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Web Data Integration for Business: What you should know

Susanne Morris

Susanne Morris

February 08, 2021

Web integration goes beyond traditional web scraping to provide hidden insights to businesses and analysts that are not easily readable by human end users. Web data integration is the process of acquiring and transforming data from multiple websites into one cohesive workflow. Many businesses have turned to web data integration for more sophisticated solutions to data quality and unpacking the potential of the web data life cycle. Ultimately, this process includes data extraction, transformation, standardization, API integration, and data mapping.

With an estimated 175 zettabytes of data predicted to be created by the year 2025, understanding the data lifecycle is becoming increasingly important for financial experts each year. Likewise, as businesses work to understand data, data experts are working to create new ways for people and businesses to harness this data influx. Methods such as web scraping have created a revolution in the data industry; however, with 2.5 quintillion data points created daily, experts need to find alternative ways to harness the power of this data. This is where web data integration comes into play.

This article will explain the data integration process, its use cases, its benefits, and its integration process. Let’s take a closer look at web data integration (WDI).

Web scraping and beyond

In order to understand the complexities of web data integration, we must first understand the foundation of WDI: web scraping. Web scraping, or web data extraction, is the process of utilizing software to access and extract data from web pages using the Hypertext Transfer Protocol. Hypertext Transfer Protocol, HTTPS, is an application-layer protocol that transmits hypermedia documents, such as HTML, displaying web pages for end-users. Web pages contain a wealth of information in text form; however, they are not easily accessible in their original form, often HTML, and require extraneous steps to unlock insights and find success upon analysis.

Similar to web scraping, web data integration’s ultimate objective is to retrieve data. However, their linkage lies in the fact that web scraping has evolved into a more extensive process: web data integration. Consequently, this data integration process amplifies the web scraping process to also include:

  • Data cleansing
  • Data normalization
  • Performing calculations
  • Unlocking hidden data
  • Custom reporting and analyzation
  • Integration capabilities

Ultimately, web data integration combines the above objectives into one interactive process with five major steps. Continue reading for a breakdown.

The web data integration process

  1. Identify. Before any data extraction can occur, you must identify what type and data sources might provide you with business insights—for example, web sources where information is located. Identify the URL where your data is located.
  2. Extract. Once you have identified and targeted specific data sources, you can begin the web data extraction process or web scraping. Relatedly, prior to starting the web data integration process, you must decide whether or not to process your data in-house or choose to outsource this process.
  3. Prepare. The preparation step encompasses a larger group of smaller data quality adjacent processes, such as cleansing, normalization, and standardization. This step is vital in ensuring that data quality is maintained throughout the remaining integration process.
  4. Integrate. After the data has been normalized and a certain standard of data quality has been achieved, you can now integrate the data into APIs.
  5. Utilize. Lastly, with fully integrated data, you can begin discovering powerful insights from your newly processed data. For example, companies are able to integrate this data for machine learning and other AI business processes.

Web data integration source types

The web data integration process requires extensive data pulled from the world wide web. Though, this data is not all the same. Web data can be extracted from many databases and can originate from many sources. Here are just a few of the different source types utilized during the data extraction process.

  • HTML data tables
  • Web sites
  • Web applications
  • Public data catalogs
  • Government catalogs
  • Semantic web (SPARQL)
  • Online encyclopedias
  • Public PDFs
  • Structured HTML data

Web data integration use cases

Now that we’ve taken a look at the more technical aspects of WDI, let’s unpack some major use cases as well as the advantages of data integration.

Competitive intelligence

Web data integration provides businesses with insights surrounding their competitor’s pricing, public sentiment, and new product/service launches. Specifically, companies can use it to extract public PDFs and data from web applications to analyze their competitors’ pricing, services, and features.

Investment intelligence

Investors and analysts can harness the power of web data integration by analyzing web data integration insights from industry blogs, social media, and news sites. For instance, insights from social media and news websites can inform investors about brand health regarding a potential investment and public sentiment surrounding a start-up in an up-and-coming industry.

Security and risk management

Companies are able to utilize web data integration to track potential risks and scan datasets for any anomalies throughout their analysis for any security breaches. Notably, companies can monitor activity on web applications and government websites to aid in risk management.

Product development

Developers can use web data extraction to find deeper insights regarding timing, features, pricing, and more surrounding the product development process. For example, utilizing information from websites that host product and service reviews can help developers find consumer needs and trending features that may enhance their design or final product.

General analysis

In addition to the various uses for investors, developers, business experts, and financial analysts, web data integration can be used for internal and external operations across many industries. For instance, insights from company-wide datasets can be used to improve workflow and increase business efficiency.

3 benefits of web data integration

1. Increased accessibility

The API capability and integration within the larger web data integration process provide quick connections and more accessibility. For example, structured and normalized datasets available through APIs can provide investors with up-to-date insights during funding periods and throughout the investment evaluation stage.

2. Enhanced insights

Manual web scraping can often miss data and therefore doesn’t provide a full picture for analysis. The iterative and enhanced features of the web data integration process allow for retrieving hidden data in HTML files that aren’t necessarily readable or accessible to human end users.

3. Improved data quality

The identifying and preparation stages of the web data integration process are centered around achieving data quality. For instance, data quality can be gained during the identify stage simply due to the targeted approach of selecting the appropriate sources for enhanced insights.

Closing words

In all, it is critical that businesses understand the importance of web data integration and that other data processing solutions must support web scraping. With a market size of 7 billion in 2020, according to Opimas research, web data integration and web extraction are proving to be standard data practices globally.

Frequently asked questions

What is web data integration?

Web data integration is the process of acquiring and transforming data from multiple websites into one cohesive workflow.

Why is web data integration important?

Web data integration provides businesses with sophisticated data solutions that enhance insights, improve accessibility, and increase data quality.

What is the web data integration process?

The web data integration process includes five steps: identifying potential data sources and types, extracting data, preparing said data, integrating the data into a web API or tool, and utilizing the data for insights.

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