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BI Data Analyst
Business intelligence (BI) comprises the strategies and technologies used by enterprises for the data analysis and management of business information.[1] Common functions of business intelligence technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.
BI tools can handle large amounts of structured and sometimes unstructured data to help identify, develop, and otherwise create new strategic business opportunities. They aim to allow for the easy interpretation of these big data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability, and help them take strategic decisions.[2]
Business intelligence can be used by enterprises to support a wide range of business decisions ranging from operational to strategic. Basic operating decisions include product positioning or pricing. Strategic business decisions involve priorities, goals, and directions at the broadest level. In all cases, BI is most effective when it combines data derived from the market in which a company operates (external data) with data from company sources internal to the business such as financial and operations data (internal data). When combined, external and internal data can provide a complete picture which, in effect, creates an "intelligence" that cannot be derived from any singular set of data.[3]
Among myriad uses, business intelligence tools empower organizations to gain insight into new markets, to assess demand and suitability of products and services for different market segments, and to gauge the impact of marketing efforts.[4]
BI applications use data gathered from a data warehouse (DW) or from a data mart, and the concepts of BI and DW combine as "BI/DW"[5] or as "BIDW". A data warehouse contains a copy of analytical data that facilitates decision support.
Reading (/ˈrɛdɪŋ/ (audio speaker iconlisten) RED-ing)[2] is a historic large market town in Berkshire, England, in the Thames Valley at the confluence of the rivers Thames and Kennet. It is on the Great Western Main Line railway and the M4 motorway, 40 miles (64 km) east of Swindon, 25 miles (40 km) south of Oxford, 40 miles (64 km) west of London, 15 miles (24 km) north of Basingstoke, 13 miles (21 km) southwest of Maidenhead and 15 miles (24 km) east of Newbury. Reading is a major commercial centre, especially for information technology and insurance.[3] It is also a regional retail centre, serving a large area of the Thames Valley, and home to the University of Reading. Every year it hosts the Reading Festival, one of England's biggest music festivals. Among its sports teams are Reading Football Club and Reading Hockey Club, and over 15,000 runners annually compete in the Reading Half Marathon.
Reading dates from the 8th century. It was an important trading and ecclesiastical centre in the Middle Ages, the site of Reading Abbey, one of the largest and richest monasteries of medieval England with strong royal connections, of which the 12th-century abbey gateway and significant ancient ruins remain. By 1525, Reading was the largest town in Berkshire, and tenth in England for taxable wealth. The town was seriously affected by the English Civil War, with a major siege and loss of trade, but played a pivotal role in the Glorious Revolution, whose only significant military action was fought on its streets. The 18th century saw the beginning of a major ironworks in the town and the growth of the brewing trade for which Reading was to become famous. The 19th century saw the coming of the Great Western Railway and the development of the town's brewing, baking and seed growing businesses, and the town grew rapidly as a manufacturing centre.
