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Digital data
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Digital data
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Digital clock. The time shown by the digits on the face at any instant is digital data. The actual precise time is analog data.

Digital data, in information theory and information systems, is information represented as a string of discrete symbols, each of which can take on one of only a finite number of values from some alphabet, such as letters or digits. An example is a text document, which consists of a string of alphanumeric characters. The most common form of digital data in modern information systems is binary data, which is represented by a string of binary digits (bits) each of which can have one of two values, either 0 or 1.

Digital data can be contrasted with analog data, which is represented by a value from a continuous range of real numbers. Analog data is transmitted by an analog signal, which not only takes on continuous values but can vary continuously with time, a continuous real-valued function of time. An example is the air pressure variation in a sound wave.

The word digital comes from the same source as the words digit and digitus (the Latin word for finger), as fingers are often used for counting. Mathematician George Stibitz of Bell Telephone Laboratories used the word digital in reference to the fast electric pulses emitted by a device designed to aim and fire anti-aircraft guns in 1942.[1] The term is most commonly used in computing and electronics, especially where real-world information is converted to binary numeric form as in digital audio and digital photography.

Symbol to digital conversion

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Since symbols (for example, alphanumeric characters) are not continuous, representing symbols digitally is rather simpler than conversion of continuous or analog information to digital. Instead of sampling and quantization as in analog-to-digital conversion, such techniques as polling and encoding are used.

A symbol input device usually consists of a group of switches that are polled at regular intervals to see which switches are switched. Data will be lost if, within a single polling interval, two switches are pressed, or a switch is pressed, released, and pressed again. This polling can be done by a specialized processor in the device to prevent burdening the main CPU.[2] When a new symbol has been entered, the device typically sends an interrupt, in a specialized format, so that the CPU can read it.

For devices with only a few switches (such as the buttons on a joystick), the status of each can be encoded as bits (usually 0 for released and 1 for pressed) in a single word. This is useful when combinations of key presses are meaningful, and is sometimes used for passing the status of modifier keys on a keyboard (such as shift and control). But it does not scale to support more keys than the number of bits in a single byte or word.

Devices with many switches (such as a computer keyboard) usually arrange these switches in a scan matrix, with the individual switches on the intersections of x and y lines. When a switch is pressed, it connects the corresponding x and y lines together. Polling (often called scanning in this case) is done by activating each x line in sequence and detecting which y lines then have a signal, thus which keys are pressed. When the keyboard processor detects that a key has changed state, it sends a signal to the CPU indicating the scan code of the key and its new state. The symbol is then encoded or converted into a number based on the status of modifier keys and the desired character encoding.

A custom encoding can be used for a specific application with no loss of data. However, using a standard encoding such as ASCII is problematic if a symbol such as 'ß' needs to be converted but is not in the standard.

It is estimated that in the year 1986, less than 1% of the world's technological capacity to store information was digital and in 2007 it was already 94%.[3] The year 2002 is assumed to be the year when humankind was able to store more information in digital than in analog format (the "beginning of the digital age").[4][5]

States

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The 3 states of data.

Digital data come in these three states: data at rest, data in transit, and data in use.[6][7] The confidentiality, integrity, and availability have to be managed during the entire lifecycle from 'birth' to the destruction of the data.[8]

Data at rest

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Data at Rest vs Data in Use.

Data at rest in information technology means data that is housed physically on computer data storage in any digital form (e.g. cloud storage, file hosting services, databases, data warehouses, spreadsheets, archives, tapes, off-site or cloud backups, mobile devices etc.). Data at rest includes both structured and unstructured data.[9] This type of data is subject to threats from hackers and other malicious threats to gain access to the data digitally or physical theft of the data storage media. To prevent this data from being accessed, modified or stolen, organizations will often employ security protection measures such as password protection, data encryption, or a combination of both. The security options used for this type of data are broadly referred to as data-at-rest protection (DARP).[10]

Definitions include:

"...all data in computer storage while excluding data that is traversing a network or temporarily residing in computer memory to be read or updated."[11]

"...all data in storage but excludes any data that frequently traverses the network or that which resides in temporary memory. Data at rest includes but is not limited to archived data, data which is not accessed or changed frequently, files stored on hard drives, USB thumb drives, files stored on backup tape and disks, and also files stored off-site or on a storage area network (SAN)."[12]

While it is generally accepted that archive data (i.e. which never changes), regardless of its storage medium, is data at rest and active data subject to constant or frequent change is data in use. “Inactive data” could be taken to mean data which may change, but infrequently. The imprecise nature of terms such as “constant” and “frequent” means that some stored data cannot be comprehensively defined as either data at rest or in use. These definitions could be taken to assume that Data at Rest is a superset of data in use; however, data in use, subject to frequent change, has distinct processing requirements from data at rest, whether completely static or subject to occasional change.

Security

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Because of its nature data at rest is of increasing concern to businesses, government agencies and other institutions.[11] Mobile devices are often subject to specific security protocols to protect data at rest from unauthorized access when lost or stolen[13] and there is an increasing recognition that database management systems and file servers should also be considered as at risk;[14] the longer data is left unused in storage, the more likely it might be retrieved by unauthorized individuals outside the network.

Data encryption, which prevents data visibility in the event of its unauthorized access or theft, is commonly used to protect data in motion and increasingly promoted for protecting data at rest.[15] The encryption of data at rest should only include strong encryption methods such as AES or RSA. Encrypted data should remain encrypted when access controls such as usernames and password fail. Increasing encryption on multiple levels is recommended. Cryptography can be implemented on the database housing the data and on the physical storage where the databases are stored. Data encryption keys should be updated on a regular basis. Encryption keys should be stored separately from the data. Encryption also enables crypto-shredding at the end of the data or hardware lifecycle. Periodic auditing of sensitive data should be part of policy and should occur on scheduled occurrences. Finally, only store the minimum possible amount of sensitive data.[16]

Tokenization is a non-mathematical approach to protecting data at rest that replaces sensitive data with non-sensitive substitutes, referred to as tokens, which have no extrinsic or exploitable meaning or value. This process does not alter the type or length of data, which means it can be processed by legacy systems such as databases that may be sensitive to data length and type. Tokens require significantly less computational resources to process and less storage space in databases than traditionally encrypted data. This is achieved by keeping specific data fully or partially visible for processing and analytics while sensitive information is kept hidden. Lower processing and storage requirements makes tokenization an ideal method of securing data at rest in systems that manage large volumes of data.

A further method of preventing unwanted access to data at rest is the use of data federation[17] especially when data is distributed globally (e.g. in off-shore archives). An example of this would be a European organisation which stores its archived data off-site in the US. Under the terms of the USA PATRIOT Act[18] the American authorities can demand access to all data physically stored within its boundaries, even if it includes personal information on European citizens with no connections to the US. Data encryption alone cannot be used to prevent this as the authorities have the right to demand decrypted information. A data federation policy which retains personal citizen information with no foreign connections within its country of origin (separate from information which is either not personal or is relevant to off-shore authorities) is one option to address this concern. However, data stored in foreign countries can be accessed using legislation in the CLOUD Act.

Data in use

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Data in use is an information technology term referring to active data which is stored in a non-persistent digital state or volatile memory, typically in computer random-access memory (RAM), CPU caches, or CPU registers.[19]

Data in use has also been taken to mean “active data” in the context of being in a database or being manipulated by an application. For example, some enterprise encryption gateway solutions for the cloud claim to encrypt data at rest, data in transit and data in use.[20]

Some cloud software as a service (SaaS) providers refer to data in use as any data currently being processed by applications, as the CPU and memory are utilized.[21]

Security

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Because of its nature, data in use is of increasing concern to businesses, government agencies and other institutions. Data in use, or memory, can contain sensitive data including digital certificates, encryption keys, intellectual property (software algorithms, design data), and personally identifiable information. Compromising data in use enables access to encrypted data at rest and data in motion. For example, someone with access to random access memory can parse that memory to locate the encryption key for data at rest. Once they have obtained that encryption key, they can decrypt encrypted data at rest. Threats to data in use can come in the form of cold boot attacks, malicious hardware devices, rootkits and bootkits.

Encryption, which prevents data visibility in the event of its unauthorized access or theft, is commonly used to protect Data in Motion and Data at Rest and increasingly recognized as an optimal method for protecting Data in Use. There have been multiple projects to encrypt memory. Microsoft Xbox systems are designed to provide memory encryption and the company PrivateCore presently has a commercial software product vCage to provide attestation along with full memory encryption for x86 servers.[22] Several papers have been published highlighting the availability of security-enhanced x86 and ARM commodity processors.[19][23] In that work, an ARM Cortex-A8 processor is used as the substrate on which a full memory encryption solution is built. Process segments (for example, stack, code or heap) can be encrypted individually or in composition. This work marks the first full memory encryption implementation on a mobile general-purpose commodity processor. The system provides both confidentiality and integrity protections of code and data which are encrypted everywhere outside the CPU boundary.

For x86 systems, AMD has a Secure Memory Encryption (SME) feature introduced in 2017 with Epyc.[24] Intel has promised to deliver its Total Memory Encryption (TME) feature in an upcoming CPU.[25][26]

Operating system kernel patches such as TRESOR and Loop-Amnesia modify the operating system so that CPU registers can be used to store encryption keys and avoid holding encryption keys in RAM. While this approach is not general purpose and does not protect all data in use, it does protect against cold boot attacks. Encryption keys are held inside the CPU rather than in RAM so that data at rest encryption keys are protected against attacks that might compromise encryption keys in memory.

Enclaves enable an “enclave” to be secured with encryption in RAM so that enclave data is encrypted while in RAM but available as clear text inside the CPU and CPU cache. Intel Corporation has introduced the concept of “enclaves” as part of its Software Guard Extensions. Intel revealed an architecture combining software and CPU hardware in technical papers published in 2013.[27]

Several cryptographic tools, including secure multi-party computation and homomorphic encryption, allow for the private computation of data on untrusted systems. Data in use could be operated upon while encrypted and never exposed to the system doing the processing.

Data in transit

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Data in transit, also referred to as data in motion[28] and data in flight,[29] is data en route between source and destination, typically on a computer network.

Data in transit can be separated into two categories: information that flows over the public or untrusted network such as the Internet and data that flows in the confines of a private network such as a corporate or enterprise local area network (LAN).[30]

Properties of digital information

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All digital information possesses common properties that distinguish it from analog data with respect to communications:

  • Synchronization: Since digital information is conveyed by the sequence in which symbols are ordered, all digital schemes have some method for determining the beginning of a sequence. In written or spoken human languages, synchronization is typically provided by pauses (spaces), capitalization, and punctuation. Machine communications typically use special synchronization sequences.
  • Language: All digital communications require a formal language, which in this context consists of all the information that the sender and receiver of the digital communication must both possess, in advance, for the communication to be successful. Languages are generally arbitrary and specify the meaning to be assigned to particular symbol sequences, the allowed range of values, methods to be used for synchronization, etc.
  • Errors: Disturbances (noise) in analog communications invariably introduce some, generally small deviation or error between the intended and actual communication. Disturbances in digital communication only result in errors when the disturbance is so large as to result in a symbol being misinterpreted as another symbol or disturbing the sequence of symbols. It is generally possible to have near-error-free digital communication. Further, techniques such as check codes may be used to detect errors and correct them through redundancy or re-transmission. Errors in digital communications can take the form of substitution errors, in which a symbol is replaced by another symbol, or insertion/deletion errors, in which an extra incorrect symbol is inserted into or deleted from a digital message. Uncorrected errors in digital communications have an unpredictable and generally large impact on the information content of the communication.
  • Copying: Because of the inevitable presence of noise, making many successive copies of an analog communication is infeasible because each generation increases the noise. Because digital communications are generally error-free, copies of copies can be made indefinitely.
  • Granularity: The digital representation of a continuously variable analog value typically involves a selection of the number of symbols to be assigned to that value. The number of symbols determines the precision or resolution of the resulting datum. The difference between the actual analog value and the digital representation is known as quantization error. For example, if the actual temperature is 23.234456544453 degrees, but only two digits (23) are assigned to this parameter in a particular digital representation, the quantizing error is 0.234456544453. This property of digital communication is known as granularity.
  • Compressible: According to Miller, "Uncompressed digital data is very large, and in its raw form, it would actually produce a larger signal (therefore be more difficult to transfer) than analog data. However, digital data can be compressed. Compression reduces the amount of bandwidth space needed to send information. Data can be compressed, sent, and then decompressed at the site of consumption. This makes it possible to send much more information and results in, for example, digital television signals offering more room on the airwave spectrum for more television channels."[5]

Historical digital systems

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Even though digital signals are generally associated with the binary electronic digital systems used in modern electronics and computing, digital systems are actually ancient, and need not be binary or electronic.

  • DNA genetic code is a naturally occurring form of digital data storage.
  • Written text (due to the limited character set and the use of discrete symbols – the alphabet in most cases)
  • The abacus was created sometime between 1000 BC and 500 BC, it later became a form of calculation frequency. Nowadays it can be used as a very advanced, yet basic digital calculator that uses beads on rows to represent numbers. Beads only have meaning in discrete up and down states, not in analog in-between states.
  • A beacon is perhaps the simplest non-electronic digital signal, with just two states (on and off). In particular, smoke signals are one of the oldest examples of a digital signal, where an analog "carrier" (smoke) is modulated with a blanket to generate a digital signal (puffs) that conveys information.
  • Morse code uses six digital states—dot, dash, intra-character gap (between each dot or dash), short gap (between each letter), medium gap (between words), and long gap (between sentences)—to send messages via a variety of potential carriers such as electricity or light, for example using an electrical telegraph or a flashing light.
  • The Braille uses a six-bit code rendered as dot patterns.
  • Flag semaphore uses rods or flags held in particular positions to send messages to the receiver watching them some distance away.
  • International maritime signal flags have distinctive markings that represent letters of the alphabet to allow ships to send messages to each other.
  • More recently invented, a modem modulates an analog "carrier" signal (such as sound) to encode binary electrical digital information, as a series of binary digital sound pulses. A slightly earlier, surprisingly reliable version of the same concept was to bundle a sequence of audio digital "signal" and "no signal" information (i.e. "sound" and "silence") on magnetic cassette tape for use with early home computers.

See also

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References

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Further reading

[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Digital data

View on Grokipedia
from Grokipedia
Digital data refers to represented in a discrete, binary format using bits— the smallest units of , each capable of holding a value of either 0 or 1— for storage, processing, and transmission within computing systems. This binary representation leverages the two-state nature of electronic switches in computers, where 0 typically denotes an "off" state and 1 an "on" state, enabling efficient encoding of complex . Bits are commonly grouped into bytes, consisting of 8 bits, which serve as the fundamental unit for manipulation and allow for 256 possible values (0 to 255) per byte. In digital systems, is stored in byte-addressable , where each byte has a unique , facilitating organized access and allocation across segments like static data, heap, and stack based on the data's lifecycle. Common types of digital include numeric (such as and real numbers), textual (encoded via standards like ASCII or ), logical (true/false values), visual (images represented by pixels in binary, grayscale, or color formats), and audio (sampled waveforms). These representations often employ fixed-length formats to balance precision and storage efficiency; for instance, an 8-bit unsigned ranges from 0 to 255, while signed variants use schemes like to include negative values. Digital data forms the backbone of modern and , encompassing raw values or sets of values that represent specific concepts, which become meaningful only upon and contextualization. In the digital age, it is generated in vast quantities—such as petabytes annually from projects like the —enabling global collaboration but posing challenges in accuracy verification, long-term preservation, and adaptation to evolving technologies. Techniques like compression (lossless for exact recovery or lossy for approximation) further optimize storage for data.

Fundamentals

Definition

Digital data refers to that is represented using discrete values, most commonly in the form of binary digits (bits) consisting of 0s and 1s, which allows for precise storage, , and transmission in electronic systems. This discrete nature enables digital data to be exactly replicated without degradation, distinguishing it from continuous representations and facilitating reliable manipulation through computational operations. In contrast to analog data, which varies continuously and is susceptible to signal degradation over time or distance—such as the grooves on a vinyl record wearing down with repeated playback, leading to reduced audio fidelity—digital data is quantized into finite states, preserving integrity during copying and transmission, as exemplified by (CD) audio where binary encoding ensures consistent quality across duplicates. The origins of digital data are rooted in electronic systems designed for efficient storage, , and transmission of , with Claude Shannon's 1948 formulation of establishing the bit as the fundamental unit of , quantifying uncertainty in communication channels without reference to meaning. The prevalence of digital data has grown dramatically; according to estimates by Hilbert and López, digital formats accounted for less than 1% of the world's total technological storage capacity in 1986 but expanded to 94% by 2007 due to exponential advances in digital technologies.

Representation

Digital data is fundamentally represented as sequences of bits, where each bit is a binary digit that can hold one of two values: or 1. In electronic systems, a bit value of typically corresponds to a level (near V, representing an "off" state), while 1 corresponds to a level (such as 3.3 V or 5 V, representing an "on" state). These bits form the basic building blocks, allowing complex information to be encoded through patterns of 0s and 1s. A byte, the most common grouping of bits, consists of 8 bits and can represent 256 distinct values (from 0 to 255 in ). For example, in the ASCII encoding scheme, the uppercase letter 'A' is represented as the byte 01000001 in binary. Higher-level abstractions build on bits for efficiency. A comprises 4 bits, capable of representing 16 values (0 to 15 in ), often used in notation where each maps to a single hex digit (0-9 or A-F). A word, which is machine-dependent, refers to the standard number of bits processed by a processor in a single operation; common sizes include 32 bits in 32-bit architectures and 64 bits in 64-bit systems. provides a compact way to denote , with each pair of hex digits representing a byte; for instance, the binary 11111111 (all 1s in a byte) is written as 0xFF. Various data types structure bits to represent specific kinds of . Integers can be unsigned, using all bits for magnitude to cover non-negative values (e.g., 0 to 2n-1 for n bits), or signed, reserving the most significant bit as a in representation to include negative values (e.g., -2n-1 to 2n-1-1). Floating-point numbers follow the standard, which defines formats like single-precision (32 bits: 1 , 8 exponent bits, 23 mantissa bits) and double-precision (64 bits: 1 , 11 exponent bits, 52 mantissa bits) to approximate real numbers via in binary. Text is encoded using standards like , which assigns unique code points (numbers) to characters from diverse writing systems, typically stored as sequences of bytes in encodings such as UTF-8. Images are represented as grids of , where each pixel's value captures color intensity; in RGB format, a pixel uses three 8-bit channels (0-255) for , , and components to form over 16 million colors. Storage capacity scales through hierarchical units starting from the bit. Common units include the byte (8 bits), (KB, approximately 103 bytes), (MB, 106 bytes), (GB, 109 bytes), terabyte (TB, 1012 bytes), and petabyte (PB, 1015 bytes), often using decimal prefixes for marketing while binary prefixes (e.g., kibibyte, 210 bytes) apply in technical contexts. The total bit capacity of a storage unit is calculated as: total bits=number of units×bits per unit\text{total bits} = \text{number of units} \times \text{bits per unit} For example, 1 TB (1012 bytes) equals 8 × 1012 bits, since each byte holds 8 bits.

Conversion

Analog-to-Digital

The process of analog-to-digital (A/D) conversion transforms continuous analog signals, such as those from sensors or audio sources, into discrete digital data suitable for computational processing and storage. This conversion is essential in digital systems, where analog signals representing real-world phenomena like sound waves or voltage variations must be discretized in both time and amplitude domains. The two primary stages are sampling, which captures the signal at regular intervals, and quantization, which maps continuous amplitude values to finite digital levels. These steps ensure faithful representation while introducing controlled approximations to enable digital handling. Sampling adheres to the Nyquist-Shannon sampling theorem, which stipulates that to accurately reconstruct a continuous signal without , the sampling fsf_s must be at least twice the highest component fmax⁡f_{\max} in the signal's bandwidth. Formally, this is expressed as: fs≥2×fmax⁡f_s \geq 2 \times f_{\max} For instance, in recording, compact discs use a sampling rate of 44.1 kHz to capture frequencies up to 22 kHz, encompassing the full human . Failure to meet this criterion results in distortion, as higher frequencies fold into lower ones during reconstruction. Quantization follows sampling by approximating each sample's to the nearest discrete level from a predefined set, introducing quantization as the difference between the actual and assigned values. In uniform quantization, the number of levels ; for example, 16-bit audio quantization provides levels, allowing fine-grained resolution over the signal's . This manifests as , with the () for a full-scale sinusoidal input given by: SNR=6.02n+1.76 dB\text{SNR} = 6.02n + 1.76 \, \text{dB} where n is the number of bits, establishing a theoretical limit on conversion fidelity. An analog-to-digital converter (ADC) typically comprises key components: a sample-and-hold circuit to capture and stabilize the input signal during conversion, a quantizer to map the held voltage to discrete levels, and an encoder to output the corresponding binary code. One common architecture is the successive approximation ADC (SAR ADC), which iteratively compares the input against a digitally controlled reference using a binary search algorithm, refining the digital output bit by bit over multiple clock cycles for balanced speed and power efficiency. Applications of A/D conversion span diverse fields, including audio digitization via (PCM), where sampled and quantized signals enable compact storage and transmission in formats like ; video processing through frame capture, discretizing pixel intensities at high rates for ; and sensor interfaces in (IoT) devices, converting environmental measurements like temperature or motion into digital form for remote monitoring. By 2025, precision ADCs in , such as smartphones, commonly process over 1 million samples per second to support features like high-resolution and real-time .

Symbol-to-Digital

Symbol-to-digital conversion transforms discrete, human-readable symbols—such as text characters, graphical icons, or visual patterns—into binary representations suitable for digital processing and storage. This process primarily relies on encoding schemes that map each symbol to a unique sequence of bits, enabling efficient transmission and manipulation by computers. For textual symbols, the American Standard Code for Information Interchange (ASCII) employs a fixed 7-bit code to represent 128 basic characters, including uppercase and lowercase letters, digits, and punctuation, providing a foundational standard for early digital text handling. Extending this capability, serves as the dominant encoding for , using variable-length byte sequences (1 to 4 bytes) to accommodate a broader array of international symbols while maintaining backward compatibility with ASCII. In imaging contexts, color symbols are digitized via the RGB model, where each 's hue is defined by three 8-bit integer values (0-255) for red, green, and blue components, yielding over 16 million possible colors per pixel in standard formats like . Input devices play a crucial role in capturing and converting these symbols through systematic mechanisms. Keyboards, for instance, utilize polling, in which the host computer repeatedly queries the keyboard's at regular intervals to detect key presses; upon detection, the device generates a scan code that is mapped to a binary character code like ASCII or UTF-8. Scanners, meanwhile, employ optical scanning to digitize printed symbols: a source illuminates the document, sensors capture reflected intensities as analog signals, and these are thresholded into binary values representing black or white modules. These mechanisms ensure discrete symbols are systematically polled or scanned into digital form without loss of discrete identity. Various encoding schemes optimize this conversion for efficiency and capacity. , introduced by in 1952, exemplifies variable-length encoding by assigning shorter binary codes to more frequent symbols and longer ones to rarer ones, minimizing overall bit usage in data streams while ensuring prefix-free decoding for lossless reconstruction. An early precursor to such binary-like systems is , developed in the 1830s, which maps alphabetic symbols to sequences of dots (short signals) and dashes (long signals) separated by spaces, effectively using two states to encode messages over telegraph lines. In contemporary applications, QR codes illustrate advanced symbol encoding by arranging data into a grid of black and white squares; they support four modes—numeric, alphanumeric, byte/binary, and —to encode up to 7,089 numeric characters or equivalent in other modes per symbol, facilitating quick digital readout via scanners. The proliferation of symbol-to-digital conversion has driven the near-total dominance of digital storage, with global data volumes projected to reach 181 zettabytes by 2025, overwhelmingly in binary formats as analog media fades. In digital cameras, this process is evident through (CCD) sensors, where incoming photons generate electron charges proportional to light intensity at each photosite; these charges are serially shifted and converted to binary pixel values via an on-chip , forming the . A key challenge in this domain is the ongoing evolution of character sets to handle global linguistic diversity; starting from ASCII's limited 128 symbols, adopted a 21-bit architecture in 1996, theoretically supporting 1,114,112 code points, with 159,801 characters encoded by 17.0 in 2025 to encompass scripts from 150+ languages.

States

Binary States

Digital data fundamentally relies on binary states, which represent the two discrete values of 0 and 1. Logically, these states correspond to false and true, or off and on, forming the basis of in . Physically, they are implemented through distinguishable electrical or material properties that can be reliably detected and switched. In electronic circuits, binary states are typically encoded using voltage levels. For instance, in Transistor-Transistor Logic (TTL) systems operating at 5V, a low state (0) is defined as 0 to 0.8 V, while a high state (1) ranges from 2 V to 5 V, with undefined regions in between to provide noise margins. These thresholds ensure robust signal interpretation despite variations in manufacturing or environmental conditions. Similar conventions apply in other logic families, such as , but TTL remains a standard reference for many digital interfaces. Binary states are stored in various media by exploiting physical properties that can hold one of two stable configurations. In , such as hard disk drives, data is encoded in the orientation of magnetic domains on a thin ferromagnetic layer; one direction represents 0, and the opposite represents 1, with read heads detecting these via changes in . Optical media, like CDs, use microscopic pits and lands on a reflective surface: pits scatter light to indicate one state (often 0), while lands reflect it for the other (1), though actual bit encoding relies on transitions between them for reliable detection. In , NAND flash memory employs floating-gate transistors where the presence or absence of trapped electrons in the gate alters the transistor's , distinguishing charged (0) from uncharged (1) states that persist without power. Switching between binary states enables computation through logic gates, which perform basic Boolean operations on inputs. The AND gate outputs 1 only if all inputs are 1; the OR gate outputs 1 if any input is 1; and the NOT gate inverts the input (0 to 1 or vice versa). These are realized using transistors as switches: in a MOSFET, a low gate voltage keeps it off (cut-off, representing 0), blocking current, while a high voltage turns it on (saturation, representing 1), allowing current flow. Combinations of such transistor switches form the gates, underpinning all digital logic circuits. Reliability of binary states is critical, as errors can corrupt . Modern achieves correctable bit rates on the order of 10−1110^{-11} per bit per hour, correcting single-bit flips through redundant coding. However, classical systems face fundamental limits from , such as in transport, which imposes a minimum probability scaling with signal bandwidth and , preventing perfect in high-speed operations.

Data Lifecycle States

Digital data progresses through distinct lifecycle states—at rest, in transit, and in use—each requiring tailored measures to mitigate risks associated with storage, transmission, and processing. These states highlight the dynamic nature of , where vulnerabilities can arise from unauthorized access, , or manipulation, emphasizing the need for layered protections aligned with established frameworks. Data at rest encompasses digital information stored on persistent media without active access or movement, such as in or filesystems. This state is particularly susceptible to threats like physical theft of storage devices or unauthorized internal access. Secure storage practices include full-disk encryption using the (AES) with 256-bit keys (AES-256), a symmetric endorsed by the National Institute of Standards and Technology (NIST) for protecting sensitive data in long-term storage. AES-256 operates on 128-bit blocks and is widely implemented in systems like encrypted hard drives and to prevent data exposure if the medium is compromised. Data in transit refers to digital data actively transferred across networks or between systems, exposing it to interception during communication. Common protocols facilitating this include the Transmission Control Protocol/Internet Protocol (TCP/IP) suite, which handles reliable data delivery over the , and , which layers (TLS) encryption atop HTTP to safeguard against . A key vulnerability in this state is the , where an adversary positions themselves between sender and receiver to capture or alter data streams. To counter such risks, and certificate validation are essential, ensuring and during movement. Data in use describes digital data being actively processed or accessed within active memory, such as volatile (RAM), where it is temporarily loaded for or . This state is vulnerable to memory scraping or attacks during runtime operations. Protection relies on mechanisms like (RBAC), which assigns permissions based on predefined user roles within an organization, limiting exposure to only authorized personnel and processes. RBAC integrates with operating systems and applications to enforce least-privilege principles, reducing the during data manipulation. Overarching security for these states is framed by the CIA triad—, , and —which provides a foundational model for protection. prevents unauthorized disclosure through techniques like AES-256; ensures accuracy and unaltered state via cryptographic hashing functions such as SHA-256, a 256-bit secure hash algorithm that produces a unique digest for verifying changes. maintains accessible through redundancy, such as configurations or distributed storage, guarding against denial-of-service disruptions. Recent analyses underscore the heightened risks to data in transit and in use compared to static storage. Digital in these lifecycle states relies on binary representation (0s and 1s) for underlying storage and processing, as outlined in the Binary States section.

Properties

Core Properties

Digital data possesses several inherent characteristics that define its nature and utility in computational systems. These core properties—exact reproducibility, , , and structured with —enable reliable storage, transmission, and processing, distinguishing digital representations from continuous analogs. One fundamental property is exact reproducibility, which allows digital data to be copied perfectly without degradation or loss of fidelity. Unlike analog signals, where accumulates during each duplication—leading to progressive —digital data consists of discrete binary states that can be replicated identically using simple bitwise operations. This ensures that multiple copies remain indistinguishable from the original, supporting applications like archival storage and where consistency is paramount. Granularity refers to the discrete, hierarchical structure of digital data, organized into manipulable units ranging from the smallest to larger aggregates like , files, and datasets. A , the atomic unit, represents a single binary value (0 or 1) and serves as the foundation for all higher-level structures; for instance, eight form a , which can encode characters or instructions, while files group these into named collections with metadata for organization. This layered discreteness facilitates precise operations, such as selective editing at the level or bulk transfer at the file level, without affecting unrelated portions. Determinism in digital data processing ensures that identical inputs always produce identical outputs, governed by predictable rules like . In digital circuits, operations rely on logic gates that implement Boolean functions—such as AND, OR, and NOT—yielding outputs solely dependent on input values, independent of timing variations or implementation details. This predictability underpins the reliability of algorithms and hardware, allowing engineers to verify system behavior through formal analysis and . Digital data functions as structured symbols interpretable by machines through defined syntax and synchronization mechanisms. Formats like use schemas to enforce rules on data organization, such as key-value pairs and nested objects, enabling parsers to validate and information consistently across systems. is achieved via headers in data packets, which include metadata for alignment (e.g., sequence numbers or timestamps), and embedded clocks or encoding schemes that maintain timing, ensuring receivers correctly interpret symbol sequences without drift.

Operational Properties

Operational properties of digital data encompass techniques for managing errors, optimizing storage and transmission efficiency, and ensuring security during handling. These operations are essential for reliable data processing in computing and communication systems, allowing digital data to be manipulated without loss of integrity or excessive resource consumption. Error detection and correction mechanisms are fundamental to maintaining data accuracy during storage and transmission. Parity bits provide a simple method for single-bit error detection by appending a check bit that ensures the total number of 1s in a data word is even or odd; for even parity, the parity bit is the XOR of all data bits, enabling detection of any odd number of errors but not correction. More robust error correction uses Hamming codes, such as the (7,4) code, which employs 3 parity bits to protect 4 data bits in a 7-bit codeword, achieving a minimum of 3 to correct single-bit errors and detect double-bit errors; the from parity checks identifies the erroneous bit position. Cyclic redundancy checks (CRC) offer efficient detection of burst errors through division in : the message is treated as a multiplied by xkx^k (where kk is the degree of the generator ), then divided by the generator G(x)G(x), and the CRC is the of degree less than kk, appended to the message for transmission; at the receiver, division yielding zero confirms . Data compression reduces storage and transmission requirements by exploiting redundancies. preserves all original data, achieving ratios of 2:1 to 4:1 for text and similar structured data through methods like Lempel-Ziv-Welch (LZW) in ZIP archives, which builds a of repeated phrases to encode them with shorter codes based on entropy reduction. , suitable for perceptual media, discards less noticeable information, enabling higher ratios such as 100:1 for video by prioritizing human visual fidelity, as in for images where coefficients are quantized to remove high-frequency details below perceptual thresholds. Transmission of digital over channels is constrained by physical limits and requires modulation to encode bits onto carrier signals. The Shannon capacity defines the maximum error-free rate CC as C=Blog⁡2(1+SNR)C = B \log_2(1 + \mathrm{SNR}), where BB is the bandwidth in Hz and SNR is the , establishing a theoretical upper bound on channel throughput without deriving the proof here. Common modulation schemes include (ASK), which varies carrier to represent binary states (e.g., presence for 1, absence for 0), and (FSK), which shifts the carrier frequency between two values for each bit, providing robustness against noise at the cost of wider bandwidth. Security operations on digital data, particularly hashing, ensure integrity by producing fixed-size digests that detect tampering. The algorithm, once widely used, became vulnerable to collision attacks after 2005, with practical exploits demonstrated by 2008 allowing forged data with identical hashes, prompting deprecation for integrity checks. As of 2025, both the family (e.g., SHA-256) and , standardized in 2015 as a sponge-based construction resistant to known attacks, are approved by NIST for integrity verification in protocols and systems, with SHA-256 remaining widely used; offers variants like SHA3-256 for 256-bit outputs.

History

Early Systems

The origins of digital data systems can be traced to ancient mechanical precursors that employed discrete, symbolic representations of information. Mechanical devices further exemplified early discrete data manipulation. The , originating around 2400 BCE in , used movable beads on rods to represent numerical values in a positional system, typically base-10, facilitating arithmetic through positional representation and serving as a precursor to computational tools. Binary arithmetic itself was formalized centuries later by in 1703, who in his treatise Explication de l'Arithmétique Binaire outlined addition, subtraction, and multiplication using only the digits 0 and 1, providing a rigorous mathematical foundation for systems reliant on two-state logic. This work emphasized binary's simplicity and universality, influencing subsequent digital developments. Theoretical advancements complemented these inventions, with Alan Turing's 1936 paper on computable numbers establishing foundational concepts for digital computation, and John von Neumann's 1945 report defining the stored-program architecture crucial for processing digital data. The saw the emergence of engineered systems that explicitly digitized information for communication and automation. In 1801, invented the Jacquard loom, which employed punched cards—perforated with holes to indicate presence (1) or absence (0)—to control the weaving of complex textile patterns, marking an early use of binary-encoded instructions for mechanical control. Building on this, designed the in 1837, a proposed general-purpose that would use punched cards for both inputting data and programming operations, allowing conditional branching and looping in a manner foreshadowing modern computing. Concurrently, Samuel F. B. Morse patented the electric telegraph in 1837, incorporating , a system of short dots and long dashes transmittable as electrical pulses, which paralleled binary signaling by distinguishing two distinct states for encoding alphabetic and numeric characters. Theoretical advancements complemented these inventions, with publishing The Mathematical Analysis of Logic in 1847, introducing as a symbolic system for logical operations on binary variables (true/false or 1/0), including conjunction, disjunction, and negation, which became indispensable for digital circuit design. The transition to electronic precursors occurred in the late 1930s and early 1940s. completed the Z1 in 1938, the world's first programmable binary computer, a mechanical device using and punched film for instructions, though unreliable due to its moving parts; Zuse's collaborator Helmut Schreyer advocated for relays to enable electronic switching, influencing later iterations like the relay-based Z3 in 1941. The Colossus, operational from late 1943, represented the first large-scale programmable electronic digital computer, built by using approximately 1,600 to 2,400 for high-speed operations and cryptanalytic tasks at , though it lacked a stored-program architecture.

Modern Developments

The transistor era marked a pivotal shift in digital data handling, beginning with the invention of the in December 1947 by and Walter Brattain at Bell Laboratories, under the direction of , which enabled reliable amplification and switching of electronic signals essential for . This breakthrough was followed by the development of the junction in 1948, improving stability and for computational applications. The advent of the in 1958, independently conceived by at and realized in practice by at , allowed multiple s, resistors, and capacitors to be fabricated on a single chip, dramatically increasing data processing density and efficiency. In 1965, Intel co-founder observed in his seminal paper that the number of transistors on an would roughly double every year, a prediction revised to every two years in 1975, driving in digital data manipulation capabilities. This "Moore's Law" held through advances in and materials until approximately 2025, when physical limits in scaling led to a plateau, shifting focus to architectural innovations like 3D stacking for continued performance gains. The digital revolution accelerated with the establishment of in 1969 by the U.S. Department of Defense's Advanced Research Projects Agency (), which implemented packet-switching to enable reliable data transmission across distributed networks, laying the groundwork for interconnected digital systems. By the , the transition to the commercial internet transformed digital data accessibility, with the National Science Foundation's decommissioning of its in 1995 allowing private sector expansion, spurred by the release of the protocols in 1993 and the growth of Internet Service Providers offering public access. This era also witnessed explosive growth in , evolving from the 305 RAMAC's 3.75 megabytes on 50 platters in 1956 to solid-state drives reaching capacities of up to 122.88 terabytes as of 2025, enabling the storage and retrieval of vast digital archives at unprecedented speeds and densities. Advancements in frameworks and further revolutionized digital data management starting in the mid-2000s, with the release of in 2006 providing an open-source distributed storage and processing system capable of handling petabyte-scale datasets across clusters of commodity hardware. This was complemented by the launch of the dataset in 2010, which curated over 14 million annotated images across 21,000 categories, serving as a foundational resource for training models in visual recognition tasks. By 2025, global creation had reached approximately 181 zettabytes annually, driven by IoT devices, social media, and cloud services, according to IDC forecasts. AI training processes now routinely operate on petabyte-scale datasets, with large language models requiring distributed storage systems to process and fine-tune massive corpora for improved accuracy and generalization. Emerging technologies are pushing digital data beyond classical binary limits, exemplified by quantum bits (qubits) that leverage superposition and entanglement to represent multiple states simultaneously, as outlined in IBM's 2025 roadmap, featuring the Nighthawk processor with 120 qubits to advance error-corrected computations. These systems, such as the 156-qubit Heron processor integrated into modular architectures, enable exploratory applications in optimization and simulation that classical computers struggle with due to exponential complexity. Parallelly, DNA-based storage offers ultra-high density, with Microsoft Research prototypes potentially storing up to 215 petabytes per gram of synthetic DNA, far surpassing electronic media in longevity and compactness for archival purposes.

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