Data capture and information recording are essential practices carried out by organizations to collect relevant information through various communication channels, whether digital or traditional. A clear example of this process is data mining, which involves analyzing large volumes of information to identify patterns, allowing companies to gain valuable insights. Today, data capture has become one of the top priorities for businesses, as it enables them to be more productive and achieve a higher level of competitiveness.
Unfortunately, a large portion of the data handled by organizations—over 80%—is stored in unstructured formats, such as paper documents, emails, PDF files, and even images. One of the main challenges of data capture is converting this disorganized information into structured data that can be easily located and processed by automated systems. This transformation is essential in order to use that data in specific tasks, such as entering information into software or manipulating it through a spreadsheet.
What does data capture mean?
When we talk about data capture, we refer to the process that allows information—whether structured or unstructured—to be extracted from any type of document, regardless of whether it's a digital or printed file. That information is then converted into a format compatible with computer systems, enabling its efficient storage, processing, and use.
In the past, this activity was carried out manually. However, due to the exponential growth in the volume of data companies must manage, advanced technologies such as Artificial Intelligence have been developed to automate the data capture process. Automation has taken this procedure to new levels of speed and efficiency. But before discussing these tools, it is essential to understand how this process unfolds in practical terms.
Stages of the Data Capture Process
Generally, the data capture cycle begins with a form filled out by a person. For example, when a patient visits a medical consultation for the first time, they must complete a form with personal information such as name, address, and more. This data, initially recorded on paper, must be digitized so it can be used within the clinical system. This means that someone must enter the information into a database or application.
The procedure follows a sequence like the following:
- Information is received in various formats such as images, physical or digital documents, emails, and more.
- The data is extracted, either manually or through automated technology, and then converted into a digital format.
- The digitized information is integrated into other business or technological processes.
Why is data capture so important?
A company that understands its internal processes and has an effective mechanism for collecting and analyzing information possesses the fundamental elements for growth and consolidation. Today, we live in a fully digitalized environment where tools such as the internet and emerging technologies are used daily by individuals, institutions, and businesses to perform a wide variety of tasks. This scenario generates an overwhelming amount of information that can only be properly managed through the use of specialized technologies, such as Big Data analytics or Artificial Intelligence.
An effective data capture strategy enables organizations to manage both structured and unstructured information, thus obtaining valuable data that facilitates decision-making. This translates into the ability to anticipate market trends, identify new opportunities, and gain competitive advantages over other companies.
5 Key Benefits of Data Capture
1. Increased Employee Satisfaction
Manual data collection can become slow and exhausting for employees. By implementing automated data capture solutions, the operational workload is reduced, allowing the team to focus on more strategic and value-added activities.
2. Error Reduction
Human work, especially when repetitive, can lead to costly mistakes. Automating the process drastically reduces the margin of error, thereby improving the accuracy and reliability of the data.
3. Improved Information Security
Physical documents not only take up space but are also difficult to protect adequately. There is a risk that sensitive data may be accessible to unauthorized individuals. Modern data capture solutions allow for the encryption and protection of such information to prevent leaks or unauthorized access.
4. Access from Anywhere
When documents are stored in physical format, their availability is limited. With digitization, data can be stored in the cloud, enabling remote access by different teams or departments and promoting more agile workflows.
5. Better Decision-Making Quality
Thanks to automation, companies can collect data faster and more accurately, allowing them to make decisions based on up-to-date and reliable information.
Fundamental Stages of Data Capture
To obtain useful and strategic information based on the analysis of large volumes of data from various sources, it is necessary to go through several specific stages:
1. Identification and Classification of Data Sources
In today's world, data comes from many platforms: social media, emails, chats, web forms, and more. That’s why the first step in the data capture process is to properly organize and classify all these sources, so they can be handled according to the business's specific requirements.
2. Information Extraction
Once the sources have been classified, it is necessary to select the appropriate technological tools to extract the information from each one. These include technologies such as OCR (Optical Character Recognition), ICR (Intelligent Character Recognition), OMR (Optical Mark Recognition), QR code reading, and IDR (Intelligent Document Recognition).
3. Verification and Export of Extracted Data
The captured data must be validated to ensure it meets the required format and the standards of the document management system. After this verification, the data should be integrated into the company’s technological platforms, such as databases or enterprise software. In some cases, validation and export are considered separate stages, depending on the complexity of the process.
Different Methods of Data Capture
The ideal method for data capture depends on the type of company and the volume of information it handles. For a long time, this process was carried out manually, which worked for small businesses. However, as companies grew, it became inefficient and prone to errors.
Manual Data Capture
Manual data capture involves a person extracting and entering information into a system. While it is still applicable in contexts with small amounts of data, it presents significant limitations: accuracy depends entirely on the operator, and the risk of errors is high.
Automated Data Capture
When it comes to processing large volumes of information, manual data capture becomes unfeasible. With automation, companies can collect data in various formats quickly and accurately. This type of capture relies on technologies such as:
- OCR (Optical Character Recognition): Allows printed or scanned text to be converted into digital data that can be interpreted by computer systems. It is widely used in sectors such as healthcare, finance, and insurance.
- ICR (Intelligent Character Recognition): Similar to OCR, but with the ability to interpret handwritten text. It is especially useful in banks and organizations that receive handwritten forms.
- IDP (Intelligent Document Processing): This technology combines artificial intelligence with OCR to automatically classify, verify, and extract data within complex workflows.
Strategic Benefits of Effective Data Capture
Implementing effective data capture solutions enables companies to build comprehensive databases with information about customers, products, suppliers, and other key business aspects. Its main advantages include:
