Time Machine

Data entry keyers

Name: Data Entry Keyers: employment and wages since 1890
Creator: Future History
License: https://futurehistory.earth/terms

Also called Data entry clerk, Data entry operator, Data entry specialist, Keying operator, Document entry clerk

In brief

In May 2025, BLS counted 127,080 US data entry keyers, with median pay of $41,340 a year. Of the BLS counts since 2003, the highest was 339,010, in May 2003. The earliest figure here is an estimate of 200 from 1890, not measured the same way as today's count. The last full technology era for data entry keyers: OCR and forms-processing software, 2000 to 2020. Employment fell between 2004 and 2010.

What the sources say

Official outlook

BLS projects employment to decline 25.5% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

50% of this job's tasks are exposed to large language models. Not a forecast of job losses. Eloundou and others, 2023

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend. In May 2021 BLS changed how it estimates these counts (a model-based method using three years of survey data) and moved fully to the 2018 occupation codes, so part of a change across 2021 can come from the method rather than the work.

Last updated 11 October 2026.

Cite this page

APA

Future History. (2026, October 11). Data entry keyers: jobs and pay since 1890 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/43-9021

Chicago

Future History. "Data entry keyers: jobs and pay since 1890." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/43-9021.

BibTeX

@misc{futurehistory_43_9021, author = {{Future History}}, title = {Data entry keyers: jobs and pay since 1890}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/43-9021}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time

19001925195019752000now

Country

2026

Known then as Tabulating Machine Operator / Card Punch Operator (Hollerith-era census and insurance work)

Known then as Keypunch Operator (IBM card-punch era, clerical labor pools)

Known then as Data Entry Operator / Keying Operator (VDT and minicomputer transition)

Known then as Data Entry Keyer / Data Entry Clerk (PC-network era)

Known then as Data Entry Keyers (BLS SOC 43-9021)

Known today as Data Entry Keyers (BLS SOC 43-9021)

Projected to still be called Data Entry Keyers (BLS SOC 43-9021)

Data from the May 2025 survey

US Employment

127K

Est.

Stated in source: BLS OEWS

Official statistic

Report an error

Median Annual Wage

$41,340

Est.

≈ $40,280 in 2024 prices (derived by us, CPI-adjusted)

Official statistic

Report an error

Beat · 1890

Herman Hollerith's tabulating system, awarded the Census Bureau contract for the 1890 census, introduces the first large-scale deployment of punched-card data operators in the United States. Clerks at the Census Bureau use pantograph punches to transcribe handwritten census schedules onto paper cards at a rate of roughly 500 cards per day each. The 60 million cards representing the U.S. population of 62 million are processed in six months, saving an estimated $5 million over manual tabulation and completing the census two years faster than the 1880 count.

Beat · 1924

IBM is formed through the merger of the Tabulating Machine Company (Hollerith's company, absorbed by CTR in 1911), the Computing Scale Company, and the International Time Recording Company. IBM would go on to manufacture the standard keypunch machines (the 024, 026, and later the 029) that defined the physical tool of the keypunch operator profession for the next fifty years. The IBM 029 Card Punch, introduced in 1964, would be the last major IBM keypunch model before VDTs made punched cards obsolete.

Beat · 1949

IBM introduces the 026 Printing Card Punch. Unlike earlier models, the 026 prints the character being punched along the top edge of the card, giving operators a human-readable verification of the machine-readable holes. A program drum allows automatic column-skipping and duplication for specific form layouts. Expert operators can punch 200 cards per hour. The 026 becomes the dominant tool in corporate data-processing departments and defines the working conditions of keypunch operators throughout the 1950s and 1960s: seated, repetitive, high-accuracy keyboard work in large open-plan "keypunch pools."

Beat · 1964

IBM introduces the 029 Card Punch, the successor to the 026. The 029 adds an improved printing mechanism and a more flexible program-card system, but its fundamental operation is unchanged from the 026. By 1964, the punched-card keypunch operator is one of the most common clerical occupations in large American organizations: every major corporation, government agency, insurance company, and hospital operates a keypunch department. The occupation is overwhelmingly female: sociologists document that employers explicitly preferred women for keypunch work, citing "patience" and "dexterity," and paid women significantly below the clerical male median.

Beat · 1971

IBM introduces the 3270 Information Display System, a family of video display terminals and controllers for IBM mainframes. The 3270 terminal lets an operator see what they are typing on a screen, receive application-level validation feedback, and correct errors before submitting a record, fundamentally changing the data-entry workflow from batch (cards submitted and errors found later) to interactive (errors caught immediately). The 3270 family becomes the standard for online data entry in banking, insurance, health care, and government through the 1970s and 1980s, and the transition from "keypunch operator" to "data entry operator" follows the transition from card punches to VDTs.

Beat · 1996

The U.S. Health Insurance Portability and Accountability Act (HIPAA) is signed into law. HIPAA's subsequent privacy and security rules, which took effect in 2003, create significant compliance requirements around the handling of protected health information (PHI), including data entered into electronic health record systems. The regulation effectively establishes a floor of human accountability for medical data entry that automation alone cannot easily satisfy: a named, trained individual must be responsible for the accuracy of PHI records, and that accountability requirement has preserved demand for human data entry keyers in health care even as other sectors automated more aggressively.

Beat · 2014

Employment of data entry keyers reaches an approximate peak of 375,267, according to DataUSA analysis of BLS data. The peak comes despite a decade of OCR adoption and growing availability of forms-processing software, suggesting that the digitization backlog from healthcare records migration (driven by the HITECH Act of 2009 and the Affordable Care Act's EHR incentives) and financial-sector compliance work created enough demand to offset automation-driven displacement through the early 2010s. From this peak, employment will fall by 62% over the next decade.

Beat · 2025

The WEF Future of Jobs Report 2025 lists data entry clerks as the third-fastest-shrinking occupational category globally through 2030, projecting a 34% decline behind only postal service clerks (-40%) and bank tellers (-35%). The WEF survey of over 1,000 employers identifies AI and automation as the primary drivers. Separately, BLS projects the U.S. occupation specifically to decline 25.9% from 141,600 in 2024 to 104,900 by 2034. The gap between the WEF figure (faster) and BLS (slower) reflects the difference between employer expectations about AI adoption speed and BLS's more conservative productivity-model methodology.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

How it began · 1890

In the summer of 1890, inside a converted building near the U.S. Census Bureau in Washington, D.C., a small workforce of clerks sat at Hollerith pantograph punches and pressed steel pins into paper cards, one person-record at a time, transcribing the handwritten returns of the tenth federal census. Herman Hollerith had contracted with the Census Bureau to process sixty million cards using the electrical tabulating system he had invented after watching a train conductor punch passenger tickets. Each clerk could prepare roughly 500 cards per day; an experienced tabulator operator could then sort 80 punched cards per minute through the counting machine. The entire census was completed in six months and under budget, compared with seven years for the 1880 count done by hand. What Hollerith had created was not merely a faster counting machine: he had created a new kind of clerical worker whose job was to translate human facts into machine-readable form. His Tabulating Machine Company was absorbed into what became IBM in 1924, and the punched card remained the dominant data-entry medium for the next sixty years. By the 1940s, large corporations, government agencies, and insurance companies employed whole floors of keypunch operators, overwhelmingly women, working in pools of a hundred or more at noisy card-punch machines. The IBM 026 Printing Card Punch, introduced in 1949, became the signature tool of the occupation: a device about the size of a large typewriter, with a special keyboard layout, a program drum that could auto-skip columns for common form layouts, and a printing head that could emboss the punched characters along the top of the card as a human-readable check. A skilled operator on a 026 could punch 200 cards per hour. The occupation grew with the computing industry throughout the 1950s and 1960s, peaked in the early 1970s as punch cards began giving way to video display terminals, reinvented itself once more as the personal computer moved data entry into every office, and now faces its most fundamental challenge yet: the tools that were always supposed to make the job unnecessary have finally become capable of doing so.

Cited forecasts · present → 2035

Will AI replace data entry keyers? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say

Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.

BLS Employment Projections 2025-35

→ 2035

-25.5%

BLS National Employment Matrix, 2025-35 cycle, line for SOC 43-9021 (Data entry keyers): 131.8 thousand jobs in 2025, projected 98.2 thousand in 2035, a change of -33.6 thousand (-25.5%). Matrix employment counts all jobs in the occupation, including self-employed workers (1.6% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.

Official statisticReport an error

How exposed is this work to AI (research measures)

Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.

Eloundou et al. (2023), "GPTs are GPTs"

2023 study · Share of tasks exposed to LLMs (β, human-rated)

50%

of tasks

What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 43-9021.00 Data Entry Keyers. Shown: human-rated β = 0.5, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.214 and γ (all LLM-assisted) 0.786; GPT-4-rated β 0.893. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error

Today, in this role

What's changing day to day for data entry keyers

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereReview batches of invoices, purchase orders, or medical records in an IDP platform (such as Rossum or Nanonets) and adjudicate low-confidence AI extractions before routing to downstream systems.

Review batches of invoices, purchase orders, or medical records in an IDP platform (such as Rossum or Nanonets) and adjudicate low-confidence AI extractions before routing to downstream systems.^[6],[7]

Tools picking this up

Rossum (Order Management) ↗Nanonets ↗

Where your edge is

Build proficiency in at least one IDP review interface; learn to tune extraction confidence thresholds and flag systematic misreads to the vendor support queue.

AI is sitting alongside you hereVerify extracted records against source documents using cross-field validation rules (e.g., totals reconciliation, date-range checks, required-field completeness) before data is committed to an ERP or CRM system.

Verify extracted records against source documents using cross-field validation rules (e.g., totals reconciliation, date-range checks, required-field completeness) before data is committed to an ERP or CRM system.^[8],[4]

Tools picking this up

Hyperscience ↗UiPath Document Understanding ↗

Where your edge is

Learn SQL or spreadsheet pivot-table techniques to run exception reports that surface systematic extraction errors across a batch, shifting from per-record checking to population-level QA.

AI is sitting alongside you hereManually key data from handwritten forms, legacy paper documents, or non-standard source materials that OCR and AI extraction tools cannot process reliably.

Manually key data from handwritten forms, legacy paper documents, or non-standard source materials that OCR and AI extraction tools cannot process reliably.^[1],[4]

Where your edge is

Specialize in high-stakes document categories (clinical handwritten notes, court filings, non-Latin scripts) where human transcription remains necessary and carries legal accountability.

Where data entry keyers go next

Natural next steps for someone with your foundation: not exits, evolutions.

Computer User Support Specialists
US median pay
$61,860 (May 2025)
BLS outlook 2025-35
-3.5%
Customer Service Representatives
US median pay
$44,770 (May 2025)
BLS outlook 2025-35
-5.3%
Insurance Claims and Policy Processing Clerks
US median pay
$49,230 (May 2025)
BLS outlook 2025-35
-1.9%

What each move takes

A direction you could grow

Computer User Support Specialists

Data entry keyers who participate in IDP or RPA automation projects accumulate working knowledge of software configuration, exception triage, and user training that maps well onto a support specialist role. The pivot requires broadening technical troubleshooting skills but the software fluency is already present.

What you'd add

· CompTIA A+ or equivalent hardware/OS fundamentals
· Help-desk ticketing systems (Jira Service Management, Freshdesk)
· RPA tool basics (UiPath Community Edition free training)
· IT service management (ITIL foundations)

What it takesSome new skills to pick up

Tools of the era

How has the work of data entry keyers changed since 1890?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

Hollerith pantograph punch + tabulating machine (census and insurance era)
Hollerith's pantograph punch required an operator to align a census schedule beside the punch, read each item, and press the appropriate hole position using a hand-held stylus guided through a template. The punch produced no printed output on the card: the operator had to verify work by re-reading the card positions visually. Tabulating machines downstream read the holes electrically and incremented mechanical counters. The entire system was designed for batch statistical counting, not for individual record lookup, and the operator's job was purely transcription: copy the paper into the machine-readable medium as accurately as possible.
Effect on the work
For the 1890 census, Hollerith's machines and their operators completed the population count in six months, compared to seven years for the 1880 census done by hand. This demonstrated conclusively that a modest number of trained operators with machines could outperform a large manual clerical force.
IBM 026 Printing Card Punch (and Remington Rand / UNIVAC keypunch era)
The IBM 026 Printing Card Punch, introduced in 1949 and widely deployed through the 1950s and 1960s, became the defining tool of the keypunch operator profession. Unlike earlier punches, the 026 printed the character it punched along the top edge of the card, giving operators immediate visual feedback and making verification easier. A program drum could be loaded with a customized column-skip-and-duplicate routine for specific form layouts, allowing an expert operator to process hundreds of cards per hour. Employers set up keypunch departments of dozens or hundreds of operators working in parallel, a factory-style arrangement that made data entry a mass-production clerical activity. Remington Rand's competing UNIVAC keypunches, designed for their own systems, added magnetic tape output capability as early as the mid-1950s via the UNITYPER, enabling direct-to-tape entry that bypassed the card entirely in high-volume environments.
Effect on the work
By the mid-1960s, keypunch operator was among the fastest-growing occupational categories in BLS clerical surveys. The occupation grew from a handful of Census Bureau workers in 1890 to an estimated 75,000 in 1950 and to roughly 291,000 by 1980, driven entirely by the spread of batch data-processing into banking, insurance, retail, government, and health care.
Video display terminals (VDTs) and online data entry (replacing punched cards)
The punched card had a fundamental workflow problem: operators created cards in a batch, the cards were carried to a card reader, the reader submitted them to a mainframe, and errors were discovered only after the job ran. Video display terminals changed this. An operator typing at a VDT could see the data on screen, receive immediate validation feedback from the application, and correct errors in real time. IBM's terminal family (1971) and the proliferation of minicomputer-based systems through the 1970s drove a rapid shift away from card-based batch entry. By the early 1980s, most large data-processing installations had moved to online VDT entry; the keypunch machine was becoming a legacy device. The occupational title was slowly changing from "keypunch operator" to "data entry operator," reflecting the shift from a specific machine to a generic task.
Effect on the work
The VDT transition did not reduce employment in the short term: if anything, the faster throughput and broader application of online systems expanded demand for data entry operators through the 1970s and into the early 1980s. Employment peaked in the first half of the 1980s and then began a structural decline as personal computers let originating departments enter their own data directly, bypassing the dedicated data-entry pool.
Personal computers and networked business applications (PC-era data entry)
The IBM PC (1981) and its successors did not immediately eliminate data entry jobs, but they restructured who did the work and why a dedicated specialist was still needed. By the late 1980s, professional and managerial workers were entering their own correspondence and spreadsheets on PCs. What remained for dedicated data entry keyers was the volume work: batch-processing incoming forms, scanning paper records into database systems, and transferring data between incompatible systems where no automated bridge existed. Networked PC applications through the 1990s created enormous new demand for this kind of bridging work: health care, financial services, and government agencies accumulated mountains of paper records that needed to migrate into digital systems, and the data entry workforce absorbed much of that backlog. The 1990s thus saw a second wave of employment growth, as the digitization backlog from decades of paper-based record-keeping created new demand even as the front-end data creation shifted to originating departments.
Effect on the work
Employment of data entry keyers grew through the 1990s into the early 2000s, as the digitization of healthcare records, legal documents, financial forms, and government records created a sustained demand for manual transcription of legacy paper into digital systems. This was the "digitization backlog" wave: not new data creation, but migration of old paper into new systems.
OCR and forms-processing software (first wave of systematic automation)
Optical character recognition technology had existed since the 1950s but only became accurate and affordable enough for large-scale commercial deployment in the late 1990s and 2000s. Human data entry keyers were increasingly repositioned as quality-control reviewers who corrected the minority of fields that OCR got wrong, rather than entering the full document from scratch. Barcode scanning at retail and logistics eliminated data entry from those sectors. The combination of these factors drove employment between 2004 and 2010, a drop in six years.
Effect on the work
Employment fell between 2004 and 2010. OCR, barcode scanning, and forms-processing software eliminated roughly a third of the job population in six years. The remaining workforce concentrated in sectors with high proportions of handwritten, non-standard, or regulated documents where OCR accuracy remained insufficient.
Intelligent document processing and AI extraction (Rossum, Hyperscience, UiPath)
Intelligent document processing (IDP) platforms, which combine deep-learning OCR with field-classification models and structured extraction pipelines, represent a qualitative leap beyond first-generation OCR. Where earlier OCR read printed text character by character, IDP platforms from vendors including Rossum (2017), Hyperscience (founded 2014), Nanonets (2017), and UiPath Document Understanding (2019) learn to locate and extract specific fields from documents they have never seen before, handle semi-structured layouts, and route low-confidence extractions to a human review queue rather than silently accepting errors. The data entry keyer's role in this model is not pure transcription but adjudication: reviewing, correcting, and approving the fields that the machine flagged as uncertain. Generative AI tools further extended the automation frontier in 2023-2024 by enabling free-form extraction from unstructured documents and multi-step workflow automation that connects extraction directly to downstream ERP and CRM systems.
Effect on the work
Employment fell between 2014 and 2024. The IDP and AI extraction wave is projected to eliminate a sizeable share of remaining positions by 2034, according to BLS projections. The WEF Future of Jobs Report 2025 lists data entry clerks as among the fastest-declining occupations globally, projecting a decline by 2030.

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Data Entry Keyers employment and pay by year

On record since1890

Latest US employment127,080 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error

Latest US median pay$41,340 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error

Outlook-25.5% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error

In other countries

The latest figure each country's statistics agency publishes for this job. Each uses its own occupation definition, survey basis and currency, so the numbers are not directly comparable with each other or with the US figures.

Canada (NOC 14111)
Employment, 2020
40,820, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021: number of persons with employment income in NOC 14111 Data entry clerks, Canada, reference year 2020. A recipients basis (anyone with employment income during the year), slightly broader than a point-in-time employed count. Statistics Canada Open Licence.
Median pay, 2020
C$47,600, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021 (Table 98-10-0586): median employment income for NOC 14111 Data entry clerks, Canada, reference year 2020, full-year full-time workers (the basis comparable to the US OEWS full-time median). Statistics Canada Open Licence.

View all 27 years of cited US data, each linked to its source

Year	US employment	Annual pay (median unless marked)	Source	Report
1890	200	n/a	Est., U.S. Census Bureau History Researched context	Report
1950	75,000	n/a	Est., The Conversation Researched context	Report
1960	n/a	$3,800	Est., University of Missouri Libraries Researched context	Report
1980	291,000	n/a	Est., ERIC Researched context	Report
2003	339,010	$22,600	BLS OEWS May 2003 Official statistic	Report
2004	313,590	$23,250	BLS OEWS May 2004 Official statistic	Report
2005	296,700	$23,810	BLS OEWS May 2005 Official statistic	Report
2006	295,650	$24,690	BLS OEWS May 2006 Official statistic	Report
2007	286,540	$25,370	BLS OEWS May 2007 Official statistic	Report
2008	272,810	$26,120	BLS OEWS May 2008 Official statistic	Report
2009	243,550	$27,150	BLS OEWS May 2009 Official statistic	Report
2010	219,530	$27,450	BLS OEWS May 2010 Official statistic	Report
2011	211,200	$27,690	BLS OEWS May 2011 Official statistic	Report
2012	207,280	$28,010	BLS OEWS May 2012 Official statistic	Report
2013	207,660	$28,470	BLS OEWS May 2013 Official statistic	Report
2014	205,950	$28,870	BLS OEWS May 2014 Official statistic	Report
2015	199,240	$29,460	BLS OEWS May 2015 Official statistic	Report
2016	194,810	$30,100	BLS OEWS May 2016 Official statistic	Report
2017	180,100	$30,930	BLS OEWS May 2017 Official statistic	Report
2018	174,930	$32,170	BLS OEWS May 2018 Official statistic	Report
2019	159,930	$33,490	BLS OEWS May 2019 Official statistic	Report
2020	151,520	$34,440	BLS OEWS May 2020 Official statistic	Report
2021	147,170	$35,630	BLS OEWS May 2021 Official statistic	Report
2022	157,380	$36,190	BLS OEWS May 2022 Official statistic	Report
2023	154,230	$37,790	BLS OEWS May 2023 Official statistic	Report
2024	135,280	$39,850	BLS OEWS May 2024 Official statistic	Report
2025	127,080	$41,340	BLS OEWS May 2025 Official statistic	Report

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Where data entry keyers go next

Computer User Support Specialists

Hollerith pantograph punch + tabulating machine (census and insurance era)Hollerith pantograph punch + tabulating…1890–1944

IBM 026 Printing Card Punch (and Remington Rand / UNIVAC keypunch era)IBM 026 Printing Card Punch1944–1973

Video display terminals (VDTs) and online data entry (replacing punched cards)Video display terminals1973–1990

Personal computers and networked business applications (PC-era data entry)Personal computers1985–2005

OCR and forms-processing software (first wave of systematic automation)OCR2000–2020

Intelligent document processing and AI extraction (Rossum, Hyperscience, UiPath)Intelligent document processing2015–present

Data Entry Keyers employment and pay by year

In other countries

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