Data entry is a generic term used to describe the process of entering data into a computer<\/strong> or another electronic device. The data can be entered into a database, an Excel sheet, a CRM, the cloud, etc.<\/p>\n Sometimes data entry involves entering data from one digital file to another (for example from Google Sheets to Microsoft Excel) and sometimes from a physical document to a digital one. This latter process is mainly referred to as data digitisation<\/strong>.<\/p>\n The data entry service is carried out mostly manually. Sometimes the terms data entry and back office are used interchangeably<\/strong>, which causes confusion in most people.<\/p>\n Back office includes all the tasks that a company has to deal with in order to support front office operations<\/strong>. Since the back office sector involves a lot of paperwork, the employees in this field have to carry out various data entry tasks, such as data recording, data transcription, data classification, data annotation, etc.<\/p>\n For greater clarity, data entry<\/strong> is generally only a part of the back office processes and involves exclusively tasks concerning data processing. On the other hand, Back Office includes data entry but is not limited to that. Back office tasks depend on the department, the sector and the company’s business model.<\/p>\n While the most popular data entry activity is copying and pasting information from a source to a database, there are also other more specific assignments that a data entry operator can handle.<\/p>\n Data Tagging- <\/strong>The tagging process adds small pieces of information or descriptions to an entry, thus allowing it to be indexed, searched in a database, explored in a catalogue, classified, etc. This service makes it possible to speed up the process of searching for a piece of data.<\/p>\n If a company has a lot of data to tag, it generally chooses to outsource the process since it is an activity that takes away a lot of time.<\/p>\n Data Annotation-<\/strong>This is the process of selecting data, allowing it to be framed or highlighted and labelled. Generally all types of data are annotated, such as images, audio, sounds, texts, etc.<\/strong> Data Capture-<\/strong>This is the process of capturing, collecting and recording the information that will then be elaborated and processed by a machine. Data capture can be done manually by a data entry operator or automatically by software. Some technologies used for data capture are<\/p>\n Some technologies used for data capture are<\/p>\n Data capture also makes use of smart cards, barcodes, QR codes<\/strong>, etc. to collect information.<\/em><\/p>\n When the source of the data or information is an audio\/video file<\/strong> that needs to be converted into text, the process that is carried out is called data transcription<\/strong>. As the name suggests, it is the process of transcribing audio and voice files.<\/p>\n Another data transcription functionality is adding subtitles or even captions to videos<\/strong>. Dialogue recognition or automatic subtitles are an example. They can also be used to help data transcription in cases where a quick solution is needed. Data Logging-<\/strong>This is the process of recording and collecting data to be stored<\/strong> for a specific period of time<\/strong> for a subsequent analysis. Data Processing-<\/strong>After the data has been collected it can be processed for various purposes. The main reason why it is processed is to analyse it and produce reports<\/strong>. This type of data entry can also be used for storage, archiving, organisation, classification, etc.<\/p>\n Data Cleansing-<\/strong>This process is used to organise and correct the information stored in the database, identify duplicates, errors, old information and to delete irrelevant information. <\/em>Data cleansing is used as a form of maintenance and updating of the database<\/strong>.<\/p>\n All companies need data entry<\/strong>, but whether they have a department dedicated to this activity depends on the volume of information they need to process daily. All companies need to record some types of information.<\/p>\n When? Every day during daily operations such as shift management, working hours, payslips, payroll, invoices, transactions, information in memory, and so on<\/em>.<\/p>\n Depending on the sector, some companies need a more targeted approach since data entry is a strategic activity for their business model. For example, e-commerce sites and online shops need data entry to enter their products online<\/strong>, label them, classify them, and so on. They also need to process the data in order to digitise catalogues and prices. Another sector that needs data entry is that of Transport and Logistics <\/strong>due to the large number of paper documents such as: invoices, delivery reports, load reports, import and export documents and transport documents<\/em>. Others also make use of data entry, including:<\/p>\n
\nMachinery can accelerate the process through automation, voice recognition, character recognition, etc.<\/p><\/blockquote>\nData Entry vs Back Office<\/h3>\n
Main Data Entry activities<\/h2>\n
\nData annotation is generally used for the analysis of large quantities of data with the help of automation and machine learning. The software that uses machine learning will recognise:<\/p>\n\n
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Data Transcription<\/h3>\n
\nAgain, machines are not fully capable of providing a perfectly accurate transcription and cannot replace manual work.<\/p>\n
\nThis branch of Data Entry<\/strong> is used to discover trends, record information on parameters, trends, activities, etc<\/em>. It is often used for scientific purposes or in international transport to monitor systems and networks. This process is almost always carried out by a machine rather than manually.<\/p>\nWhich companies need the Data Entry service?<\/h2>\n
\nThe data entry service for eCommerce is by far one of the most requested because it can be easily outsourced.<\/p>\n
\nRecently, Software companies<\/strong> need data entry operations and data annotations as a result of the development of artificial intelligence and machine learning. The workload for these projects is generally enormous and therefore a lot of staff would be needed to meet the deadlines.<\/p>\n