Data Warehousing

Abstract

Information and information assets are becoming hugely valuable to business enterprises. Therefore, these assets need to be stored properly and readily accessible whenever they are required. However, the availability of excessive data makes the process of information extraction from the data difficult and almost impossible. Although results from Internet search show that data is same as information, it is not always correct. Excessive data is too much. However, there is a way of simplifying the storage of data for extraction of information whenever the users need the information. This systematic method of storage of data in a way that information will be extracted easily is referred to as data warehousing. This paper is meant to supplement the existence of information about data warehousing by discussing data warehousing.

Introduction

            A data warehouse is a large and central store of data from a wide range of sources with the business enterprise, a company, or an organization. Data warehouse is used in guiding the management decisions by availing the data. It is also a subject-oriented, time variant, integrated, and non-volatile large collection of data that help in the process of making a decision in an organization. By this definition, it means that (Kimball, & Ross, 2013):

·         Subject-oriented: By this feature, it means that a data warehouse stores data that is a target to a particular subject. For example, a data warehouse is likely to store data that is regarding total sales and number of customers for an organization among others. This means that it does not store general data of daily organization’s operations.

·         Integrated: It means that data held in the data warehouse may be distributed across heterogeneous and integrated sources. For example, client information may be stored on RDB and sales data may be from Flat files.

·         Time Variant: The data stored in the data warehouse may not be current data. However, it varies with time because data is characterized by an element of time. For example, customer data for the last five years.

·         Non-Volatile: Data warehouse is different from the Enterprise Operational Database. There it should not frequently be modified. It should be exposed to only two operations of data loading and data access.

Data Warehousing

            Data warehousing is a derivation is a derivation of the data warehouse. Therefore, data warehousing is the storage of huge amount of data by a business enterprise. The data in the warehouse need to be stored in a way that is secure, reliable, easily retrievable, and easily manageable. The concept of data warehousing dates back in 1988 with the research of Barry Devlin and Paul Murphy of IBM. The need for a data warehouse has continued to evolve as the complexity of computer systems has continued to grow immensely and handled increasing amounts of data (Kimball, & Ross, 2013).

Features of a Data Warehouse

(i)     A data warehouse is different and separate from operational database

(ii)   Data warehouse helps in integrating data from heterogeneous systems within the business enterprise

(iii) A data warehouse stores large amounts of data which is more historical as opposed to being current

(iv) A data warehouse does not need high accuracy of data

(v)   It has general complex queries

(vi) The goal and objective of a data warehouse are execution of statistical queries and give outcomes capable of influencing decision-making process of a business enterprise

Data Warehousing Concept

            Data warehousing is a concept that has grown from a large amount of data that is generated and stored recently by the business enterprises. It has also grown from the urgent requirement to use the stored data to achieve goals that traverse beyond the routine tasks associated with day-to-day business processes. A large business enterprise is made of several branches and managers want to quantify and assess how each of the branches contributes to the worldwide business performance (Farooq, 2013).

            The business enterprise stores comprehensive data according to the tasks each of the branches performs. Therefore, to meet the needs of the managers, tailor made queries can be issued to help in retrieving the required data. However, this process would not work without the database managers formulating the desired query which is typically an aggregate SQL query having studied the database catalogs closely. Upon the processing of the SQL query, a report is generated after which it is passed to the senior managers as spreadsheets (Farooq, 2013).

            Database designers realized the above approach is not feasible since it demands a lot of time and resources. Additionally, it does not accomplish desired results. Furthermore, there is a likelihood of mixing of analytical queries and routine transactional queries which slow the system down thus not meeting the needs of the users. Modern advanced data warehousing processes online analytical processing (OLAP) and online transactional processing (OLTP) separately. It does this when it creates new information storage capable of integrating basic data from different sources, arranges the data formats properly, and then avails data for analysis and assessment with the aim of planning and process of decision-making (Farooq, 2013).

Fields of Data Warehousing Applications

            Data warehousing has found application across many fields in the current business world and industries. Below are some of the fields of application (Triplet & Butler, 2013):

·         Trade: Used for analyzes of sales and claims, shipment and control of inventory, public relations, and customer care

·         Craftsmanship: Production cost control as well as supplier and order support

·         Financial services: Used for risk analysis and detection of credit cards fraud

·         Transport industry: Management of vehicles

·         Telecommunication services: Analysis of call flow and customer profile analysis

·         Health care service: Used for analysis of patient admission and discharge and bookkeeping in the department of accounts.

            Data warehousing is not restricted to enterprises application field. Its application also includes epidemiology, demography, natural science, and education among many others. However, these applications have a common field which is the need for storage of data and query tools to help in easy and quick retrieval of information summaries from the huge amount of data stored in the databases or availed through the Internet. This resultant information allows the users to study business progress and phenomena, learn about some of the meaningful correlations and gain important knowledge to help in the process of decision making (Triplet & Butler, 2013).

Data Warehousing Architectures

            Below are architecture properties which are important to creation and building a data warehouse system (Farooq, 2013):

·         Separation: Both analytical processes and transactional processes should be separated as much as possible.

·         Scalability: Hardware and software architectures should easily be upgradable as the data volume which needs to be well managed and processed, the number of user’s requirements which should be met

·         Extensibility: The data warehouse architecture should have the capability to host new applications and emerging technologies without the need to redesign the whole system.

·         Security: A data warehouse should have monitoring accesses which are important because of the confidential and strategic stored.

·         Administrability: The management of data warehouse should not be excessively difficult.

Benefits of Data Warehousing

            A data warehouse helps an organization to have a solution to the problem of having to pull data from the transactional systems quickly and efficiently and thus converts the resultant data into information for decision making. However, there are some specific benefits that a successful implementation brings to an organization such as (Farooq, 2013):

(i)     It enhances business intelligence (BI)

(ii)   In helps to increase the performance of query and the system

(iii) It brings about timely access of data in the organization

(iv) It provides the organization with business intelligence from several sources

(v)   It enhances consistency and quality of data

(vi) It provides historical intelligence of data to the organization

(vii)  It has a high return on investment

Conclusion

            This paper has presented corporate information about data warehouse and data warehousing. It has shown the importance of implementation of such a system to a business enterprise. Specifically, it helps in the management of data for easy and quick retrieval for the purpose of making a decision. It has also made us understand the fundamental concepts underlying data warehousing and why every business enterprise should have one. The information contained herein is not exhaustive of data warehouses and data warehousing. It is my recommendation that researchers should unearth more information for further reading on data warehousing.

References

Farooq, F. (November 01, 2013). The data warehouse virtualization framework for operational    

            business intelligence. Expert Systems, 30, 5, 451-472.

Kimball, R., & Ross, M. (2013). The data warehouse toolkit: The definitive guide to dimensional modeling. Indianapolis, Ind: Wiley.

Triplet, T., & Butler, G. (May 14, 2013). A review of genomic data warehousing                           

            systems.Briefings in Bioinformatics, 5.)

Sherry Roberts is the author of this paper. A senior editor at MeldaResearch.Com in research paper company if you need a similar paper you can place your order for pre written essays.