Data entry firms enter handwritten or typed data from paper or digital documents into documents, databases, spreadsheets, or web platforms. As part of the service, they extract, clean, digitize, organize, and manage information to enable businesses to eliminate fragmentation across their assets and find information easier.  

Their offering extends to converting files between formats, such as PDF to Excel, HTML, or Word, invoice processing, eCommerce cataloging, and extracting information from financial reports, surveys, questionnaires, and medical records. 

With the advent of digitalization, data entry companies began offering data scraping from websites, image data entry, and numeric/alphanumeric entries. 

Data entry services can take a few hours or several days, depending on the volume, complexity, and type of entry. Common turnaround times include same-day delivery (3 to 12 hours), overnight, 1 to 2 days, or weekly/monthly schedules for high-volume or ongoing tasks.  

Many providers also offer expedited services for urgent projects that require completion within hours or overnight. 

Data entry outsourcing companies charge between $6 and $10 per hour for their services. General data entry, eCommerce data updating, and catalog management entail lower hourly rates, while data mining and data processing cost a bit more.  

Data conversion is mainly charged per-page, wherein single-page conversion starts at $0.15 per page. PDF conversions or scientific formula conversions can go from $0.35 to $0.40 per page.  

Service type, data volume, type of entry, data complexity, and team composition affect the pricing of a data entry firm. Project timelines also impact overall costs, as rush requests entail overtime fees and larger teams. 

The industry also plays a role. Healthcare, financial, and legal sectors require familiarity with protocols, meeting compliance standards, stringent security, and stricter quality assurance protocols, all of which incur higher costs. 

Every data entry starts with process discovery. At the very onset, the business needs to define the source systems and document types, target schema, validation rules, turnaround times, volume variability, required accuracy thresholds, and exception categories.  

It also needs to decide whether the vendor will do pure manual entry or OCR-assisted capture. After vendor selection, the parties usually run a pilot using real sample files to measure field-level accuracy, latency, exception rates, and overall fit. At this stage, they also finalize SOPs, data dictionaries, confidence thresholds, QA sampling logic, and escalation paths for ambiguous or unreadable records.  

In steady-state operations, work is routed through defined queues, low-confidence items go to human review, outputs are reconciled against business rules or master data, and performance is managed through SLAs and KPIs such as cost per record, first-pass yield, turnaround time, and defect density.