Data is usually stored in any business purposely as a reference or to be used in making decisions. It is mandatory for any business to gather adequate data for it’s a day to day application. Web scraping can be used in the creation of data warehouses that are important to the success of any business. In this article, we explore the creation of a warehouse and the generation of data to knowledge through information.
Web Scraping (sometimes referred to us as data extraction or knowledge discovery) is the process of analyzing information from different perspectives and then summarizing it into useful and important information that can find valid applications in improving revenues, reducing costs, or even both. Web scraping is usually regarded as one of the best tools used for analyzing data and summarizing the relationships that have been identified. Technically we can conclude web scraping is the process of finding correlations or patterns among numerous fields in large databases that are related.
Web scraping is relatively a new term, but it is important to understand that technology is not. This is because companies have used supercomputers in sifting through large volumes of supermarket scanner data and thereby analyze market research reports for a number of years. The continuous innovations in web harvesting are greatly increasing the level of accuracy on the analysis and at the same time driving down the cost.
In the web scraping process, there have been realized dramatic advances in data capture, data transmission, and storage capabilities in the integration of the various databases into data warehouses. Data warehousing can be defined as a process of centralized data retrieval and management. Data warehousing is a relatively new term although it has been practiced for many years. It represents an ideal vision for the maintenance of a repository of all the organizational data. It is important to note that the centralization of data is usually needed in order to maximize user access and analysis. Dramatic technological advances are making the web scraping vision a reality for many companies. Any advance in the data analysis promotes greatly the validity of web scraping process.
For example, one Indian Valley grocery chain used the web scraping process to analyze the local buying patterns of their customers. They came to discover that most many bought diapers on Thursday and on Saturdays. They also realized that they also tended to buy some beer. Further analysis from the web scraping process showed that the shoppers usually did their grocery on Saturdays for the week. On Thursdays, however, they only bought a few items. The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain could use this newly discovered information in various ways to increase revenue.