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Characteristics of data
In today's world, data is a key resource for companies in almost every industry. Whether it’s physical data like paper documents or digital data like electronic files, skillful acquisition, processing, and centralization of this information can lead to more efficient operations and, consequently, a real competitive advantage. Unfortunately, many enterprises still struggle with data chaos and lack effective tools for efficient importing and scraping of data, preventing them from fully leveraging modern technology.
Fortunately, continuous advancements in artificial intelligence (AI) allow for solving these problems. Modern AI techniques revolutionize the way companies acquire, process, and utilize data, making the process more efficient, accurate, and automated than ever before. Additionally, these techniques require less labor than previous methods, making them a much cheaper solution.
Problems with traditional data importing
Before discussing innovative AI-based data importing solutions, it's worth examining the challenges associated with traditional data importing methods. One of the main problems is the lack of standardization in data formats. Data can be stored in various forms, such as paper documents, electronic files (CSV, XML, PDF), or even digital images – the sources of data can be virtually limitless.
Moreover, physical data is often unreadable or incomplete, making accurate processing difficult. In the past, employees had to manually transcribe information from paper documents, which was tedious, time-consuming, and prone to errors. The advent of OCR technology has significantly simplified this process, but it has not always been accurate (especially when the quality of documents was very poor).
Even in the case of digital data, importing it often required complex processes, such as creating custom parsers for each data source and then ensuring that these sources did not introduce changes in the returned data.
It is also worth noting that importing data is one issue, but the target storage of data is another. Data must be properly processed and returned in the format of the target system – this also presents many challenges, such as incompleteness or data validation.
Traditional data importing techniques enhanced by AI
Artificial intelligence offers a range of innovative solutions that enable more efficient and accurate data importing from various sources. Below is a comparison of traditional methods, supplemented by how AI can improve their performance:
Scanning physical documents with OCR
Optical Character Recognition (OCR) technology enables the digitization of paper documents by automatically reading and converting their content into digital form. OCR technology began to be developed before personal computers appeared on the market, and even so, its performance was not always perfect. However, thanks to AI development, the problem of reading physical data seems to be finally solved. Modern OCR systems, enhanced with machine learning algorithms, can handle various fonts, formatting, and even incomplete data or illegible handwriting.
Data parsers for websites
Scraping data from websites is crucial for many companies that obtain information from various online sources where the provider does not offer a public API. Traditional rule-based parsers often required complex configuration, extensive testing, and were prone to malfunctioning in case of changes to the website structure (or technologies used to present data on the site). Thanks to AI, parsers can now dynamically adapt to changes on websites and efficiently extract the necessary data. We no longer need to continuously verify their operation and monitor for changes in the website structure – AI parsers, as long as there are no drastic changes (e.g., the data is completely removed), flexibly analyze the HTML structure and extract the needed data.
Importing with CSV, XML, and other formats
Many systems rely on importing data from CSV or XML files. Here, too, there is a risk of changes in the structure of these files – especially when the systems based on them are not regularly updated. Over time, problems arise when a system returns files in a different structure than initially prepared (e.g., adding additional data, changing conventions), or another system requires a different structure (usually after a major update). As a result, systems can no longer cooperate. Here, AI-based file converters come to the rescue, dynamically modifying imported/exported files and saving them in a compatible data structure, ensuring systems continue to communicate.
Another major problem is data incompleteness – sometimes a system does not always return all the expected data in a CSV/XML file. AI helps detect such cases and fill in the missing or incorrectly filled places with specified data, tailoring it to our needs.
Regardless of the data format, AI-based importers can automatically recognize the structure of files and import their content. During this process, they validate the data and adjust it to the target format – all without writing special rules and exceptions for individual cases.
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Additional capabilities in data importing with AI
AI significantly enhances traditional data importing techniques. Besides the methods discussed earlier, the use of AI opens up entirely new possibilities for data importing that were previously unavailable.
Extracting information from graphic files
Traditional data importing methods often struggled with information contained in graphic files, such as document photos or screenshots of web pages. However, thanks to advanced image recognition techniques, AI systems can effectively read and process data from such sources. Besides written text, we can use AI to extract visual data, such as reading values from charts or advanced diagrams.
Expanding poor information with AI
Many data sources contain poor or incomplete information, making their effective use difficult. However, AI-based systems can enrich this data, filling in missing elements based on context and other available information (e.g., fetching additional information from the internet). Thus, seemingly simple information can be expanded to meet the specific criteria of a system.
More accurate returning of uniform format
One of the main advantages of AI systems is their ability to standardize and unify data formats. Regardless of the source, these systems can automatically and flexibly convert data into a common, consistent format, facilitating further processing and analysis. Traditional data importers often required customization and adding support for each new data source. However, AI-based systems are much more flexible and versatile, enabling data import from various sources without the need for complex configuration.
This significantly reduces the time spent writing dedicated rules for each data source.
Interpreting imported data
Artificial intelligence can further enrich imported data with insights or other interpretations. Thus, based on data import, after analyzing and drawing conclusions by AI, the system can approach their further processing in a completely different way. For instance, machine learning algorithms can assign appropriate priorities to imported data or label them accordingly, and depending on this, lead their further recording in a completely different way.
Our experience
At WebMakers, we also use AI for data importing. In our daily work, we have significantly simplified data scraping from websites, especially when it is a one-time task for a specific site, and writing a dedicated parser would be too time-consuming. Recently, the marketing department needed to obtain information about companies participating in an event. The website was coded in such a way that the HTML code was rendered on the fly using JavaScript technologies, and access to the site required authentication (logged-in user). An additional difficulty was the fact that the listing did not contain company names, only their logos. Writing a parser would be extremely difficult, and interpreting companies would require adding further nesting in the scraper, which would go through specific subpages and extract additional information. We approached the problem in the simplest way possible. It was enough to take a screenshot of the entire page (using a special browser plugin), and then the saved image was sent to ChatGPT 4o with an appropriate prompt that aimed to identify the company names from the screenshot and additionally fetch additional information about them (URL, short characteristics, etc.). We received exactly the data we expected, and the whole process took a few minutes instead of probably at least a few hours that would have been needed to write a dedicated scraper. Additionally, we could immediately convert the obtained data to a specific format by appropriately configuring the prompt sent to the LLM.
The future of data importing with AI
Advancements in artificial intelligence promise even more advanced and automated solutions for data importing and scraping. In the future, special AI models will be able to independently determine what data is needed and how to acquire and process it. Data importers will operate universally, and dedicated solutions in this area will not be necessary. Such a universal importer will only need to be appropriately configured, and the rest – how to fetch the data and process it – will be created without human intervention (writing source code). AI algorithms will figure out how to retrieve the desired data from a given source, validate it, fill in any incomplete or incorrect data, and return it in the target format.
Thanks to developing machine learning techniques, AI systems will become increasingly intelligent and fully adaptable, enabling companies to efficiently acquire and utilize data from almost any source.
Summary
The revolution in data importing and scraping using artificial intelligence is already underway. Modern AI techniques, such as advanced OCR, dynamic web page parsing, image recognition, and data enrichment, allow companies to acquire information from various sources more efficiently and accurately.
AI capabilities provide companies with more accurate and essentially unlimited (in terms of data sources) conditions for centralizing and utilizing their data resources. As a result, it allows for low-cost organization of their information resources, enabling the organization to operate more stably based on solid data. As AI advances, data importing and scraping will become even more automated and intelligent, allowing companies to fully centralize and harness the potential of their data.
