In today’s data-driven business environment, timely and accurate data collection is essential for making informed decisions. Many organizations rely on publicly available online data to track market trends, competitor information, or customer feedback. However, manually gathering data from websites can be labor-intensive, prone to errors, and inefficient. To overcome these challenges, our team implemented a solution using Microsoft Power Automate Desktop to automate the process of web data scraping, data storage in Excel, and data preparation for analytics.
This article shows how Power Automate can be used for web scraping, outlines its key features, shows how we designed and executed an automated workflow, and compares its strengths with Python for more advanced and scalable use cases.
Web scraping with Power Automate is the process of extracting data from websites using Power Automate. This method enables users to create workflows that interact with web pages, extract data automatically, and store it without requiring coding knowledge. Key use cases for this type of scaping may include market analysis, price comparison, lead generation, academic research, brand monitoring or competitive analysis.
Firstly, this is a no-code solution. Power Automate’s drag-and-drop interface allows users to set up automated workflows without the need to write code. This feature makes it highly accessible for non-developers who do not want to hire an IT team.
Secondly, this is browser automation with Chrome, Firefox, etc. It allows you to interact with websites like a human. For example, open the browser, navigate through pages, interact with elements and even fill the forms.
Thirdly, Power Automate allows users to extract various types of data such as links, URLs or images. Users can select what data they would like to extract, making the process more efficient.
And finally, Power Automate integrates with Microsoft tools and third-party services, including SharePoint, Excel and Power BI. This makes it easier to store the extracted data for further analysis.
The automated workflow reduced the time spent on manual data collection by 80%. The client’s team could focus on higher-value tasks, such as data analysis and strategic decision-making.
Automating the data extraction process eliminated human errors, ensuring consistent and accurate data collection. This accuracy was critical for making reliable business decisions.
The scheduled flow ensured that the Excel file was always updated with the latest data, providing stakeholders with real-time insights and enabling faster responses to market changes.
The solution was designed to be easily scalable. The client could expand the workflow to extract data from additional websites or include new data points without significant modifications to the existing flow.
The error-handling and notification system provided real-time alerts, ensuring that any issues were promptly addressed, minimising downtime and maintaining data availability.
The first step in the implementation process was to create a new flow in Power Automate Desktop to automate the task of opening a web browser and navigating to the target website.
Launch Power Automate Desktop and create a new flow named “WebDataScrapingFlow.”
Add the “Launch new Chrome” or “Launch new Edge” action to open the web browser and navigate to the URL of the target website.
Configure the flow to maximise the browser window to ensure all web elements are fully visible and accessible for data extraction.
Once the browser was set up, the next step was to extract data from the webpage.
Inspect the webpage to identify the specific data elements to be extracted. This could include product information, pricing, customer reviews, or other relevant details.
Use the “Extract Data from Web Page” action in Power Automate Desktop to capture the desired data.
Define the target elements using XPath or CSS selectors for precise extraction.
Store the extracted data in a data table variable within Power Automate Desktop.
If the website includes multiple pages of data, implement a loop to navigate through each page and extract data iteratively.
Use the “Loop” action to click the “Next Page” button and continue extracting data until all pages have been processed.
Verify the extracted data by displaying it in a message box or writing it to a temporary file for validation.
After successfully extracting the data, the next step was to store it in an Excel file for analysis.
Create a structured Excel file with predefined columns corresponding to the extracted data points.
Use the “Write to Excel Worksheet” action in Power Automate Desktop to export the data from the data table variable into the Excel file.
Specify the file path, sheet name, and starting cell for the data export.
Save the Excel file in a shared location or a cloud-based service like OneDrive or SharePoint for easy access by the client’s BI team.
To ensure the flow runs smoothly and reliably, we implemented error-handling mechanisms and notifications.
Error Handling: Use a “Try-Catch” block to handle potential errors, such as website downtime or changes in the page structure.
Logging: Record any errors encountered during the flow execution in an error log file for troubleshooting.
Notifications: Configure the flow to send email or Teams notifications to relevant stakeholders upon successful completion or failure.
The final step was to automate and schedule the flow to run at regular intervals.
Use Power Automate Cloud to create a scheduled trigger for the desktop flow.
Configure the trigger to run daily, weekly, or at any other frequency that aligns with the client’s data needs.
Monitor the flow’s performance and update it as necessary to adapt to changes in the website’s structure or data requirements.
Project Objectives
The project aimed to achieve the following objectives:
Solution Overview
Our solution leveraged Power Automate Desktop to automate the entire process of web data scraping and data storage in Excel. Power Automate Desktop is a low-code platform that allows users to build and deploy automation workflows for desktop and web applications. It is particularly well-suited for tasks such as:
– Navigating websites.
– Extracting structured or unstructured data.
– Exporting data to various file formats, including Excel.
– Integrating with other Microsoft tools like Power BI for analytics and reporting.
Advantages
Disadvantages
Choosing whether to use Power Automate or Python for website scraping depends on your technical skills, how complex the task is, and how much flexibility you need.
Power Automate is ideal for users without programming knowledge. Its integration capabilities allow users to use data more effectively. For example, integration with Power BI would be useful for business analysts who are already using the Microsoft ecosystem. Plus, ready-made connectors and templates simplify and speed up the process of creating scrapers.
On the other hand, web scraping with Python offers flexibility. Its Beautiful Soup, Selenium, and Scrapy packages allow the tool to handle any scraping scenarios. Thus, web scraping is way faster with Python. This makes the whole system more stable, reducing timeout errors. It is also more scalable, allowing the scraping of large volumes of data. For example, in one of our projects, we attempted to scrape 2 million rows of data using Power Automate, but the estimated runtime was around 2 months. We switched to Python instead, and the script successfully pulled all 2 million rows in just 3 hours.
Thus, Power Automate is ideal for business analysts or non-developer users who need quick scraping that integrates with Microsoft tools, while Python is the choice for developers who need highly scalable and customisable scraping.
This article highlights how Power Automate Desktop can streamline the process of website data scraping, data storage, and real-time analytics. By automating these tasks, the client significantly improved operational efficiency, data accuracy, and decision-making capabilities.
The solution is adaptable, scalable, and capable of integrating with various BI tools, making it a valuable asset for any organisation seeking to leverage online data for competitive advantage. If your organisation is looking to automate data collection and analysis, we can help you design and implement a customised solution that meets your specific needs.
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