Data Accuracy Matters: Web Scraping for Businesses
Data quality can seem straightforward — after all, isn’t it either correct or incorrect? Not necessarily.
Information may have a wide range of consistency problems that go beyond the true or false binary. This is especially important while web scraping. When you need tons of information for your project, getting reliable, high-quality data is critical to success.
The significance of data accuracy cannot be overstated because it is one of the most critical aspects of its quality. All data you have must be reliable, as it is this that lends prestige to the business. These parameters must be precise and presented in a clear and coherent manner.
To ensure that all information is correct, some discipline and absolute precision are required when web harvesting. Let’s look at why data quality is essential for your company and how it can help you increase your revenue.
The Consequences of Inaccurate Data
Data means facts — namely, the what, how many and why on any given subject. Actually, scratch that — data is the subject. This concept is more applicable to digital archives of past events that software can access and large enterprises can use to their benefit.
Data quality can mean the gap between a project being discontinued and the company gaining a significant competitive advantage in a market.
Take the World Financial Meltdown of 2008, for instance. The 2008 financial crisis, which was one of the worst in history, was sparked by faulty evidence that overstated the value of mortgage-backed securities, collateral debt obligations, and other commodities. Key financial companies such as Bear Sterns went bankrupt when the subprime mortgages that created these derivatives defaulted and their true value became clear. Across the globe, the collapse resulted in mass evictions, foreclosures, and employment cuts.
Ten years later, only men were suggested by Amazon’s AI-powered recruiting app. Amazon, like many other big corporations, was looking for software to help the HR department scan applications for the right applicants. To that end, they began developing AI-powered recruitment tools in 2014. There was only one problem: the software favoured male candidates more than female candidates.
The system’s machine learning models were conditioned on ten years’ worth of resumes sent to Amazon, the majority of which were from men. As a result of the training data, the program began to penalise resume phrases, including the word “women’s” and even ended up downgrading applicants from all-women universities.
However, the corporation attempted to neutralise the platform but eventually concluded that it couldn’t ensure it wouldn’t discover any biased method of sorting applicants and eventually abandoned the campaign.
Smart Decisions Create Successful Businesses
Simply holding that data does no longer suffice. The information must be true-to-life. Meaning, data must also be an accurate reflection of reality. The old adage of ‘you reap what you sow’ highlights the importance of having credible and precise data when doing market analysis.
Many common market analytics need data accuracy as a founding principle. However, supplying accurate and timely data for these assessments means a good foundation: a data governance culture that is both robust and persistent. Keeping these factors in mind, you can improve your business’ ROI, stay on top of time and generate customer satisfaction.
Your marketing team will be able to provide the right ads at the right time and in the right place if they have accurate and reliable details on the clients. This will help your marketers drive potential customers further in the product experience and end up in a higher rate of return on data holdings.
To ensure data integrity, among other aspects of data quality, this initiative would necessitate dedicated resources at all levels of the company. However, the investment is worthwhile because it not only allows for accurate market analyses but it also increases stakeholder approval of the results.
How to Use Web Scraping Wisely
When starting a scraping project, make sure to spell out all of the conditions for the information you’ll be getting, such as consistency and coverage. Your data quality specifications should be precise and testable, allowing you to compare data to specific standards.
1. 404: Data Not Found
The location of targeted information on complex web pages may be difficult, and the auto-generated Xpath might not be precise enough. Bots that struggle to get full data sets face a problem when visiting pages that load more content while a user scrolls down the tab. Pagination keys, which the bots are unable to press, may also pose a problem with finding the right information. All of this leads to inaccurate data retrieval which necessarily involves extra quality assurance.
2. Know Your Sources
The sites you use for data collection have an impact on the accuracy of the information you get, so use specific, trustworthy websites and web pages. Find out what exactly it is that you are allowed to scrape, then consider whether you actually need the extracted information you find on a specific website.
3. Websites a-changing
Modern websites are seldom plain in design. The bulk of tools have been evolving for years, and various aspects of them may have varying architectures. Furthermore, as technology and patterns change, sites make subtle changes to their configuration that can cause the scraping process to malfunction. You should always keep an eye on your scraping and parsing bots during the project to make sure they’re running smoothly to ensure correct data extraction.
4. Stay Efficient
When you scale the web scraping tools, it’s critical that the quality assurance of the gathered data keeps up, particularly if data quality assurance is limited to visual comparisons of the scraped page and manual inspections.
As a side note, if you start gathering new types of data (for example dates or images), make sure each type or format has its own validation methods. Quality assurance isn’t a “one size fits all” type of deal.
Data Accuracy Means Good Practices
The aim is to provide high-quality data. The end result is data consistency. Your business would be in a stronger position to successfully venture into the future if it has the best human and technical capital. Web scraping ensures that you collect all of your information, however, it remains up to you whether the process of web harvesting provides the accurate results that best suit your company.
Data accuracy is critical for ensuring that the data collection is reliable and straightforward. Dealing with low-quality or messy data makes the marketing team’s work even more difficult and limits the accuracy of their analysis.
Now that you’ve got accurate web data extraction down, it’s time for you to get started with your scraping journey. I recommend you try the freemium plan provided by WebScrapingAPI.