types of data compression:

  • Text compression—primarily uses codes or symbols for patterns and redundancies; reduces overall size, but information remains unaltered.
  • Audio compression—eliminates noise to shrink file sizes, but reduces overall quality.
  • Image compression—similar to text compression; replaces repeated color patterns using codes to reduce size.
  • Video compression—a combination of audio and image compression, removes unwanted images and background noise to reduce overall size at the expense of quality.

Data Compression Techniques

Broadly speaking, there are two overall approaches to data compression. Each is better-suited to certain applications and types of data depending on the desired result.

Lossless Compression

Lossless data compression is non-destructive—it retains the original information and preserves the original file structure, maintaining absolute quality. The original version can be entirely restored. Common applications of lossless compression techniques are archiving and formatting.

It’s primarily used for executable files like documents, software applications, spreadsheets, texts, or other critical system files. Familiar lossless compression formats include ZIP, GIF, PDF, and PNG.

Lossy Compression

Lossy data compression reduces the original size of the data by compromising some detail—it permanently removes unnecessary bits. Although it only discards unimportant information, it still affects the data quality. Common applications of lossy compression are multimedia files such as audio, photos, graphics, and videos.

Good results are possible when executed effectively, but aggressive compression can affect the file quality considerably. As a result, it’s used when some degree of quality loss can be tolerated. The most familiar formats include JPEG, MPEG, MP3, MP4, and MOV.

Data Compression Algorithms

Data compression relies on a wide range of algorithms to work. Here are the most common.

Run Length Encoding (RLE)

This lossless method reduces the number of bits used in data representation. Overall size is reduced but no information is lost. For example, if the data set includes several repeated characters—such as “aaaabbbbcccddee,” the RLE algorithm encodes it as “4a4b3c2de.” The same information is available in fewer bytes, but the data sequence remains unaltered.

Huffman Coding

Another lossless algorithm, this is primarily used for data sets consisting of frequently occurring characters. It generates a unique code for each character based on frequency—when the string is represented using these codes, overall size is reduced but the data remains unaffected.

Lempel-Ziv Algorithm

A lossless algorithm widely used for GIF and TIFF formats, this creates a codebook for encountered sequences. Because codes take up less space, the overall size of the data is reduced.

LZSS (Lempel-Ziv-Storer-Szymanski) Algorithm

This lossless algorithm uses a textual substitution principle based on the dictionary coding technique. First it substitutes a string of symbols using a reference. Then it removes duplicate data and ensures that the new file size is smaller than the original. LZSS can be easily implemented and is widely used for GIF, TIFF, PDF, and text file compressions.

DEFLATE

A combination of the LZSS and Huffman coding algorithms, this lossless technique was initially developed for ZIP files but is now also used for gzip in HTTP compression and PNG formats. It works by finding repeated character sequences and encoding them based on frequency.

Then it uses Huffman coding to compress the data a second time using shorter codes, reducing size considerably. Popularly used for web content compression, it enhances the browsing experience by compressing HTTP responses and reducing load times and bandwidth.

Audio and Video Codecs

Encompassing a wide range of algorithms, these advanced techniques offer significant compression for media files. The popular MP3 format used for audio files utilizes perceptual coding, removing data that is less noticeable to listeners and reducing file sizes.

Similarly, high-efficiency video coding (HEVC), or H.264, compresses video files using entropy coding and motion compensation. This leads to higher compression ratios without compromising visual quality. This video codec is what makes high definition video streaming and conferencing possible.

The Importance of Data Compression

For the modern enterprise, data is central to business functions. It’s used for everything from predictive analytics and trend-spotting to understanding customer behavior, refining marketing strategies, and enhancing user experiences.

Accumulating data sets is not inherently valuable—for data to be advantageous, it must be stored systematically to ensure quick retrieval and accessibility. But blindly expanding storage capacities in response to growing data volumes is neither scalable nor economical.

Data compression is one arrow in an organization’s data management quiver. It helps ensure that storage is optimized and identifies repetitive patterns to streamline interpretation and analysis.

As storage technologies improve, the data compression market is expected to innovate more real-time compression algorithms with minimal loss in quality to meet customer applications. At the same time, as the Internet of Things (IoT) expands across sectors, the demand for data compression solutions that preserve data integrity and security will grow with it.

Advantages of Data Compression

Data compression offers an array of advantages that cater to the specific business needs. Here are the most common.

Storage efficiencies Significantly condenses data volumes, allowing organizations to store more information within the same physical storage space.
Faster speeds Facilitates swifter data transmission across networks; particularly beneficial for businesses operating in cloud environments or those that rely heavily on data transfer across multiple locations.
Performance gains Compressed data can be accessed and processed faster, lading to quicker response times in data-driven applications.
Versatility Can be applied across diverse data formats (e.g. text, images, multimedia content), making it a universally relevant solution.
Scalability Facilitates an adaptable storage environment, enabling businesses to scale capacities in response to fluctuating volumes.

Disadvantages of Data Compression

While data compression offers numerous benefits, there are a few downsides—here are the most notable.

Computational demand The resource-intensive compression process can hog CPU processing power, slowing down systems and affecting concurrent operations.
Reduction limitations The achievable compression ratio is finite, making it an inherent limitation of data compression; not all files can be compressed indefinitely, and there’s often a threshold beyond which further compression is not feasible.
File size limitations Some tools may have constraints on maximum file size, requiring multiple rounds of compression that each diminish quality.
Quality concerns Compression can degrade the quality of the original content, especially when aggressive or lossy methods are employed.
Security issues Some antivirus solutions may struggle to scan compressed files, leaving vulnerabilities unchecked.

Data Compression Uses

Data compression is a useful component for both storage management and data management, making it valuable across most industries. Here are some of the most common applications for it.

Communication

Because data compression reduces file size, it increases the overall capacity of communication channels by using less bandwidth. It also enhances wireless data efficiency—current electronic storage systems extensively use data compression techniques for cost-saving and better space utilization.

Cloud Computing

Data compression maximizes the capacity of cloud storage solutions, ensuring accessibility without excessive storage overhead. It also speeds up file transfer, reduces costs, and optimizes network traffic, simplifying multi-user or multi-location cloud deployments.

File Archiving

With data compression, it is possible to archive large volumes of data and free up system space. Inactive files or data not in regular use are generally archived, and can be retrieved if needed.

HD Streaming

Streaming video users have come to expect seamless experiences with superior visual and auditory fidelity. Compressing multimedia data improves transmission rates, leading to faster streaming, reduced buffering intervals, and consistent high-quality output.

Mobile Data Usage

Mobile users demand fast connections and limited data usage—data compression facilitates smooth media streaming and enhances mobile gaming. Compressed files require less storage and reduce download times.

Healthcare

Diagnostic images from X-rays, MRIs, and other medical tests are often stored in compressed formats, optimizing storage while preserving the quality and integrity of critical patient information.

Bottom Line: Data Compression

As enterprise data use skyrockets—and as their dependence upon data to fuel decision-making across all departments grows in parallel—reliable data and storage management solutions become an essential need. Data compression is just one of the many tools in a data management toolbox. Its applications span domains with applications from enhancing cloud storage efficiencies to ensuring seamless high-definition streaming and safeguarding crucial medical records. With a wide range of techniques and algorithms designed to shift the balance between file size and file quality, data compression is an effective solution for all businesses. As our reliance on data continues to strengthen, strategically deploying data compression techniques will be integral to operational efficiency and resource optimization.

Learn more about how enterprises use data for everything from operational efficiency to customer engagement by reading our complete guide to data analytics.

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