Extracting data from internet-based sources is critical in a range of industries. In many cases, the data is available through APIs. However, there are also many instances where data scientists and engineers will need software to handle extracting data from web pages, PDFs, and other sources.
People are unclear sometimes on why this is necessary, though. Here are four reasons why organizations may use web data extraction software.
Browser-Only Features
Some data only exists in web-only interfaces. Websites often do this for obfuscation purposes. They may want to limit access to the data without explicit blocking. Also, some website deployments use asynchronous fetches and on-page scripts to compile data presentations.
In these cases, users need software that can drive a browser and navigate these complexities before collecting the data. Organizations also usually need to automate this process so they can collect data from many pages or sites. No human can keep up with this pace so data extraction tools become essential to the job.
Poorly-Formed or Unstructured Data
Even if an API is available with the desired data, you might find it poorly formed. You will face the challenge of normalizing the data and saving it in your preferred format. While you can often test the process by hand, you’re also likely going to want a setup that can automate the task. You’ll need software that can follow predefined patterns to reformat and store the data.
There are also many scenarios involving unstructured data. In some cases, unstructured data is present because the publisher never meant it as data. Suppose you’re scraping websites for sentiment analysis. The software needs to convert articles, comments, reviews, and social media posts into data points. This usually includes devising a scoring formula so you can make derive statistics.
You may also encounter cases with unstructured data because it wasn’t meant for automated analysis. If you pull data tables from PDFs on government websites, for example, you’ll often end up with messy data at best. You will have to impose your preferred structure on the data so you can use it.
Regulatory Compliance
Most organizations will bump into regulatory compliance issues as they collect data from the internet. California and the European Union are notable protectors of consumer data. The U.S. federal government’s HIPAA rules on medical data privacy can also be challenging to confront. Running afoul of these regulators can generate fines in the millions of dollars.
Your organization will want to avoid or resolve as much regulatory risk exposure as possible. The right data extraction software should give you the means to avoid regulatory shortfalls. It also should produce logs and reports that allow you to hunt down any potential failures. Robust data extraction practices can foster regulatory peace if a party raises concerns.
Persistent Monitoring
Many extraction processes exist for monitoring purposes. A financial services firm, for example, might want to track market sentiment across a broad spectrum of channels. Once more, the job requires automation software that can navigate many interfaces and data standards.
Monitoring tools often need to be speedy and responsive compared to collection software. A company monitoring price changes on websites so it can pick the perfect time to buy products or make trades needs a snappy system.
The software doesn’t have to only be good at parsing the data. It also has to be lean and speedy so it can notify decision-makers of changes. At firms that are automating the process down to letting machines make the decisions, the monitoring has to be flawless. Otherwise, there’s a risk that the entire process could go off the rails because the extraction tools fed bad data into the model.
Conclusion
Web data is a trove of opportunities. Your ability to leverage the available data will depend on your software suite. By choosing the right software, you can collect the needed data for a wide range of purposes.
Competent and speedy extraction can be a massive competitive advantage. It will differentiate your business even in industries that have powerful incumbents. Many operations can build these advantages into complete business models. This allows them to sell data as a product, leverage it for decision-making, or offer it as a service.
