How to Automate Supply Chain Risk Reports: A Guide for Developers
Do you use Python? If so, this guide will help you automate supply chain risk reports using AI Chat GPT and our News API.
We live in a world with an ever-growing wealth of data, much of it available on the open web. Data also continues to accumulate in deep and dark places of the internet. Web data extraction allows companies in different industries to monitor relevant information on the open, deep, and dark web. They can use different types of web data — usually through web data integration platforms — to generate actionable insights automatically at scale.
We’ve created this guide to explain what web data extraction is, ways to extract web data, use cases for web data extraction, and the data feeds we offer.
Web data extraction is the process of extracting, transforming, and unifying data from web pages into structured, machine-readable formats. It sometimes involves enriching the extracted data with attributes such as entities, sentiment, types, and categories. This structured data is used for specific business use cases or research purposes.
For example, a venture capital company could use web data extraction to gather data from websites with consumer reviews or online discussions. A team would analyze the data to discover shifts in public opinion about a specific company and predict future performance. VC leaders could then factor performance predictions into their decision on whether to invest.
The three most common ways to extract data from web pages include:
The DIY approach means you build a web crawler in-house using your preferred language, e.g., Python, Ruby, or JavaScript. This approach gives you complete control – you choose how much data to scrape and how often to scrape it. A DIY web crawler requires technical skills, so companies usually turn to web developers to build it. We discuss the DIY approach to web data extraction in this white paper.
Many ad-hoc web scraping tools are available today, with prices and features varying widely. These tools automate parts of the extraction process. Some tools consist of basic automated scripts, while others use advanced technologies like machine learning. Some of them require developer involvement — e.g., to manage lists of websites to crawl and maintain the scraping tool. Ad-hoc scraping tools don’t scale well, and many include more features than you need, making them a less cost-effective option for most projects.
A web data provider, also known as a Data as a Service (DaaS) provider, allows you to extract web data without having to build any infrastructure or a web scraping system. You instead purchase the web data you need for your platform or application. DaaS solutions provide broader data coverage and far greater scalability than ad-hoc scraping solutions.
Most DaaS vendors provide data feeds through APIs, making integrating web data with platforms and applications easy. While DaaS solutions offer scalability for larger operations, you typically need to work with the DaaS provider to customize the data feeds.
To learn more about which extraction approach would work best for your business, download our Web Data Extraction Playbook.
You can use web data extraction to obtain relevant data for a wide range of use cases, such as:
You can achieve many business goals through web data extraction for brand monitoring, such as:
Other things you can do with brand monitoring include gauging consumer sentiment, improving engagement with customers, and identifying user-generated content involving your brand.
Traditional and alternative web data contain hidden signals and insights, giving companies a knowledge advantage that enables them to:
This is not a comprehensive list — you can do even more with access to a wide range of web data for competitive intelligence.
Outmaneuvering your competitors requires effective market and product research, which means conducting research not only with Google searches but also by using open web data. Extract relevant data from online review sites, blogs, and forums to discover how customers view your products. Learn how they feel about product changes, value for the price, and overall satisfaction. You can also use open web data to perform market research, such as monitoring pricing trends over time, keeping an eye on your competitors, and determining consumer demand for specific products.
If you want your business to succeed, you need to know how your customers feel about your brand. Many customers express their thoughts about nearly everything online, including products and services. You could leverage that public data for sentiment analysis, using it to achieve various business goals. For example, a restaurant chain could analyze user-generated content that mentions elements of the customer dining experience, such as food quality, service, value for the price, locations, and overall ambiance. Armed with relevant open web data, the chain could provide an even better experience for diners leading to more returning customers and higher revenue.
Breaking news and current events — local and global — can potentially impact your business positively or negatively. You can use web data extraction to monitor news content for specific keywords or create a personalized news feed based on your interests. For example, an investment firm could monitor news content for keywords related to a current global economic recession. The data would provide valuable insights that help investors determine opportunities or risks regarding investing in specific companies or markets.
Organizations must effectively track new and changing regulations or they increase their risk of compliance failures. For example, organizations worldwide face potential Anti-Money Laundering (AML) and Know Your Customer/Business (KYC/B) compliance violations, which can lead to financial fines amounting to millions of dollars. By extracting data from relevant public government websites, companies can continuously track changes in laws like KYC/B and AML. They can better monitor compliance risk and ensure they comply with current regulatory requirements, avoiding hefty financial penalties. Organizations should also monitor public data about different companies, analyzing the data against existing law. For example, a financial services provider might discover a competitor currently faces AML fines or a sneaker brand might find a rival embroiled in a government-led legal case.
Every company operating online faces a wide range of digital threats, which includes data breaches, phishing attacks, cloud-based service attacks, and ransomware. When bad actors succeed in breaching systems or applications, they typically sell or trade companies’ sensitive data via dark web hacker forums, chat apps, or paste sites. Many hackers will plan cybersecurity attacks far in advance, discussing their plans with others on the dark web. By monitoring dark web data, you can discover digital threats to your business early and identify new and emerging trends in cybercriminal circles.
Companies today face many threats from outside and within the business. For example, a stock trading business could find a malicious insider selling sensitive company information to third parties or extremists on alternative social media sites making threats against executives and VIPs. Corporate travelers risk exposure to threats due to crime or terrorism at their destination. You could extract data from dark web sources to discover leaked company information. And using data from sites across the deep, dark, and open web, you could detect threats to high-risk executives and create travel and site security assessments for corporate travelers.
Specialty platforms and web data products need to incorporate varied web data, and lots of it, for their customers to succeed. And companies worldwide use Webz.io as their go-to source for structured data from the open, deep, and dark web.
Webz.io collects data from open, dark, and deep web sources. We provide this data in the form of feeds, most of which we make available through REST APIs. Using our APIs, platforms, and applications can generate relevant insights at scale. Here are brief overviews of our API products:
Archived Web Data API — This API is ideal for AI modeling, where you need a massive volume of web data to train AI or NLP models. The API provides access to historical data from news, blogs, online forums, and reviews across the open web going back to 2008.
Web data can help you and your customers address a wide range of concerns — from thriving in a highly competitive market and increasing brand loyalty to protection from cybersecurity threats and staying on top of compliance risks. With Webz.io, you can provide the scalable big data or specialty technology solution your customers need. And getting started with web data extraction is easy — pick a Webz.io API (or multiple APIs) for your use case, plug it into your platform or solution, and go!
Want to know how Webz.io can help you make the most of web data extraction? Contact us to speak with one of our web data experts (or DaaS experts).
Do you use Python? If so, this guide will help you automate supply chain risk reports using AI Chat GPT and our News API.
Use this guide to learn how to easily automate supply chain risk reports with Chat GPT and news data.
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