How AI Automates Data Scraping and Data Analysis

12 Jun 2024

Over the last few years, AI has revolutionized our lives by not just automating repetitive work but also seemingly developing the ability to "think" like a human being and tap into the creativity pool. Seriously, how many of you have used "Chat-GPT" to compose a poem or used "Suno" for another love song?

Maybe it's time that we moved from a dark time when humans needed to be responsible for all the boring copy jobs to a more high-tech future where we are left to do the real killing, for example, negotiation and strategic planning.

Data Scraping in The Era of AI

Web scraping Tools with AI

In modern times, we usually collect data online from one or multiple sources. The tedious process can be automated since the harvesting action is repetitive. To do this, there are many data collection tools available, or if you prefer, you can call them web scraping tools.

Old ways of scraping data from the internet can be problematic because they rely on the HTML rule of the website to navigate to the target data. Once the HTML structure is changed, the scraping rule will be invalid. Not to mention modern websites tend to involve Javascript interaction to enhance user experience, which adds to the difficulty of getting the data precisely.

With the help of AI, however, we can deal with website changes easily. Take one tool, for example. As a no-code-involved scraper tool, Octoparse is dedicated to integrating AI into its intuitive scraping interface.

It leverages AI to improve the auto-detection of web page elements, making it easier for beginners to start scraping. The AI enhances the accuracy of identifying data fields, buttons, and other interactive elements on web pages, reducing the learning curve for new users. By simplifying the initial setup, users can quickly create effective scraping workflows without technical knowledge.

For more advanced users, Octoparse's AI can assist in writing and adjusting scraping rules. Once trained, the AI can generate and modify the necessary code to accommodate changes in website structures. This capability ensures that scraping rules remain effective even as websites evolve, reducing the need for manual intervention and ongoing maintenance. Users can rely on the AI to handle complex adjustments, ensuring continuous data extraction with minimal disruptions.

Robotic Process Automation (RPA) With AI

There are also AI-based RPA tools (Robotic Process Automation) to automate any repetitive and regular steps within or between software and systems.

“Robotic process automation is not a physical [or] mechanical robot,” says Chris Huff, chief strategy officer at Kofax. While it can mimic most human-computer interactions to carry out the most mundane and repetitive tasks and processes in the workplace, at high volume and speed. For example, imagine you need to move files from one place to another or conduct a freight booking.

With AI joining automation, things can be done in a more intelligent way. For example, AI can help decide which document and file to be processed by using Natural Language Processing(NLP). AI can read and interpret the text and content, and categorize them for different automation workflows.

We can also use simple natural language to talk with AI, so it can build the RPA workflow automatically for us according to our demands and even based on historical patterns and situations. The time when AI can be our powerful partner in life and at work has arrived!

Data Analysis in The Era of AI

Backed by the magic of machine learning, AI can process big and complex datasets and draw accurate predictions and insights by identifying patterns and anomalies.

It's not just about crunching numbers. AI is getting way smarter than that nowadays.

AI Data cleaning

Since data is not always consistent in format and may include inaccuracy, AI can help with data cleaning and preprocessing by identifying anomalies such as duplicate entries, misspelled addresses, missing values, inconsistent formatting for locations, etc.

Octoparse's AI also helps in the preliminary cleaning of extracted data. By applying AI algorithms to filter and refine the raw data, users can receive higher-quality outputs that are more useful for analysis. This automated cleaning process helps eliminate errors and inconsistencies, providing cleaner datasets that require less manual processing. As a result, users can focus on analyzing the data rather than spending time on tedious cleaning tasks.

AI Data visualization

AI can create interactive charts and graphs that reveal even the slightest change overlooked by the human eye. With real-time data constantly being fed to the AI system, the dashboard will reflect the latest trends and patterns for any prompt actions.

For example, ThoughtSpot leverages AI and a search-driven interface to simplify data exploration and visualization. It connects to various data sources, consolidating information in one platform, and allows users to create logical data models that define relationships and context. By typing natural language queries into ThoughtSpot's search bar, users can have the AI interpret and fetch relevant data. The platform generates interactive charts, graphs, and dashboards based on these queries, which users can further customize.

AI Data insights

As humans, we excel at drawing insights from circumstances. But even the lowest level data analysts take a long time to master the skills of graph interpreting and data processing. Using AI, then, can greatly save us time and also the cost to get the insights we need. AI, with its powerful and fathomless NLP(Natural Language Processing) ability, can help us conduct predictive analysis as well as sentiment analysis.

In E-commerce, AI-based data analysis solutions like Octoparse VOC help thousands of companies get a thorough understanding of how their products fare. From customer profile (who, when, where, why), positive/negative feedback to unmet needs and pre-purchase concerns, this tool(also including its extension) provides detailed information that can be crucial for later product development and marketing campaign direction.

About price monitoring, some AI tools can make this process fast and easy. Competera is an AI-powered pricing platform designed to help retailers optimize pricing strategies. With algorithms and machine learning, it offers pricing optimization based on many factors like demand elasticity.

It's clear that AI indeed plays a significant role in predictive analytics. By forecasting future patterns, AI-based data analyzing tools can help businesses stay ahead of the curve.


As AI learns and adapts, the era of data being a headache will be over. Humans, being the commanders, will be the ones to choose which possibility calculated by AI should be taken.