Data Entry Clerk
Snapshot
Data entry clerks update, maintain and retrieve information held on computer systems. They prepare source data for computer entry by compiling and sorting information, process customer and account source documents by reviewing data for deficiencies and verify entered customer and account data.
Data entry clerks are responsible for maintaining and updating information within computer systems. This role requires a keen eye for detail and accuracy, as you'll be handling sensitive data and ensuring its integrity. You’ll be preparing data for input, verifying information, and retrieving data as needed, contributing to efficient data management practices.
- • Inputting and updating data into computer systems and databases.
- • Reviewing source documents for accuracy and completeness, identifying and correcting errors.
- • Verifying data entered against source documents to ensure accuracy.
Where this occupation is in demand
Reported labour shortages and surpluses, by year. Published for occupation groups, not for individual job titles.
Figures cover General and keyboard clerks — 5 jobs including this one.
In shortage: Denmark, Greece, Italy.
Longest-running shortage: Italy, 3 years.
Select a place on the map to see its figures.
About this source›
Source: ELA/EURES labour shortages and surpluses. Readings are published at occupation-group level, and cover Europe. Editions differ in annex layout and country coverage, so a change between years does not always mean the labour market changed. Countries in grey were not reported, which is not the same as being in balance.
What these words mean
The four things this section reports
- Reported demand
- Whether employers report needing people in this job — a judgement published by a national or EU body, not a count.
- Where it is heading
- Which way employment in this job is expected to move over the coming years, from an official projection.
- Openings
- Roughly how many openings arise — from growth and from people leaving the job.
- Typical pay
- What people in this job typically earn where the source publishes it. Blank does not mean unpaid; it means nobody publishes it for that place.
A measure is left out when nobody publishes it for that place, rather than shown as zero.
Which way the market leans for you
- In your favour
- More openings than people looking — employers are competing for candidates.
- Balanced
- Openings and candidates are roughly matched.
- Competitive
- More people looking than openings — expect to compete.
- Mixed evidence
- Sources disagree, or the same occupation group is short in one part and oversupplied in another.
Every source resolves to one of these four, so there is a single vocabulary to learn. What differs is the evidence behind it, which is printed underneath each verdict — a measured ratio of openings to jobseekers, or an assessment published by a national body.
How this job compares with other jobs in the same country
- Strong
- Among the strongest in that country
- Good
- Stronger than most jobs in that country
- Mixed
- About typical for that country
- Weak
- Weaker than most jobs in that country
This is a rank within one country, not a score you can carry across borders — the registers behind two countries count different people, so the same number means different things in each. It is also why a job can be among the strongest in a country and still show as Competitive: it leads the field in a market that is crowded overall.
Where these come from
Every figure is published by a national statistics office, a public employment service or an EU body, and each card names its source and the period it covers. Some places are counted monthly, others assessed once or twice a year, so two places on the same map can be describing different moments — the date is always shown.
None of this predicts one person’s chances. It describes a market.
Reported demand · Typical pay · Where it is heading (United States, Ireland, Australia, Malta, United Kingdom)
| Reported demand | Typical pay | Where it is heading | |
|---|---|---|---|
| United States | Not reported | $41,340 2025-05 | −25.9% 2024–2034 · BLS 2024-2034 |
| Ireland | Not reported | — | −45.3% 2023–2035 · Cedefop 2026 * |
| Australia | Assessed as balanced Jobs and Skills Australia 2025 * | — | — |
| Malta | Shortage reported ELA/EURES 2024 * | — | −2.3% 2023–2035 · Cedefop 2026 * |
| United Kingdom | Not reported | £26,534 2025-provisional | — |
* Published for occupation groups, not for individual job titles.
U.S. Bureau of Labor Statistics (OEWS)
Source: Office for National Statistics, Annual Survey of Hours and Earnings, Open Government Licence v3.0
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Future Outlook for data entry clerk
data entry clerk is entering a period of transformation. With a 42% exposure to AI tools, this role is not being replaced, it is evolving. Mastery of new digital tools will be the key to staying ahead.
How are these scores calculated?
The Resilience Score (0–100) estimates how structurally protected this occupation is from automation and AI disruption, based on task-level analysis. Higher scores mean more human-judgment-intensive tasks. AI Exposure shows the estimated percentage of task hours that current AI capabilities could affect. These are model-derived structural indicators, not predictions about individual job security.
How could data entry clerk change as AI adoption grows?
Several task areas may shift toward AI-assisted workflows, so reskilling becomes more important.
Illustrative scenario based on task automatability — not a forecast. Values are rounded the further ahead you look.
Several task areas may shift toward AI-assisted workflows, so reskilling becomes more important.
Illustrative scenario based on task automatability — not a forecast. Values are rounded the further ahead you look.
Model-based estimate. Use it as a planning signal, not a guarantee.
Compare this to your profile
How AI may change this role
Deterministic, model-based interpretation of current role signals — not a guarantee of replacement.
What still depends on people
- apply information security policies
Where AI may become a co-pilot
- apply statistical analysis techniques
- use word processing software
Tasks most exposed to automation
- maintain data entry requirements
- process data
- perform data cleansing
Vital Signs & AI Vectors
AI Exposure Vectors
0-100%Exposure to AI-assisted analysis, pattern recognition, and predictive modelling tasks
Exposure to content generation, creative augmentation, and large language model tools
Exposure to physical automation, robotics, and sensor-driven task displacement
Exposure to workflow automation, decision-support software, and process digitisation
Technical Details
NexFuture v3.0 estimates automation exposure natively from ESCO essential-skill groups, weighted by skill mass and calibrated against expert anchors. Scores are probabilistic estimates, not guarantees. See the NexFuture Methodology White Paper for full details.
Measures automation exposure. It does not measure pay, demand, or how many jobs exist near you.
What people in this role usually do
Digital Technology
A typical day as a data entry clerk
09 09:00 · Morning apply information security policies
10 10:30 · Mid-morning maintain data entry requirements
12 12:00 · Midday apply statistical analysis techniques
14 14:00 · Afternoon perform data cleansing
15 15:30 · Late afternoon process data
17 17:00 · Wrap-up use word processing software
Task order is illustrative. Individual days vary.
What you need to do this work
The skills, knowledge and tools this role calls for — and the traits and rewards that come with it.
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maintain data entry requirements
Uphold conditions for data entry. Follow procedures and apply data program techniques.
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process data
Enter information into a data storage and data retrieval system via processes such as scanning, manual keying or electronic data transfer in order to process large amounts of data.
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apply statistical analysis techniques
Use models (descriptive or inferential statistics) and techniques (data mining or machine learning) for statistical analysis and ICT tools to analyse data, uncover correlations and forecast trends.
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perform data cleansing
Detect and correct corrupt records from data sets, ensure that the data become and remain structured according to guidelines.
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use word processing software
Use computer software applications for composition, editing, formatting, and printing of any sort of written material.
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apply information security policies
Implement policies, methods and regulations for data and information security in order to respect confidentiality, integrity and availability principles.
documentation types
The characteristics of internal and external documentation types aligned with the product life cycle and their specific content types.
query languages
The field of standardised computer languages for retrieval of information from a database and of documents containing the needed information.
resource description framework query language
The query languages such as SPARQL which are used to retrieve and manipulate data stored in Resource Description Framework format (RDF).
ABBYY FineReader
The computer program ABBYY FineReader is software that electronically converts printed and typed images into machine-encoded text so that documents can be electronically stored, edited and digitally displayed.
data models
The techniques and existing systems used for structuring data elements and showing relationships between them, as well as methods for interpreting the data structures and relationships.
data storage
The physical and technical concepts of how digital data storage is organised in specific schemes both locally, such as hard-drives and random-access memories (RAM) and remotely, via network, internet or cloud.
information confidentiality
The mechanisms and regulations which allow for selective access control and guarantee that only authorised parties (people, processes, systems and devices) have access to data, the way to comply with confidential information and the risks of non-compliance.
LDAP
The computer language LDAP is a query language for retrieval of information from a database and of documents containing the needed information.
LINQ
The computer language LINQ is a query language for retrieval of information from a database and of documents containing the needed information. It is developed by the software company Microsoft.
MDX
The computer language MDX is a query language for retrieval of information from a database and of documents containing the needed information. It is developed by the software company Microsoft.
N1QL
The computer language N1QL is a query language for retrieval of information from a database and of documents containing the needed information. It is developed by the software company Couchbase.
- database
Path to become a data entry clerk
What it typically takes to qualify: education level, where it is a regulated profession, and where to study.
Primary education
76 programmes in 13 countries teach the core skills of this job. Plus 69 short courses that build single skills.
A few examples, taught in the language of this page:
These are 1 of 145.
Sources
- 1. Source: Europass Qualification Dataset Register (European Commission)
Growth Pathways & Similar Roles
Explore typical career progression paths, adjacent skills, and similar roles to plan your next transition.
Where does data entry clerk fit?
Similarity scores based on skill overlap from ESCO data.
For organisations
The data on this page, working for your students, clients and staff.
Frequently asked questions
What skills are most important for a data entry clerk?
Is this a good career choice for someone looking to change careers?
What kind of work environment can I expect as a data entry clerk?
Is there a shortage of Data Entry Clerk in Europe?
How much does Data Entry Clerk pay in the United States?
Sources: ESCO O*NET ELA/EURES Cedefop BLS Data updated October 3, 2026 About our data