LEARN MORE...

Data Warehousing Research Paper

Content type

User Generated

Subject

Computer Science

Type

Research Paper

Uploaded By

pynverbonr

Pages

Showing Page:

1/8

Introduction

Data is a collection of information that needs to be organized and structured to have meaning. It is

therefore important to store it for future use and analysis when needed in order to make well-thought out

decisions in businesses. Data storage therefore requires investment in the right applications that will

guarantee stability, flexibility and performance. The data needs to also be analyzed in such a way that it

will be meaningful as storing huge chunks of data in a disorderly manner does not quite fulfill the essence

of data warehousing. This report will therefore look into data warehousing, various data sets as well as

databases that all form part of the data storage process.

Data Warehousing and the difference between operational and strategic data sets

Data warehousing is basically a consolidation of all data in a given organization that has been

optimized for the purpose of analysis. It is defined as a general facility where enterprises are able to

satisfy their information needs by getting quick, accurate and useful information (Perkins, 2003 p.1).

Data warehousing is increasingly becoming important in capturing of information and essential for

any application to be effective. Data warehousing helps in decision making as all meaningful data

can be retrieved easily and in a short period of time (Nguyen, 2011 p.1). It is however

disadvantageous as it requires heavy investments in terms of resources both monetary and human

resource to set up and maintain.

Operational data sets

This type of data set is designed in a way that will enable the integration of data from multiple sources to

aid in the additional operations. The data is used for controls and operational data support. Since the data

does not originate from one source, the process of integration involves cleaning. The operational data sets

are also designed to contain indivisible data.

Strategic data sets

Showing Page:

2/8

Strategic data sets aid in analysis known as the SWOT analysis. This type of analysis is useful for any

business as it helps companies to determine their strategic direction as well as strategic goals. The data

also helps marketers to keep abreast with the latest trends and the products in demand that are appealing.

Key stakeholders may also make use of the information to develop standard practices for business

environment.

The main difference between operational and strategic data sets is that operational is internally-based and

concentrates on the internal operations of a given organization whereas strategic data set is external.

Data Mining and OLAP (On-Line Analytical Processing) compared with OLTP Systems

Data Mining is a process that explores and analyzes large amounts of data stored in repositories with the

aim of discovering meaningful correlations, trends or patterns (Galit et al, 2010 n.p). Business intelligence

is one of the most important applications that is targeted by data mining. The benefit of data mining in

business intelligence helps businesses to perform effective market analysis, gauge customer feedback

among others. A data warehouse can be enriched with advanced analytics such as OLAP or data mining.

On-line Transaction Processing (OLTP) applications are aimed at fulfilling the day-to-day transactional

requirements as well as the operational data retrieval needs of the enterprise. It is also responsible for

maintenance of an accurate model of a real world enterprise. It is also characterized by frequent updates

where the transactions have access to only a fraction of the database. Both OLTP and OLAP are systems

of management data.

OLAP databases are made in a de-normalized manner such that the files are made redundant so as to

improve analytical performance. Its processing speed is however very slow and usually takes hours since

it involves transactions of huge data. Data is organized in a multidimensional model that supports

decision and analysis of data since its main aim is to assist in problem solving.

OLTP is considered as the source to all original data. The data is well structured, organized and detailed.

It therefore is speedy and performs relatively well in reliability, security and data integrity. It is aimed at

Showing Page:

3/8

End of Preview - Want to read all 8 pages?

Access Now

Unformatted Attachment Preview

Introduction Data is a collection of information that needs to be organized and structured to have meaning. It is therefore important to store it for future use and analysis when needed in order to make well-thought out decisions in businesses. Data storage therefore requires investment in the right applications that will guarantee stability, flexibility and performance. The data needs to also be analyzed in such a way that it will be meaningful as storing huge chunks of data in a disorderly manner does not quite fulfill the essence of data warehousing. This report will therefore look into data warehousing, various data sets as well as databases that all form part of the data storage process. Data Warehousing and the difference between operational and strategic data sets Data warehousing is basically a consolidation of all data in a given organization that has been optimized for the purpose of analysis. It is defined as a general facility where enterprises are able to satisfy their information needs by getting quick, accurate and useful information (Perkins, 2003 p.1). Data warehousing is increasingly becoming important in capturing of information and essential for any application to be effective. Data warehousing helps in decision making as all meaningful data can be retrieved easily and in a short period of time (Nguyen, 2011 p.1). It is however disadvantageous as it requires heavy investments in terms of resources both monetary and human resource to set up and maintain. O ...
Purchase document to see full attachment

Tags: Data storage Data Warehousing Operational data sets Strategic data sets Data Mining and OLAP