THE EFFECTS OF ONLINE ANALYTICAL PROCESSING (OLAP) FOR DECISION SUPPORT IN AN ORGANIZATION (A CASE STUDY OF GTBANK)
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- NGN 3000
BACKGROUND OF THE STUDY
The term OLAP stands for Online Analytical Processing. OLAP analyzes corporate data in several dimensions and allows for complicated computations, trend analysis, and advanced data modeling. Many Business Intelligence (BI) solutions use the OLAP (Online Analytical Processing) technology. According to Chaudhuri & Dayal, (1997) it is a strong data discovery tool with unlimited report viewing, complicated analytical computations, and predictive "what if" scenario (budget, forecast) planning capabilities. For Business Performance Management, Planning, Budgeting, Forecasting, Financial Reporting, Analysis, Simulation Models, Knowledge Discovery, and Data Warehouse Reporting, it serves as the basis. It allows end-users to do ad hoc data analysis in many dimensions, giving them the knowledge and understanding they need to make better decisions.
Gonzales (2006) stated that a typical organization maintains and utilizes a number of operational data sources. These operational data sources include the databases and other data repositories which are used to support the organization’s day-to-day operations. This repository is known as a data warehouse. A data warehouse is created within an organization as a separate data store whose primary purpose is data analysis for the support of management's decision making processes (Inmon, 2002). Often, the same fact can have both operational and analytical purposes. For example, data describing that customer X bought product Y in store Z can be stored in an operational data store for business-process support purposes, such as inventory monitoring or financial transaction record keeping. That same fact can also be stored in a data warehouse where, combined with vast numbers of similar facts accumulated over a time period, it is used to analyze important trends, such as sales patterns or customer behavior (Nenad, Mary, Boris, Frada & Clyde (2008).
Mohammed stated in 2014 that Data warehousing is a set of decision-making tools intended at assisting knowledge workers (executives, managers, and analysts) in making better and quicker choices for the enterprise's smooth operation. It acts as a tangible representation of a decision support data model, storing the data that an organization requires to make strategic choices. It offers company leaders with the architecture and tools they need to methodically organize, interpret, and apply their data to make strategic choices. Data Warehouse is a database of historical, summarized and consolidated data is more important than detailed, individual records used for reporting and analysis. It refers to the database that is maintained separately from an organization’s operational databases which contains consolidated data.
The data stored in the data warehouse is uploaded from the operational systems, over potentially long time they tend to be much larger than operational databases. DW systems allow for the integration of a variety of application systems. They support information processing by providing a solid platform of consolidated historical data for analysis. DW technologies have been successfully deployed in many industries: manufacturing (for order shipment and customer support), retail (for inventory management), financial services (for credit card analysis, risk analysis, and fraud detection), utilities (for power usage analysis), and healthcare (for outcomes analysis) (Mohammed , 2014).
1.2 STATEMENT OF THE PROBLEM
Business is a multifaceted activity in which decisions are made based on a variety of factors. Businesses keep track of their actions by taking into account a variety of factors. These variables are shown on axes (x and y) in a spreadsheet, with each axis representing a logical grouping of data in a category.
All effective judgments are built on a foundation of knowledge. Successful firms plan, evaluate, and report on sales and operational operations on a regular basis in order to maximize efficiency, cut costs, and increase market share. The more sample data you have, statisticians will tell you, the more likely the final statistic will be correct. Naturally, the more information a firm has about a certain activity, the more likely the strategy to improve that activity will be successful. All businesses collect data using many different systems, and the challenge remains: how to get all the data together to create accurate, reliable, fast information about the business. A company that can take advantage and turn it into shared knowledge, accurately and quickly, will surely be better positioned to make successful business decisions and rise above the competition.
To analyze and report on the health of a business and plan future activity, many variable groups or parameters must be tracked on a continuous basis—which is beyond the scope of any number of linked spreadsheets. These variable groups or parameters are called Dimensions in the On-Line Analytical Processing (OLAP) environment. Nowadays, many spreadsheet users have heard about OLAP technology, but it is not clear to them what OLAP means. Therefore, this study tends to evaluate Online Analytical Processing (OLAP) and its effect for decision support in an organization.
1.3 OBJECTIVES OF THE STUDY
The primary objective of this study is to evaluate Online Analytical Processing (OLAP) and its effect for decision support in an organization. Other specific objectives are:
- To examine the benefit of using OLAP in an organization.
- To examine the challenges of using OLAP in an organization.
- To examine the extent to which OLAP helps in effective decision support in an organization
- To proffer solutions to the challenges of using OLAP in an organization
1.4 RESEARCH QUESTIONS
The following research questions were posed in accordance with the study's objectives:
- What is the benefit of OLAP in an organization?
- What are the challenges of using OLAP in an organization?
- To what extent has OLAP helped in effective decision support in an organization?
- What are the possible solutions to the challenges of using OLAP in an organization?
1.5 RESEARCH HYPOTHESIS
The study will test the validity of the following hypothesis:
H0: There is no positive significant relationship between OLAP and effective decision support in an organization
HA: There is a positive significant relationship between OLAP and effective decision support in an organization
1.6 SIGNIFICANCE OF THE STUDY
This study will expose all business organizations such as banking and manufacturing firms to significant the role OLAP plays in supporting more logical and analytical decision making ranging from planning, budgeting, reporting, and analysis. This study will also bring to the knowledge of the public target the benefits of using OLAP, its challenges and different OLAP systems.
This study will further serve add to existing literature on this topic and shall serve as reference material for further research on this topic or related field in the future.
1.7 SCOPE AND LIMITATION OF THE STUDY
This study will focus on examining the benefit of using OLAP in an organization. This study will also examine the challenges of using OLAP in an organization. The extent to which OLAP helps in effective decision support in an organization will also be looked into in this study. Finally, this study will proffer solutions to the challenges of using OLAP in an organization. ICT department staffs of Guarantee Trust Bank (GTB) in Ilorin, Kwara State shall serve as enrolled participants for this study.
Some of the factors that posed a challenges to the completion of this study were time constraint, insufficient fund, and inadequate literature in this study domain.
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