Web sites are an integral part of the technological world today. And the rate at which the websites are evolving over the years has been phenomenal. As per the inputs from the website Internet Livestats, there are more than one billion websites in the internet, which is clearly an indication of the exponential rate at which the websites are being added every second to the internet.
The use of the web sites to derive data is also growing like never before, across countless sectors. Companies need data for various purposes like obtaining new customers, tracking industry trends, analysis for business purposes, understanding government regulations and more.
According to a recent case study released by IBM on Big Data analytics,
- ·Over 1 billion Google searches happen every day and over 294 billion emails are sent every day.
- ·Trillions of sensors monitor, track and communicate with each other, populating the Internet of things with real-time data.
- ·Facebook access, analyzes and stores 30 + petabytes of user generated data while Twitter deals with 230 + million Tweets everyday
A result of this mercurial growth of this high volume data, also referred to as ‘Big Data’, the process of extracting, maintaining and tracking the required web data for its productive use is posing challenges – The primary obstacle being the inability to obtain data from secure, trustworthy websites at a faster rate for online research.
The speed, consistency and reliability are the other key factors which are at stake during the process and overlooking them often leads to redundancy. Handling large volumes of data also leads to inefficient processing, as it becomes increasingly difficult to extract manually.
Automating the web data extraction is obviously the best approach and many organizations are leading the way in finding path-breaking solutions to achieve a reliable way to achieve automation. It is extremely beneficial for harvesting structured information with specific data types. Also, website structure changes are monitored, providing access to the right data at desired intervals.
Automation in web data extraction, thus results in a reduction of redundancy, elimination of the manual errors, cost overhead. The web data extract is more precise and reliable as the extraction tools are equipped to handle high volumes of data of a wide variety on a consistent basis. The resulting collated structured data from existing websites makes it easy for the disparate systems to consume the data.
- Every industry has its own requirements for data extraction. For eg: a healthcare company would want to extract data pertaining to latest trends in healthcare industries and government regulations for medicines and healthcare practices. In a retail industry the requirement would be more focussed on the understanding pricing of competitor products in the industry. The challenge hence lies is understanding the needs of the customer and provide the required data efficiently in the easiest way possible.
- The bottom line is that data extraction requirements will go through the roof in coming years, and various kinds of technologies will evolve in space of web data extraction. Customization would become more important and rather than designing a product, a data extraction platform is something which will become the need of the hour. The platform should be able to cater to various kinds of clients, with easy plug and play integration with the client systems. The output data can be fed into other enterprise systems of the clients such as Web Analytics, CRM, & marketing automation. By usage of AI, data visualization, analytics (text based, image based) customers can make sense of the humongous amount of extracted web data.
By helping organizations eliminate their IT and manpower costs, automation of web data extraction will become the front runner in aiding the growth of Big data based enterprises and other data driven businesses in near future.