IT 812 Business Intelligence and Data Warehousing

IT 812 Business Intelligence and Data Warehousing

IT 812 Business Intelligence and Data Warehousing

Abstract

This IT 812 business intelligence and data warehousing looks into the various factors including data warehousing, data mining and business intelligence as well the use and benefit of these for the modern day business organizations. The report will also be looking into the factors of related to the ethical as well as logistical issues associated with collecting and analysing business data with the intention of obtaining business intelligence. The study will base itself on the various recent texts, peer-reviewed conference papers and journal articles. The topics which will be covered in this report are very significant for the companies in the modern day business environment in view of the fact that advanced information technology have become one of the major source of the companies worldwide.

Introduction

The topics which will be covered in this report are very significant for the companies in the modern day business environment in view of the fact that advanced information technology have become one of the major source of the companies worldwide. Due to the fact that the information technology and the related aspects have developed and advanced during the last few decades, more and more number of companies these days are using the same to streamline the various operational as well as strategic processes within scope of the company. Amongst these strategic processes, the forecasting as well as decision making processes have become of utmost importance for the companies  in view of the changing global business dynamics and competitive landscape. Hence the companies are also trying to utilize the solutions provided by the information technology to fine-tune these processes. It can be said that these processes can be helped with various data and business intelligence tools which help the enterprises for providing reports for the strategic business management through pool resources from the electronic data interchange as well as business data. This helps in better decision making through ensuring competitive intelligence within the origination. These processes are also helpful in knowledge management as well as knowledge sharing for the companies. The basic component for these processes is the storage and retrieval option of the data which is provided by the data warehouse solutions for the companies and the process of mining data from the same. Hence in this report, the aim of the author will be to look into these factors (Kurt, 1999).

IT 812 Business Intelligence and Data Warehousing

This business intelligence and data warehousing assignment help looks into the various factors including data warehousing, data mining and business intelligence as well the use and benefit of these for the modern day business organizations. The report will also be looking into the factors of related to the ethical as well as logistical issues associated with collecting and analysing business data with the intention of obtaining business intelligence. The study will base itself on the various recent texts, peer-reviewed conference papers and journal articles.

Analysis of the potential and actual uses of data warehousing and data mining in organisations

Introduction to key terms

To develop an understanding regarding the uses of data warehousing and data mining in organisations it is of utmost importance that a knowledge regarding the definition of various related aspects which will be involved.

We will first be looking into the defection of data. The data can be defined as the various information or statistics or other indictors predominantly in the alphanumeric format which shows information in an unorganized as well as raw form. Data can also consist of various symbols. Data usually helps in representing various ideas, conditions, or objects.

From the basis of the data the companies tend to draw business intelligence through various data analysis and representation. As has been opined B de Ville, business intelligence are the technologies and application for collecting, storing as well asanalysing business data that helps the enterprise to make better decisions (Ville, 2001).

Definition of data warehouse and data mining

The definition of data warehouse can be given as a system infrastructure which can source, store, clean, extract and, confirm data into a dimensional data store. The data warehouse also has the capability of supporting as well as implementing the process through which the end users can query the same for extraction of the data in addition to the analysis of the same for various decision making purposes in the organisations. Various tools for data mining and OLAP are used for facilitating the complex business models as well as multinational analysis.  The definition of data warehouse can be given by Inmon W.H as an integrated, subject oriented, non-volatile and time variant collection of data which provides significant support to the decision making process of the management. Data Warehouse is a repository of business or enterprise databases which provides a clear picture of historical as well as current operations of organizations (Michael, 2004).

Data mining which is also termed as knowledge or data discovery can be deified as the process in which data is analysed from various perspectives and summarized into several valuable pieces of information. These set of information can be utilized for various operational as well as strategic processes within scope of the company. Amongst these strategic processes, the forecasting as well as decision making processes have become of utmost importance for the companies  in view of the changing global business dynamics and competitive landscape. The various micro decisions which data mining help in include how to cuts costs, increase revenue, or both (Michael, 2004).

This is usually done through the help of various available data mining software which allows users to analyse data from multiple dimensions, categorize it, in addition toabridge the identified relationships. From a strict technical view data mining is the procedureused to findpatterns or correlations amongst the huge number of fields and data in relational databases which are large.

Introduction to data warehouse and data mining

The data warehousing and data mining in organisations have become two very well used terms and concepts. During the past couple of decade data warehouses as well as data warehousing have gained a lot of ground for the purpose of developing and delivering business intelligence (BI). Due to this most of the medium to large companies are currently using various formats of data warehouse for storing as well as extracting data in addition to give a lot of priority to the same as a corporate strategy. Various critical business applications which are used in the context of the modern day business organizations including demand forecasting and predictive analysis, online analytical processing, complex query processing, statistical analysis, in addition tothe significant business decisions are done om the basis of the various data which are available in the data warehouse of the companies (Kurt, 1999).

Actual uses of data warehousing and data mining in organisations

In view of the fact that warehousing of data helps in providing a coherent picture of the conditions of the business at any specific point of time, currently the most widely used usage of data warehousing is the implementation of the same for the decision making processes which are efficient as well as effective. This provides the companies opportunity for the development of system which can ultimately help in theretrieval of the data through extraction in flexible methods. In conjunction to this the process of data mining helps in developing the ways through which the data is stored with the intention ofimproving thestandards of analysis as well as reporting. These reporting are often used for the management information system (MIS) which is a very important decision making tool for the companies and the senior management of the companies.

It is considered by the experts on the data warehouse that the various data stores are related as well as connected to each other physically in addition toconceptually. This helps in storing the data of any business across a number of databases which enables the users to analyse the same with the broadest range of data due to the connection of these databases with each other in some way or the other. Hence it can also be confirmed that the modern day data warehousing solutions also help the businesses to relate their base of data with each other on the basis of validity and relevance of themwhich helps the physical databases to create a connection so that the data can be analysed together for the purposes of reporting.One of the major part of the data warehousing and data mining is the OLAP or Online Analytical Processing tools which help in developing consistent as well ascomplete data store from a number of different sources within the organization. These various sources can be different departments or the differentactivities or the operations of the business entities. OLAP helps in which easily understanding and using the data in business applications. Some of the ways in which the data warehousing as well as data mining help the business originations in their existing forms are as following:

  • Solving what-if analysis
  • Quick decisions on current & historical data
  • Provide ad-hoc information for loosely-defined system
  • Manage & control businesses
  • Integration of data across the enterprise
Potential uses of data warehousing and data mining in organisations

Although the basic infrastructure as well as the functions of the data warehousing and data mining in organisations will remain the same due to the required activities out of this, the use of the same in various other operational as well as strategicdecision making can be enhanced. This will be different for different types of originations on the basis of the kind of work which are done by the same. Following are some of the examples which show the possible scopes as well as potential uses of data warehousing and data mining in these organisations:

  • Financial Data Analysis
    • Clustering as well as classification of the customers for targeted marketing.
    • Recognition of chances of laundering of money as well as other crimes related to finance.
    • Prediction of loan payment patterns of the customers as well as analysis of credit policy.
  • Retail Industry
    • Activation and retention of customers
    • Cross-referencing of items as well as product recommendation
    • Multidimensional analysis of sales, effectiveness of sales campaigns, customers, products, time and region.
    • Customer relationship management activities
  • Telecommunication Industry 
    • Fraudulent pattern analysis.
    • Identification of unusual patterns.
    • Mobile Telecommunication services.
    • Multidimensional Analysis of Telecommunication data.
    • Multidimensional association and sequential patterns analysis.
    • Use of visualization tools in telecommunication data analysis.

Identification and evaluation of ethical and logistical issues associated with collecting and analysing business data in order to obtain business intelligence

Introduction to collecting and analysing business data in order to obtain business intelligence

As has been seen, on the basis of the data the companies tend to draw business intelligence through various data analysis and representation. As has been opined B de Ville, business intelligence are the technologies and application for collecting, storing as well as analysing business data that helps the enterprise to make better decisions. Hence it can be said that for the purpose of development business intelligence in the business entities it is required to collect as well as store the business with the provision of analysing the same in the future. However there have been multiple ethical and logistical issues which have been identified to be associated with collecting and analysing business data with the purpose of obtaining business intelligence. All of these factors have been able to lead to a lot of discussions as well as deliberations on the part of the subject matter experts as well as the industry practitioners. Most of these discussion have based themselves on the human impacts on the storage and use of including the privacy as well as security concerns in addition to the limitations of the analysis.We will be looking into these factors,

Identification and evaluation of security and privacy issues associated with collecting and analysing business data

For decades the security and privacy protection has been a concern for public policy. Especially during last couple of decades due to the rapid changes in the information technology and science the various modes of collection as well storage of data has grown. Especially due to the advent of the internet and electronic commerce in addition to the expansion of more sophisticated methods of collecting, using as well as analysing business and customer information have made security and privacy a major issue (Hiller, 2002).

With the growing acceptance and usage of data mining which deals with large amounts of data, which can be collected and stored easily through the computer systems, these concerns grow manifold. Especially in view of the fact that these data for the business houses also containa lot of personal information of the consumers the issue becomes even more pertinent. When personal and sensitive data are published and/or analysed, one very significant question which is needed to be taken into account is whether the analysis violates the privacy of individuals whose data is referred to. For the reason that collecting and analysing business data in order to obtain business intelligence through the process of data mining makes it possible to analyse routine business transactions in addition tocollectsubstantialquantity of information on the subject of buying habits as well as preferences of the individuals, which may be very private to him or her.

The other issue is related to the topic of data integrity. A very important issue with the application is integrating redundant or conflicting data from various sources.  Edward McNicholas, global co-leader of the Privacy, Data Security, and Information Law Practice at Sidley Austin LLP, said he thinks some of the potential risks of Big Data are overstated, but believes, “the most significant risk is that it is used to conceal discrimination based on illicit criteria, and to justify the disparate impact of decisions on vulnerable populations.”

Identification and evaluation of data analysis and related issues associated with collecting and analysing business data

The other data analysis and related issues associated with collecting and analysing business data can be discussed as below (Dunham, 2005):

  • Mining Methodology as well as User Interaction
    • Ad-hoc data mining in addition to data mining query language
    • Visualization as well asexpression of data mining results
    • Handling incomplete data as well as noise in them
    • Incorporation of background knowledge
    • Interactive mining of knowledge at multiple levels of abstraction
    • Mining different kinds of knowledge in database
    • Pattern evaluation
  • Performance and Scalability
    • Scalability as well asefficiency of data mining algorithms
    • Distributed,parallel in addition to incremental mining methods
  • Issues Related to Applications and Social Impacts
    • Application of domain specific data mining tools, discovered knowledge, decision-making and intelligent query answering
  • Issues Relating to the diversity of Data Type
    • Handling complex as well asrelational types of data
    • Mining information from global information systems as well as heterogeneous databases including web database.

Conclusion

From this study it can be concluded that companies these days are trying to utilize the solutions provided by the information technologyto fine-tune various internal processes. It can be said that these processes can be helped with various data and business intelligence tools which help the enterprises for providing reports for the strategic business management through pool resources from the electronic data interchange as well as business data. This helps in better decision making through ensuring competitive intelligence within the origination. These processes are also helpful in knowledge management as well as knowledge sharing for the companies. The basic component for these processes is the storage and retrieval option of the data which is provided by the data warehouse solutions for the companies and the process of mining data from the same.  Although the basic infrastructure as well as the functions of the data warehousing and data mining in organisations will remain the same in the course of the future due to the required activities out of this, the use of the same in various other operational as well as strategic decision makings can be enhanced. This will be different for different types of originations on the basis of the kind of work which are done by the same.  However there have been multiple ethical and logistical issues which have been identified to be associated with collecting and analysing business data with the purpose of obtaining business intelligence. All of these factors have been able to lead to a lot of discussions as well as deliberations on the part of the subject matter experts as well as the industry practitioners. Most of these discussion have based themselves on the human impacts on the storage and use of including the privacy as well as security concerns in addition to the limitations of the analysis (Dunham, 2005).

References

  • B. de Ville, (2001), “Microsoft Data Mining: Integrated Business Intelligencefor e-Commerce and Knowledge Management”, Boston: Digital press.
  • Berson Alex, Smith J. Stephen, Thearling Kurt, (1999), “Building Data Mining Applications for CRM”, McGraw-Hill Companies.
  • C. Date, (2003), “Introduction to Database Systems”, 8th ed., Upper Saddle River, N.J.: Pearson Addison Wesley.
  • Chen, S. H, (2002), “Genetic Algorithms and Genetic Programming in Computational Finance”, Boston, A: Kluwer.
  • D. Pyle, (2003), “Business Modeling and Data Mining”, Morgan Kaufmann, San Francisco, CA.