4 Steps of Web Data Integration (With Tips & Examples) in '24

Today, most applications in use are primarily web-based platforms. These web-based applications generate and store large amounts of data. Businesses extract data from various websites, such as blogs and social media platforms, since it provides them hidden insights. However, extracting relevant data from multiple web sources, organizing it, and managing it all is a time-consuming and complex process. This is where web data integration comes in.

What is web data integration?

Web data is aggregated from different websites, making it difficult to manage and transform in a single location. Web data integration aggregates, transforms, and manages web data sourced from different websites in a unified framework. It enables businesses to get an accurate and unified view of web data.

Both web scraping and web data integration solutions are used to extract data from web pages. However, data extracted from different web pages using a web scraping solution is not easily accessible in its original form. Web data integration goes beyond web scraping by extracting and normalizing data from various web sources. In this way, web data is made easily usable by end-users.

What is the importance of web data integration for businesses?

Web data integration helps businesses to integrate the scraped data into existing systems. With the features listed below, it improves the web scraping process: 

• Data cleaning: Web data integration manages web data aggregated from different websites into a single framework. Web data comes in various formats as it is collected from multiple web pages, which can lead to data incompatibility.  It is critical to have data that is compatible in order to execute an accurate analysis. Web data integration technology provides a clear view of data with a unified framework. This approach allows users to manage different types of data in a single location.
• Data normalization: Monitoring a dataset that contains data from multiple sources and organizing it according to the rules set by the organization is not easy for its users. Data normalization assists businesses in organizing their data so that it seems consistent across all fields and datasets. Data normalization is performed on a regular basis using web data integration technology in order to optimize the usage of datasets. The benefits of data normalization include:
  • It makes the dataset more comprehensible and improves data integrity. Anyone who integrates with data can easily understand and work on it. 
  • Removes duplicate data from a dataset.
  • Normalization assists organizations in organizing their data in a dataset and eliminates redundancy and irrelevant observations in the dataset.

Web data integration process

1. Identify web sources before data extraction

Finding the correct web sources that provide business insight is the first step before gathering data. 

We summarized three essential criteria to consider when choosing a data source:

• Evaluate the quality of data source:
  • Is the data accurate in every aspect, and does the data source include enough information?
  • What is the extent of the information? 
  • Is it up on par with your expectations?
  • Is it truly necessary for you to have this information?

• Make sure data is updated on a regular basis: Data begins to age from the moment it is created. Make sure you collect the most up-to-date information. It enables businesses to conduct real-time or near-real-time analysis.

• Data completeness: Determine which data is essential to your business’s success. This will help you understand if there is missing information when you collect data from any source. 

2. Extract web data 

After target web data sources are identified, the next step is to extract data from its sources. A web scraper is a specialized tool that extracts data from websites automatically. It also prepares and organizes extracted data so that it could be easily analyzed.

If you want to learn more about web scraping and its use cases, feel free to read our comprehensive article on the topic.

To automate and simplify data extraction, you can also use an ETL (extract, transform, load) solution instead of a scraping tool. In web pages, data can be stored in both structured and unstructured formats. For the preparation step, the ETL tool cleans data by removing duplicate data and whitespace. It gives organizations better control over data, prevents data silos, and allows them to access data in a single, centralized location.

3. Prepare web data  

1. Finding and combining all relevant web data,
2. Data issues are found and fixed in order to provide an accurate dataset. Unnecessary and extraneous values are removed, missing values are handled,
3. Data is translated into a standardized format,
4. Data is prepared and stored in a data repository, such as a data lake or warehouse.

4. Integrate prepared data with APIs

Standardized data is integrated with APIs. It provides businesses with seamless connectivity between their internal systems and applications. Data is shared across applications without the need for human intervention.

For more on web scraping

To explore web scraping use cases for different industries, its benefits and challenges read our articles:

• The Ultimate Guide to Web Scraping Challenges & Best Practices
• Top 7 Web Scraping Best Practices You Must Be Aware of

For guidance to choose the right tool, check out data-driven list of web scrapers and data integration solutions to find the best vendor for you and reach out to us: