Term Paper on "Management the 21st Century"

Term Paper 10 pages (3148 words) Sources: 1+

[EXCERPT] . . . .

Management

The 21st century has brought with it a number of challenges in relation to data management. Translating data into high quality and understandable information is the key to competitive advantage. Determination can be unconstructively influenced when required information can neither be retrieved nor offered comprehensibly. At the same time as various large businesses have made it investments nearing 50% of their yearly principal expenses (Teresko, 1996), and nearly all companies have been plagued with data, there is still an unrelenting "information gap." In addition, there are human expenses. Executives managing database surroundings use a great deal of time with not only internal but also external resources, filtering data to acquire required information. Therefore, these huge demands on not only financial resources but also human resources have left executives with information overload (Ye, 2003).

Key to these 21ST century challenges has been the problem of acquiring dependable and assimilated outlook from various information courses. Previously, companies developed and applied "executive information systems" (EIS) along with "decision support systems" (DSS) to give assimilated presentations from broken and incoherent data that quite often got sorted from operational computer structures. Despite the fact that information given from an EIS or DSS had often been assimilated, significant, and practical, executives started to recognize that excavating huge quantities of data from a data warehouse might outcome in more precise information outlines and enhanced accurateness in market evaluation (Phillips, 1997).

Concentration on knowledge management in companies has enhanced the view of imparting and utilizing data to gain competitive advantage (Zorn et al., 1999). Attention in data mining is enhancing as companies look towards a more comprehensive understanding of their business, in order to not only satisfy their customers, but also enhance their efficiency. Data mining is a method that allows withdrawal of concealed predictive information from huge databases. It is a prevailing method with immense potential to assist businesses in concentrating on the most significant information accessible in their databases. It offers various kinds of tools to forecast future tendencies and actions to let executives make practical, knowledge-oriented decisions (Ye, 2003).

These new mechanized and innovative analyses surpass and exceed the analyses of preceding actions given by exhibitive tools distinctive of EIS and DSS. By means of data mining abilities, several business problems that had previously been too protracted to solve can be effortlessly tackled by exploring databases for concealed patterns and discovering prognostic information. Acquiring such advantages, on the other hand, is dependent on data quality in warehouses and efficient utilization of data mining tools (Ye, 2003).

Both these aspects (data quality in warehouses and efficient utilization of data mining tools) are rarely understood and seldom executed in the correct way. The purpose of this research is to explain the functions of data mining in favor of business decisions and to define some of the obstacles to its effectual deployment in companies and give recommendations.

Literature Review

For along time, business executives had complained that they are sinking in data generated by computer-founded structures. Nonetheless, practical and useful information is still in short supply. Companies have been amassing huge quantities of data. The range of the business world's data is predicted to double every year and a half (Frawley et al., 1991). The typical Fortune 500 business deals with more than a terabyte of digital information - that is amid 20 million to 500 million text reports and pages - every day, with 57% yearly increase (Kempster, 1998; Zorn et al., 1999). For case in point, the databases of at&T might comprise phone call patterns of its 100 million clients who produce more than 200 million phone calls every day.

On the other hand, most of the data has not been successfully used because of lack of highly developed systems to examine this significant and complexly developed data (Rosen, 1996). This difficulty has been further complicated by the company's rising utilization of Web tools (for instance, e-commerce, extranets and intranets). These tools allow companies to collect additional data from their digital dealings, which, consecutively, need far-reaching analyses to satisfy their clients. Problem of surplus data and very little information is the core issue at present (Ye, 2003).

Ye (2003) writes, "Advanced technologies have enabled us to collect large amounts of data on a continuous or periodic basis in many fields. On one hand, these data present the potential for us to discover useful information and knowledge that we could not see before. On the other hand, we are limited in our ability to manually process large amounts of data to discover useful information and knowledge. This limitation demands automatic tools for data mining to mine useful information and knowledge from large amounts of data. Data mining has become an active area of research and development (Ye, 2003)."

Data mining" can be defined as a procedure for making breakthroughs by excavating unidentified and actable information from huge and secure databases in favor of making practical business decision. Data mining evokes algorithms, which itemize designs from, or equip models to, data (Fayyad, 1997). The excavated information can be utilized to shape a forecast or categorization model, recognize relations amid database accounts, or offer an outline of the databases being excavated. Those designs or systems can be utilized to direct strategic decisions and predict the outcome of those decisions (Kempster, 1998; Saarenvirta, 1999). For that reason, data mining discovers and reveals previously unknown or overlooked information for executives which, consecutively, outcomes in more practical business decisions.

Kelly (2005) writes, "The term 'data mining' has come to refer to a set of techniques that originated in statistics, computer science, and related areas that are typically used in the context of large datasets. The purpose of data mining is to reveal previously hidden associations between variables that are potentially relevant for managerial decision making. The exploratory and modeling techniques used in data mining are familiar to many statisticians and include exploratory tools such as histograms, scatterplots, boxplots, and analytic tools such as regression, neural nets, and decision trees (Kelly, 2005)."

The procedure of data mining comprises the recognition of the data mining aims, recognition of the data, which is to be utilized, training and renovation of the data, along with data mining. May be the most effective access to data for analysis is to excavate the data from a warehouse. This, on the other hand, does not mean that a data warehouse ought to exist so as to extract and mine data. Data mining applications can, in addition, function outside warehouses. In this instance, nonetheless, data mining needs additional steps for mining, importing, along with evaluating data (Ye, 2003).

In operation, data mining needs the execution of two major steps. The first is the assortment and alteration of data into a reliable design for the mining processes (this is where warehousing comes into the picture), and the second is the utilization of analytical methods to assess data and recognize designs and forecasts for making decision (Ye, 2003).

The warehousing process

This measure might use from 50% to 80% of the entire data mining endeavor. In view of the fact that data is frequently stocked in various systems and designs, data from varied sources has got to be combined to generate a data warehouse, which is able to assist mining of data and making of business decisions. The warehousing procedure utilizes electronic and digital methods to combine data from various resources, redesign it, and thereafter arrange it in a manner to assist in drawing a macro outlook of information for managers and more comprehensive information for functional clients (Ye, 2003).

Data mining process

This process comprises the automatic withdrawal of embedded, formerly unidentified, and potentially helpful information from data in huge warehouses or databases. It comprises methods to mechanically discover designs in a warehouse with no previous production of a theory. It utilizes software designs to forecast future circumstances, categorize clients or conditions into known groups, discover developing operational difficulties, or assist in making concurrent business decisions (Ye, 2003).

Barriers to effective data mining

Several barriers exist in the implementation of effective data mining. Firstly, lack of vision towards data mining by organizational leaders is a common phenomenon. Secondly, producing and sustaining data and utilizing unacceptable resources and designs systems. Thirdly, ignoring corporate responsibility inside the data management workforce, which leads to inferior data quality. Fourthly, managers often do not set the standards for measuring data and fail to provide workable definitions of quality. Fifthly, selection of data mining software is done while disregarding company vision towards data mining. And lastly, failure to set up monitoring processes to maintain data quality at common stages of data formation, mining, and execution (Ye, 2003).

It is important to note that even the most complicated data mining applications cannot create successful databases and warehouses if the data is wrong. For case in point, a production firm had to fragment an almost $20 million database venture because of disconnectedly defined merchandise data along with inferior quality. A publishing and broadcasting firm used four… READ MORE

How to Reference "Management the 21st Century" Term Paper in a Bibliography

Related Term Papers:

Drucker's Management Principles Term Paper

Drucker

21st Century: Drucker's Management & Leadership Principles

21st Century: Drucker's Management & Leadership Principles

Since the turn of the century, the landscape of management may have changed, but the… read more

Term Paper 7 pages (2466 words) Sources: 5 Topic: Management / Organizations

---

Management Skills and Competencies Term Paper

Management Skills and Competencies: Personal Development Plan

This paper outlines the key management skills and competencies needed for 21st century managers. They fall into the three major categories of: technical,… read more

Term Paper 4 pages (1696 words) Sources: 1+ Topic: Management / Organizations

---

Management Skill and Competencies Term Paper

Management Skills & Competencies

Skills and Competency Grid for the 21st Century Manager

The purpose of this work is to first create a skills and competency grid for the 21st… read more

Term Paper 6 pages (2582 words) Sources: 1+ Topic: Management / Organizations

---

Business Case in the 21st Century Research Proposal

Business Case

In the 21st century, organizational change has gone from something that happened only once in a while -- for example as the result of a crisis -- to… read more

Research Proposal 3 pages (922 words) Sources: 3 Style: APA Topic: Management / Organizations

---

Applied Management and Decision Sciences Thesis

management and decision sciences from various theorists; and, analyzes the evolution of managerial decision making from scientific management to the complicated forecasting models used today. The objectives of the Breadth… read more

Thesis 90 pages (25680 words) Sources: 40 Topic: Management / Organizations

---

View 100+ more related papers  >>