Online Data Warehouses and Analytical Processing
Q 1: Data Warehouse and Operational Database
A data warehouse refers to a system designed for pulling together data (large volumes of information) from a wide range of sources that an organization uses for analysis and reporting. It uses reports with complex queries to make different business decisions. A data warehouse is also seen as a consolidated logical or physical repository of data gathered from various systems. The primary focus or purpose of a data warehouse is to store large volumes of historical information. It also used for OLAP (Online Analytical Processing), which uses various complex queries to evaluate rather than process transactions (of data) (Rostek, 2010).
On the other hand, an operational database refers to an instance of a database that updates or creates large chunks of information in real-time. The creation or upgrade of large data is based on various technologies that bolster the speed, availability levels, recoverability, concurrency, and data integrity required a particular set of data. Some of the standard operational databases include sensors, machine data, science, IoT (Internet of Things), communications, media, applications, user interfaces, and transactions (Dozier, 2002). Don't use plagiarised sources.Get your custom essay just from $11/page
Despite their differences, an organization might need both a data warehouse and an operational database for many reasons. Both of them are relational data systems that can help the organization store large volumes of historical data as well as current store transactions and track specific transactions for its business operations.
Q 2: Amazon
OLAP has many valuable uses that can benefit any online retailer like Amazon in many ways. Such firms combine both IT and business functions in their operations, so they prefer systems that can blend both needs, and OLAP is one of the best bets for such a task. From a business standpoint, OLAP enables intuitive and fast access to a wide range of data and related calculations for reporting and analysis. For that reason, Amazon can use it to project returns, profits, losses, and consumer trends to make informed decisions to make on driving its business operations. For its IT needs, Amazon can use OLAP to enhance its data warehouse or other databases with aggregate information and business transactions.
Other areas in which Amazon could use OLAP include analyzing business-related multidimensional data and carrying out business-related calculations. OLAP can also help Amazon tap trustworthy information and calculations to help boost and upstage its online retail business. In addition to the speed-of-thought analysis, OLAP can also help Amazon carry out self-service, flexible reporting. The continued use of OLAP by Amazon and other online retailers is likely to have them gain some valuable insights from their analysis. In addition to enjoying the benefits of OLAP, they are also expected to learn that this system reduces the demand or need for other IT resources.
Q 3: Capacity and Constraint Management
One of the widely used concepts of capacity is that it refers to the highest volume of a product or a unit of products that can be manufactured by a firm, production system, capital stock, a specific technology, and industry, among other production factors. Capacity is underpinned by several concepts which, although outlined different tenets, share some central characteristics. They include identity and power, collective ability, a phenomenon of systems, potential, and creation of public value. Contrary to the widespread notion that capacity is limited to production, it can also boost both the operations and supply chain of the company.
On this issue, UL (2013) posits that capacity building addresses systemic issues and fosters the introduction of new changes that the company can use to upstage its productivity level, drive greater employee commitment and loyalty, and ensure improved financial results. In the case of the flow of products in and out of the company, UL (2013) describes the capacity building as a tool for supply chain sustainability that can help a company handle its supply chain management drawbacks through effective and consistent improvement. Capacity also fosters an in-depth understanding of the factors that cause noncompliance, assisting the management in identifying solutions to various problems affecting the organization across different sectors. (UL, 2013)
Q 4: Location Strategies
Decisions regarding the company’s selection of a location involve a perfect balance of several factors. One of the more pressing factors that have to be considered in selecting a place is the availability or nearness to raw materials. Carr (2012) explains that if a company is focused on manufacturing units where raw materials are turned into finished products, then it should select a location with many raw materials, especially in a place they come cheap. The other factors that formulate a company’s decision on a location strategy include closeness to the market, availability/cheapness of labor, availability of transport facilities, and availability of water, power, and fuel, among others.
A location strategy helps the company run its operation in a place where it can grow without much struggle or having to incur hefty costs. In addition to that, a location strategy is essential to a company in many ways, one of which is an increased ability to attract and retain talent. In most cases, organizations select cities for a location, so they get to access many international, professional, and young people to join their workforce. The other benefits of a location strategy to an organization include access to a network of connected businesses, improved regulation of taxes, and increased growth.
References
Carr, R. (2012). Location Strategy: Do Downtowns Go With Dentures? National Real Estate
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Dozier, C. (2002). Embedded in-memory databases: IMDBs remove complexity barriers.
Computer Technology Review, suppl. Storage Inc., 34-37.
Morgan, P. (2006). The Concept of Capacity. May: European Center for Development Policy
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Rostek, K. (2010). Data Analytical Processing in Data Warehouses. Foundations of
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- (2013). Using Capacity Building to Drive Continuous Improvement in Global Supply
Chains. Retrieved Jan 28, 2020, from UL: https://legacy-uploads.ul.com/wp-content/uploads/sites/40/2015/02/UL_WP_Final_Using-Capacity-Building-to-Drive-Continuous-Improvement-in-Global-Supply-Chains_v11_HR1.pdf