If you aren’t using a machine-learning-driven intelligent Web scraping solution yet, here are three reasons why you might want to abandon that entry-level Web-scraping software or cut your high-cost script-writing approach.
- You need to keep an eye on a large number of web sources that get updated frequently.
- Understanding what’s changed is at least as critical as the data itself.
- You don’t want maintenance and scheduling to drag you down.
Here’s what an intelligent Web-scraping solution can deliver – and why:
1. Better data monitoring of an ever-shifting Web
If you need to keep a watch over hundreds, thousands or even tens of thousands of sites, an intelligent Web scraper is a must, because:
- It can scale – easily adding new websites, coordinating extraction routines, and automating the normalization of data across different websites.
- It can navigate and extract data from websites efficiently. Script-based approaches typically only can view a Web page in isolation, making it difficult to optimize navigation across unique pages of a targeted site. More intelligent approaches can be trained to bypass unnecessary links and leave a lighter footprint on the sites you need to access. And, they can monitor millions of precise Web data points quickly. This means you can monitor more pages on more sites with more frequent updates.
2. Critical alerts to Web data changes
A key sales executive suddenly drops off of the management page of your main competitor. That can mean big shakeup in the entire organization, which your sales team can jump on.
An intelligent Web scraper can alert you to this personnel shift because it can be set to monitor for just the changes; less powerful technologies or script-based approaches can’t. Whether you’re tracking price shifts, people moves, or product changes (or more) intelligent Web scraping delivers more profound insights.
3. Maintenance may become your biggest nightmare
You’ve purchased an entry-level tool and built out scrapers for a few hundred sites. At first, everything seems fine. But, within weeks you begin to notice that your data is incomplete and not being updated as you’d expected. Why did your data deliveries disappear?
Reality is that these low-cost tools are simply not designed for mission-critical business applications – on the surface they look helpful and easy to use, but underneath the surface they are script-based and highly dependent upon the HTML of a website. But websites change, and entry-level web scraping tools are simply not engineered to adapt to those changes.
And, most of these tools are simply not designed for enterprise use. They have limited reporting, if any, so the only way to know whether they’re successfully completing their tasks is by finding gaps in the data – often when it’s too late.
An intelligent web scraping approach doesn’t rely upon the HTML of a web page. It uses machine learning algorithms which view the web the same way a user might. A typical reader doesn’t get confused when a font or color is changed on a website, and neither do these algorithms. But simple approaches to web scraping are highly dependent on the specific HTML to help it understand the content of a page. So, when websites have design changes (on average once every 18 months), the software fails.
While entry-level web scraping software can be an easy solution for simple, one-time web scraping projects, the scripts they generate are fragile and the resources required for tracking and maintenance can become overwhelming when you need to regularly extract data from multiple sites.
Case in point: Shopzilla assimilates data five times faster than outsourced Web scrapers
To demonstrate the power of intelligent Web scraping, here’s a real-life example from Shopzilla. Shopzilla manages a premier portfolio of online shopping brands in the United States and Europe, connecting more than 40 million shoppers each month with millions of products from retailers worldwide. With the explosive growth of retail data on the Web, Shopzilla’s outsourced, custom-built approach, based on scripting, could not add the product lines of new retailers to its site in a timely fashion. It was taking up to two weeks to write the scripts needed to make a single site accessible.
By deploying Connotate’s intelligent web scraping platform on site, Shopzilla gained the ability to harness Web data’s rapid growth and keep up to date. Today, new sources are added in days, not weeks. The platform continually monitors Web content from thousands of sites, delivering high volumes of data every day in a structured format. The result: 500 percent more data from new retailers. An added bonus: the company has reduced IT maintenance costs and its dependence on outsourced development timetables.
Case in point: Deep competitor intelligence in two languages
A global manufacturer needed to monitor competitors’ technology improvements in a field where market leadership hinges on an ability to quickly leverage these advances. That meant accessing scholarly journals and niche sites in multiple languages. Using the Connotate solution, it was able to access highly-targeted, keyword-driven university and industry research journals and blogs in German and English that are hard to reach because they do not support RSS feeds. Our solution also incorporated semantic analysis to tag and categorize data and help identify new technologies and products not currently in the keyword list. The firm enhanced its competitive edge with the up-to-the-minute, precise data it needed.
ARTICLE SOURCE: This factual content has not been modified from the source. This content is syndicated news that can be used for your research, and we hope that it can help your productivity. This content is strictly for educational purposes and is not made for any kind of commercial purposes of this blog.