Data Analytics Research- Big Data
Big Data combines data sets where its size, intricacy, and exponential growth make it hard to analyze by current technology, such as analytical data store tools. The current problem with big data is the inability to analyze it quickly enough to be useful for business intelligence. Data sets can be as large as 30-50 terabytes.
Big Data is generated from technologies, such as web logs, RFID sensors, machinery, cars, web searches, social networks, computer, phones, GPS devices, and call center records. In order to use Big Data ERP or CRM systems must be used. New technology is being created to meet the demands for business intelligence, such as parallel processing databases, which simultaneously distributes the process of large sets of data across many servers.
When Big Data is efficiently stored, processed, analyzed, and effectively used, companies are able to gain more understanding of the business, customers, products, and competitors. This increases productivity, which leads to increased sales, lower costs, better customer service, and improved products and services.
Big Data is directly and indirectly incorporated in our lectures because Big Data is discussed in dimensional modeling as being stored in relational databases then transferred to a dimensional database. Because there is a lot of data SQL is used to manage data in relational databases. In the ETL process: extractions, transform, and load; data is stored in data warehouses then subject oriented content is stored in data marts. This data is then summarized and stored in analytical data store. It is loaded into pivot tables that efficiently summarize the data into a data cube allowing businesses to effectively make decisions. This builds on concepts by introducing SAS Enterprise Miner, which mines through massive amounts of the data.
In IBM’s case study of Big Data, Patagonia leverages analytics to track and forecast global inventory and financial outcomes. It uses Big Data to improve their budgeting process and improve merchandising and supply analysis, and planning by forecasting future inventory levels.
Navint. “Why Is BIG Data Important?” Navint. Navint, May 2012. Web. 12 Apr. 2013. <http://www.navint.com/images/Big.Data.pdf>.
IBM. “Patagonia leverages analytics and gains new insight.” Mar. 2013. IBM. 12 Apr. 2013.