The data entry process consists of entering relevant data in a digital system or ERP software after it has been extracted from various documents. It is the necessary part for business that helps to transform the data into a suitable computerised format for workflow optimisation.
For instance, accounting departments need to extract data from certain parts of vendor’s invoices. They can apply special invoice scanners for data extraction and then enter this data into ERP software for accounting or financial reporting.
The process of data entry is usually manual and repetitive. It takes a lot of time and human sources. Due to this feature, many companies choose to outsource data entry.
Whether it performs in-house or outsourced, data entry continues to be a manual process that brings many errors and rework. Also, there can be different data formats, irrelevant info, and defects. For companies managing noticeable data volumes, these problems are reflected in extra payments and work delays.
Due to the manual data entry challenges, the automation process can significantly save time and resources for businesses. In the Research and Markets report analysts notice that the global data capture market is projected to grow by €3.4 Billion during 2021-2025.
Data entry automation has made a noticeable impact on the service providers’ workflow. Now, these companies can manage larger data sets than ever. In this publication, we are going to discuss the definition, process, use cases, benefits, and core features of automatic data entry.
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What is automated data entry?
Businesses can organise data entry automation with advanced software products. They improve the data entry process by removing or reducing manual tasks. This software extracts data from various file types: PDFs, emails, websites, and images. Then, the software presents relevant data from these documents in a structured format: XML, JSON, or CSV.
Automatic data entry software applies various modern technologies such as optical character recognition (OCR), robotic process automation (RPA), and artificial intelligence (AI). These solutions manage repetitive tasks, analyse documents in huge volumes, and are capable of educating for further work improvement. Their key features reflect accuracy, scalability, fast processing, and flexibility to save human time and resources.
The role of AI in automated data entry
Automated data entry helps employees focus on tactical and strategic tasks instead of performing repetitive and monotonous. This way of work increases general productivity, time, and outcome.
What stages include the automatic data entry process?
The efficient end-to-end approach to data entry automation consists of the following 5 steps:
The process of automatic data entry
1. Uploading documents
Companies receive raw data that can be unstructured, partly unreadable, and irrelevant. This data is presented in various forms: images, documents, and scanned files. In the first step, data is imported into a system or directly into the software for further extraction and automation.
2. Pre-processing documents
At this step, the software prepares and converts raw data into a machine-readable format. This process is realised by advanced OCR, AI, and ML technologies whose capabilities help algorithms to read and analyse documents.
3. Recognising and extracting relevant data
This step aims at the recognition and extraction of only relevant data. You can train the algorithm of the automated data entry software to recognise requested data fields.
4. Validating extracted relevant data
At this time, the software performs data verification. It is based on configured verification rules. You can realise this step manually or in a semi-automated way. The extracted data can be checked for accuracy and improved if it is needed.
5. Transferring extracted relevant data
The final step of automatic data entry consists of importing data into an appropriate database or system. Data can be presented as a structured output in various formats: JSON, XML, Exel, and CVS. For example, you can send extracted data into accounting databases or ERP software for further payments or forecasts.
What are the use cases of automated data entry?
Companies with different process structures and goals can find many benefits in automating data entry. The most frequent cases of Graip.AI clients are:
- data extraction from invoices, receipts, POs, and bank statements to perform accounting and financial tasks;
- collection and storage of relevant data for customer services;
- data extraction for faster and more accurate HR processes;
- creation of correct reports on the base of collected business data;
- the realisation of the “Know Your Customer” (KYC) process that includes ID card and other documents verification to check the identity, suitability, and risks of a business relationship;
- scraping documents and websites for data collection and analysis.
What are the benefits of automated data entry?
Businesses can receive various benefits from the integration of automatic data entry. The most important reason is the ability to focus on tactical and strategic goals instead of performing manual data entry. There are some other benefits for business:
1. Higher accuracy
Software for automatic data entry applies advanced technologies such as AI and ML for highly accurate extraction and post-processing minimisation. Algorithms of these technologies are efficient for error reduction and data limitations management.
2. Lower overall costs
Automatic data entry helps to reduce operational payments by optimising manual error- processes. Companies can avoid extra costs by outsourcing and hiring specialists for manual data entry.
3. Saved time
Automated data entry significantly reduces the time for documents and data processing. The technology saves 75% of the time on monotonous manual data entry.
4. Higher scalability
The technology helps to process unstable volumes of documents during the year and seasonal or even unpredicted strikes in data entry demand.
5. Increased productivity
Businesses can use allocated resources for automatic data entry and receive more productivity and impact on the outcome.
6. Increased employee involvement
While your business reduces monotonous manual work, employees show higher engagement in tactical and strategic tasks.
7. Intelligent data processing and storage
The software based on AI technology can classify specific document types and sources.
What are the core features of automatic data entry software?
Companies can make final decisions about automatic data entry implementation based on the key features of this technology.
- An ability to identify and classify various forms of data.
- Integration into ERP systems and automation tools.
- A data validation interface for manual checkups.
- Verification rules to warn users about the need for manual data review.
- A low-code or no-code environment that does not need to be supported by many developers.
- Real-time triggers to organise and automate a work process.
- Advanced AI and ML functions that help the automated software to learn and develop through every interaction.
- An ability to process different file types such as PDF, JPEG, email, Exel, and CSV.
Conclusion
Due to the Gartner study, the RPA technology can save nearly 25,000 hours per year at a cost of €830,000 for financial companies with 40 full-time accounting staff. As we mentioned, RPA is an essential part of automatic data entry. Such results show great perspectives of automatic data entry for business during further decades. Automated data entry consists of 5 simple stages: uploading, pre-procession, recognition and extraction, validation, and transfer to databases or ERP systems.
The automated data entry process significantly improves the work of various business units: financial, accounting, human resources, customer services, reporting, big data analysis, security, and risk management.
The amount of automatic data entry benefits impresses by its variety and necessity for businesses. There are higher accuracy, reduced overall costs, saved time, higher scalability and flexibility, increased productivity, higher employee involvement, and intelligent data processing and storage.