In case you missed the first part of this series, where we went through data validation techniques, you can read it now: A Practical Guide To Web Data Extraction QA Part I: Validation Techniques
When it comes to web scraping at scale, there’s a set of challenges you need to overcome to extract the data. But once you are able to get it, you still have work to do. You need to have a data QA process in place. Data quality becomes especially crucial if you’re extracting high volumes of data from the web regularly and your team’s success depends on the quality of the scraped data.
Whether you are managing a hedge fund trying to find innovative sources of alpha or are an analyst looking to future proof your company’s financial investments, as big data continues to disrupt the investment research landscape, getting on top of these alternative datasets as early as possible is the key to capturing the immense alpha left in this data.