Web Data scraping, AI & Automation: Made for each other

1. Web Data scraping, AI & Automation: Made for each other Automation and Artificial Intelligence (AI) are the hot topics in the tech world today. Research reports state that AI is going to take over the world and that will change the world faster than ever before. If we look at the e-commerce world, it is heading towards building an artificially intelligent digital commerce platform. The new e-commerce platform could intelligently recommend most preferred products to the customers. And, if we look at the key source of developing artificial intelligence is data – properly trained data. There are various sources of data. One key source of data is the internet and there are various methods to crawl data from the internet. Automated extraction of data from various websites can be termed as ​web data scraping. Now, let us look at a few aspects of automation in web data scraping or web data crawling. Why is automation needed for web data scraping? When automation is synced with any term — the thought is fast, accurate and flexible. According to the World Wide Web survey, there are nearly 5 billion websites in 2018. Is it possible to access data manually from all those sites? It is unrealistic. This is when the importance of automated tools to get the data comes into the picture. There are numerous automation tools from which one can scrap data. R and Python are the two major open source software tools used for automated web data scraping.

2. Here the bot should be able to navigate through different pages and should collect the data. The thing is, different websites use different navigation systems which results in complexity, so the developer, writing web data crawling bots should have sound technical knowledge. There should be a minimal human interference once the machine is programmed. We can find dozens of coding languages in use today. But, we need to identify the best coding language which gives us the maximum automation efficiency. Python is top-ranked open source programming language used by Reddit, Instagram, Venmo according to Coding Dojo press. And has become extremely popular among data scientists. Web Data Scraping: Steps to Follow ● ● ● ● ● ● ● Analyse the source data and build custom script Crawl Data using right scripting language Store crawled data in desired format Use right tool to perform cleansing, deduplication, formatting, & analysis Store output results from analysis Present visualization of results using suitable BI tool Periodic web data crawling – as per the business needs Automated web data scraper resolves the problems of big data and human errors, by reducing the time and effort it takes to solve manually. The major advantages of automated web scrapers are, they save costs of manpower and man-hours required for the same job if it’s done manually. For some straight forward web data scraping, software could do it much faster compared to humans, which is pretty obvious. Once a web data scraper is deployed with the proper mechanism, it efficiently extracts from every single source. This is a highly probable human error. To summarize, application of AI and automation in ​web data scraping is immense. The consistency of web data scrapers is unmatchable with the humans; they will continue to extract data until it gets over or it is instructed to stop. Web data scrapers do not require a lot of maintenance over a long period of time, which adds to its value. There are boundless potential for improvements in web data scraping automation leveraging AI and hence, the scraping tools become truly intelligent.

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