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).
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:
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
Web data integration is the process of acquiring and transforming data from multiple websites into one cohesive workflow.
Web data integration provides businesses with sophisticated data solutions that enhance insights, improve accessibility, and increase data quality.
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.