Emails, files, images, documents, and social networks are all sources of data. And modern businesses spend huge amounts of money on... manually extracting this data and analyzing it. Meanwhile, modern technologies make it possible to completely automate this process. Intelligent data extraction is a trained algorithm that sifts through data. It can instantly prepare a thesis report, compile numbers, catalog personal information, and much more. In this article, we will tell you what data extraction is, how to implement it, and in which areas it is already revolutionizing.
What is Intelligent Data Extraction?
Intelligent data extraction is the result of combining artificial intelligence and machine learning. AI capabilities allow it to thoroughly examine various sources: scanned images, various electronic file formats, articles on websites, threads and photos on social networks, and so on. After examining the source, the intelligent data extraction system selects nuggets of information that it uses to improve various workflows or answer user queries. Intelligent data extraction systems are used in many areas, from finance to medicine. They identify patterns in documentation, analyze customer feedback, promptly provide information upon request, and so on. Intelligent data extraction helps reduce the risk of human errors. Imagine a mountain of documents. Imagine how much time an employee will spend studying it. Imagine how tired he will be, and because of fatigue, he may miss something important. An established intelligent data extraction system will spend less time on analysis. But the main thing is that she won’t get tired and won’t miss anything. This achieves several things:
- Increased workflow efficiency
- Reduced number of errors
- And you can save a lot of money
##How Intelligent Data Extraction Works We have already learned what data extraction is. So let's figure out how it works! We'll follow all the steps from start to finish!
Step #1: Receive data
The setup begins with selecting the information source. This could be anything: scanned images, various electronic file formats, articles on websites, threads and photos on social networks, and so on. But let's take a specific example. Let’s imagine that a conventional bank needs to attract a new client. The sources of raw data in the case of a bank will be digital forms, scanned documents, transaction histories, and more from various channels such as online applications, email, and mobile banking platforms.
Step #2: Pre-treatment
Highlight relevant sources. Get rid of unnecessary sources, the data from which seems redundant to you. You may need to convert your scanned forms into a more convenient digital format. By doing this, you will ensure data consistency.
Step #3: Training the Algorithm
Machine learning models “learn” to interact with data. By analyzing sources of information, they learn to recognize patterns and relationships. Let's remember the example with a conditional bank. To train the algorithms, the bank can provide past loan applications that are in its database. The algorithm will study these applications and learn to recognize data fields such as "Name" and "Annual Income".
Step #4: Extraction
At this stage, the algorithms extract relevant data points. Let's look at the example of a bank. The trained algorithm will extract personal data or amounts from the transaction history on the application form. Note that the algorithm can process huge deposits of data in a short time but will not lose the accuracy of its extraction.
Step #5: Check
Trust, but check. Before accepting an algorithm as fully trained, check how successfully and efficiently it interacts with the data. At this stage, validation will help you confirm the correctness of the extracted data. Let's say, in the case of a bank, when rechecking, you need to pay attention to deleted data using predefined rules.
Step #6: Continuous Improvement
Algorithms learn and improve as they interact with data. Therefore, the accuracy and reliability of their work increase with each request processed. For example, a bank implemented data extraction into its workflow. And after some time, the bank introduced new conditions. No problem! The trained algorithm adapts to them with amazing speed.
What is the effectiveness of Intelligent Data Extraction?
Businesses waste money, time and effort manually extracting data. However, modern technologies and trained algorithms are much more effective. But let's look at the points:
| Feature Manual | Data Extraction Intelligent | Data Extraction |
|---|---|---|
| Time Consumption | High (Hours to days) | Minimal (Minutes to hours) |
| Error Rate | Prone to human errors | Significantly reduced |
| Cost Higher (Labor-intensive) | Lower (Automation savings) | |
| Scalability | Limited | Highly scalable |
| Data Consistency & Quality | Variable | Consistent and high-quality |
| Adaptability | Rigid processes | Adapts to varying data forms |
Applications of Intelligent Data Extraction
Intelligent data extraction helps you automate and improve the handling of user requests or your entire workflow. Let's find out which industries benefited most from this innovation:
1. Healthcare
Precision in healthcare depends on the well-being of patients. Intelligent data extraction simplifies tasks such as managing patient records and transferring information from handwritten prescriptions to electronic medical records. On a busy day with a large influx of patients, a doctor can make a request, and an automated algorithm will fulfill it. For example, it will save test data and attach it to the patient’s medical record. The doctor will leave the task to the system and return to work. But his request will be accurately executed, and the data will be saved in the right place. In addition to administrative tasks, the data extraction system can be assigned research functions. For example, she can be entrusted with the study of medical literature and its thesis retelling. All this makes the work of medical institutions more efficient! And all thanks to modern technologies—that is, an intelligent data extraction system!
2. Surveillance tools
Surveillance tools collect data for digital systems. This data is then processed by application performance monitoring tools. And in this case, intelligent data extraction will provide significant help:
- Log management: A trained algorithm will reduce the volume of log files several times. It will identify inconsistencies and patterns that indicate system errors. Thanks to this, it will take a couple of hours to find this error, but a couple of seconds!
- Optimization of metrics: From a huge volume of data, the algorithm will identify relevant metrics that will give a clear picture of the performance of the digital system. Well, then you can carry out timely optimization!
- Real-time alerts: The algorithm can detect critical incidents and trigger immediate alerts. Thanks to this, the reaction will be quick, and the digital system will be protected from a potential threat.
- Analysis of user behavior: The algorithm studies user requests, based on which it can suggest improvements to the interface or system responsiveness. Well, the user experience will become more pleasant!
3. Legal service providers
In the legal field, meticulousness is important. Of course, accurate data extraction improves the legal service delivery process. And here's how exactly:
- Document Review: An automatic algorithm quickly scans the entire volume of data and then extracts the relevant articles, dates, or names of the parties involved. After reviewing any document, the algorithm will identify key points and provide a summary report.
- Contract analysis: Having studied the conditions specified in various documents, the algorithm will identify possible risks and options for revising any clauses. The algorithm will transmit the information to the specialist, and he, in turn, will be able to advise the client.
- Case Study: To strategize a case, you need to find a precedent. The algorithm will be able to do this much faster than a human, crawling through a huge amount of data in a matter of moments.
- Client data management: The algorithm can study clients’ personal files, catalog them, and update and supplement them. So all important information will be available at the right time.
4. Accounting and taxation
Come tax season, data extraction can help accountants easily sort through countless stacks of receipts, financial statements, and transaction records. The algorithm will identify the most important points and present them in the form of a report, and the accountant will be able to save time and effort. Intelligent data extraction will allow you to quickly reconcile records, identify inconsistencies, and make all necessary payments in a timely manner. Additionally, the trained algorithm can be used to analyze data from previous financial years. It will quickly identify deviations and shortcomings and help correct them in a timely manner.
5. Banking and finance
The bank is at any time inundated with numerous inquiries, applications, and demands for immediate consultation. To understand this flow, you need accuracy as well as a quick reaction. And intelligent data extraction will help with this. The client who contacted the bank will provide his data, and the algorithm will instantly analyze the most important points. For example, to approve a loan application, it is necessary to verify the client’s solvency. This means that the algorithm will reveal the client’s credit score, employment records, and asset valuations. In addition, the intelligent data extraction system can notice unusual actions in the client’s personal account and immediately report them. And now the client is freed from problems with scammers. Additionally, a trained automated algorithm is useful in analyzing market reports. It will quickly identify stock trends or key economic signals.
Techniques for Intelligent Data Extraction
For intelligent data extraction (IDE) to reach its full potential, you need to ensure that the data is not only accurate but also useful. To do this, you should use several methods that will help filter the data and protect it:
- Quality over quantity: Determine what data you and your customers need. Load only relevant and up-to-date data. The total amount of data will be reduced, but the remaining data will be reliable, and their analysis will give extremely accurate results.
- Update your algorithms regularly: Algorithms need constant training and updating; otherwise, they will become outdated and useless. Provide algorithms with relevant data on which they can improve.
- Data Verification: Data verification ensures that the data is accurate. However, it is best to carry out the verification in two stages. Primary and secondary verification will help identify inconsistencies and errors, if any. This way, you will save yourself from possible problems and risks.
- Structured data storage: organize received data. Then the algorithm will be able to retrieve them faster based on your request. If the data is not systematized, the algorithm will have to spend additional time searching and analyzing it.
- Keep your data private: Nothing is more important than protecting your data! This includes personal or confidential information about you and your customers that scammers can use. Therefore, make sure that this type of data is encrypted.
- Feedback Loop: Give your users the opportunity to provide feedback. Then they can alert you if your data is inaccurate or out of date. Ultimately, this will show them that you care about them and that their opinions are important to you.
- Integration with other systems: Check if your IDE system integrates with other business systems. If the integration is broken, there will be problems with data transfer and compatibility.
- Regular audits: Don't stop at two-step verification before loading data. Extracted data should also be regularly checked for accuracy and consistency. And all this is already in the process of being used. This way, you can identify and fix any system problems early.
Want to Use Intelligent Data Extraction?
Intelligent data extraction helps you explore raw sources of information and turn them into tools to improve workflows and user experiences. However, before you implement trained algorithms, determine exactly how they will benefit you and what problems they will help solve. Intelligent data extraction is constantly being improved; it is rebuilt to new conditions and adapted to your requirements. Modern business has the opportunity not only to collect data and engage in long, very long manual analysis... No, modern business can use the full potential of data for successful activities!