Data Warehouse and Data Mining in Business - Essay Example

Data Warehouse and Data Mining TABLE OF CONTENT 1 INTRODUCTION AND HISTORY 2 MARKET ANALYSIS 2 1Market-Basket analysis 2.1.2 Concept Description 2.1.3 Deviation 2.1.4 Visualization 3.1SYSTEM COMPARISON AND EVALUATION 3.1.1 Oracle Data Mining 3.1.2 Intelligence Miner and SYBASE 4.1 COMPARISON OF UAE AND THE INTERNATIONAL LEVELS 4.1.1 Reliability 4.1.2 Adoptability 4.1.3 Capacity 5.1 RECOMMENDATION 6.1 CONCLUSION Work Cited LIST OF ACRONYMS OLAP- On-Line Analytical Processing ODM –Oracle Data Mining UAE- United Arab Emirate RDBMS – Relational Data Management System 1.1 INTRODUCTION AND HISTORY Past years have seen an increase in data and information in organization. The doubling and increase amount of data lay basis of innovation and need for electronic storage. This has prompted system manager to develop systems that are reliable and secure, this forms of systems are referred to as data warehousing where other aspect is data mining where a curtain pattern and works on it to ensure a desired output is developed. Different levels of data are represented differently. The expert information collected is then used to analyse the market in business and create strategies needed to maintain the market. The choice of the system depends on the firm’s priority the market is flexible and there is need to introduce a database system management that covers is adjustable. 2.1 MARKET ANALYSIS The need to develop and acquire information technology has revolutionized business. Market analysis refers to the study and acquisition of the market trends and activities associated with the market. When analyzing the market, the firm uses various methods. The most effective is data mining and various tools are used to facilitate it. Patterns are developed using data mining where graphic visualization combine with statistical analysis and refinement to produce the desired market tend or activity. The combination of the three provides for a pattern extraction from large sets of data which combine with various factors to provide emerge with a market analysis (Shaw et al, 2001). 2.1.1Market-Basket analysis The method is also referred to as the dependency analysis. Where a relationship is drawn between the acquisition of goods and the client base of the same. This lay basis about the marketing strategies to be deployed by a given firm. It creates room for the firm to develop a strategy that dominates the market in relation to the buying power (Shaw, 1993). Marketers develop strategies that will ensure a steady market for their products. The approach gives room for a well-developed method where there is a balance between goods in that there is a continuity in supply in the market. 2.1.2 Concept Description It involves classifying customers into specified domain. The concept is to allow character summarization to be derived from a subset of data. A marketer always monitors customer trends by using the concept description. This domain and any change on preference identified and action plan taken to cover for the customers needs determine the nature of customers. The customer’s classification depends on various factors including age, gender, income and other social factors. In which different strategies will be determined using this model while introducing a product. 2.1.3 Deviation Commonly referred top as deviation detection, it works to determine when the market seams to change or appear different. By analyzing the market different factors acts as variables and when a foreign factors are introduced them need to adjust comes in mind (Berson & Stephen, 2004). Adjustment is usually done before the factors get compared. the variables usually affects the nature of the market and the comparison of the new element verses the old provides for an opportunity for the firm to adopt measures in order to maintain a market command. The deviation detector allows the firm updates its systems and transfers the development to various departments. 2.1.4 Visualization After analyzing and conducting changes to data there is, need to represent the information on a graph and be presented. This allows each person to relate to the data collected and be able to contribute in decision-making. The process includes proving detailed information graphically without compromising any data. The customer’s data in the approach is converted into graphs to avoid the bulkiness of previous data that seamed complex to represent. The object representation varies in forms and design and depending on software used each can be generally be classified into, two and three dimension. All other concepts and approaches are summarized into the graphs (Berson & Stephen, 2004). 3.1SYSTEM COMPARISON AND EVALUATION The system providers that contain data warehousing and data mining includes Oracle, IBM DB2, intelligence Miner, SYBASE among others. The section will focus on some of the above, analyse their effectiveness, their differences and similarity will lay focus of the discussion. 3.1.1 Oracle Data Mining Among all packages that contain data mining, ODM as it is commonly known is preferred amongst most companies involved in data mining and warehousing. The package contains various factions which include, Naïve Bayes and Association role that performs algorithms, they assist when performing computation during deviation and market basket analysis. It also contains OLAP, which performs all statistical and analytical functions they combine with the multiple algorithms functions to ensure efficiency. The general programming control is enhanced by API, which ensures system control and integrates application (Oracle corporation, 2001). Multiple prediction types ensures that time is saved as it predicts what data should be imputed and prevents double entry while analyzing. The batch scoring modes ensures accuracy and provides for statistical data analysis. It represent them in codes and ensures that a specific output is relayed based on the principles laid this is according to Oracle Corporation (2001). The combination of these main elements ensures the software functions effectively. Its effectiveness ensures that quality is what one gets while using it as a preferred data-mining tool. Its demerits are the fact that it is complicated and its programming only provides for specialist to use it as an analyzing tool. The output is always bulky and needs further representation expert analysis. 3.1.2 Intelligence Miner and SYBASE The software is effective while performing Data warehousing as they provide clustering, they provide for intra- cluster and inter cluster. According to new age publisher, by grouping, the software ensures easy Data Mining. They are preferred in relatively smaller firms as the capacity to handle larger data is unavailable using the applications. They relay on preset applications to perform analytical functions. As compare to Oracle they seam reliable as higher rate of accuracy is registered while using oracle. The ability to handle limited data provides an ideal position for oracle to be used. The compatibility of SYBASE is the only advantage as they integrate well with any given Operating System. 4.1 COMPARISON OF UAE AND THE INTERNATIONAL LEVELS Comparing data mining and data warehousing in United Arab Emirates with the international levels, several factors including reliability adoptability, system capacity and nature are considered. 4.1.1 Reliability The reliability of system information in the UAE are of lower standards in that business relay on other means while integrating data as compared to other countries where business have adopted high quality data management and analysis systems. Data mining in other countries is attributed to tested software that are compatible to each set of business. The UAE system relay on limited softwares that have been copyrighted source companies hence limited internal control. Test are done abroad and no internal environment being considered hence less reliable. 4.1.2 Adoptability Other countries have adopted the data mining and warehousing as part of their business. The capacity of UAE is limited due to their nature of business and client base. The cost of using the systems prompts many businesses to seek alternatives (Nagabhushana, 2006). The level of usage in the country is comparatively low and existing traditional models are referred while analyzing a specific market aspect. Those companies adopting the systems tend to have their origin from the west or are multinationals. Data mining and warehousing requires expertise labor, which is limited in the UAE. Many companies tend to outsource this services, the result is reduced confidentiality and increase in cost hence the limited adoptability. 4.1.3 Capacity The size of business are too small in terms of capacity hence lack the ability to handle large source of data. The complication of the systems allows only limited usage due to the nature of business being conducted by the firms. International levels sets standards to be used while performing data mining thus the required standards in the UAE are low hence many firms opt out of the system. The revolution in the business world has ensured that many trends being adopted and although the slow pace the country is adopting the Data mining and warehousing systems of doing business (Nagabhushana, 2006). 5.1 RECOMMENDATION The adoption of the system depends on the effect of it towards the organizational goal. There is need to seek modern ways of doing in order to analyse and adopt counter strategy. Data mining and data warehousing should be effective and reliable. The output data should be relayed in an orderly manner to ensure informed decision-making. 6.1 CONCLUSION Data mining and data warehousing is an emerging trend in business and there is need to adopt this system. Its application is wide beyond market strategy and can be expounded to cover the operation al aspect of business. System developers are depended on what the market offers. Adjustments should to ensure adaptability and reliability. A positive aspect of the system is the way is developed in that there is unlimited number of data stored in it. The need to save on space and ensure secure storage drives the firm towards investing in RDBMS. The firm’s ability to hire experts and adopt the system achieves the desired output. Work cited Shaw, Michael, Subramaniam Chandrasekar,Tan Gek, Welge, Michael. “Knowledge management and data Mining for marketers”. ELSEVIER. 21 Jan 2001. Web . 19 Feb 2014 Shaw, Michael. Machine learning methods for intelligent decision support: an introduction, decision support system. ELSEVIER, Boston: 1993. Print. “Data mining and warehousing concepts”. New age publishers .n.d. web. 19 Feb 2014 Berson, Alex, Smith Stephen. Data warehousing, data mining & OLAP. McGrew- Hill, New Delhi. 2004. Print. “Introduction to Oracle data mining” .Oracle Corporation, n.d. web. 19 Feb. 2014 Nagabhushana, s. Data warehousing: OLAP and Data Mining. New Age International, New York. 2006. Print Read More