1.1 Background to the Study

In the dynamic landscape of the modern banking industry, the role of technology in decision support has become increasingly crucial. One prominent technological advancement that has significantly influenced decision-making processes is Online Analytical Processing (OLAP). OLAP enables banks to analyze vast amounts of data swiftly and efficiently, providing valuable insights that empower decision-makers to make informed choices. This introduction explores the profound impact of OLAP on decision support within the banking system, shedding light on its benefits and implications. Inmon, W. H., & Hackathorn, R. D. (1994).

The banking sector operates in an environment characterized by complex transactions, diverse financial products, and regulatory compliance. In such a context, decision-makers are tasked with navigating intricate data sets to make strategic choices. OLAP, as a powerful analytical tool, facilitates the extraction of meaningful patterns and trends from these data sets. By allowing multidimensional analysis, OLAP enables banks to examine data from various perspectives, such as customer segments, financial products, and market trends, providing a holistic view essential for effective decision support. Kimball, R., & Ross, M. (2002).

Moreover, the speed at which decisions are made is crucial in the fast-paced banking industry. OLAP's ability to process and analyze data in real-time empowers decision-makers to respond promptly to market changes, emerging risks, and customer demands. This real-time capability enhances the agility of the banking system, enabling it to adapt swiftly to evolving economic conditions. As a result, banks can optimize their operations, streamline processes, and proactively address challenges, ultimately fostering a competitive edge in the market. Sharda, R., Delen, D., & Turban, E. (2014). Top of Form

Bottom of Form


1.2 Statement of the Problem

The modern banking industry is continually evolving, with advancements in technology playing a pivotal role in shaping its landscape. One of the prominent technological trends is the adoption of Online Analytical Processing (OLAP) systems for decision support. This research aims to investigate and analyze "The Effect of Online Analytical Processing for Decision Support in a Banking System." The statement of the problem revolves around understanding the impact of integrating OLAP technologies into banking operations, specifically focusing on decision-making processes. The study will delve into how OLAP systems enhance data analysis, reporting, and querying capabilities in the context of banking activities. Additionally, it will explore potential challenges and limitations associated with the implementation of OLAP in the banking sector. By addressing these issues, the research seeks to provide valuable insights that can inform decision-makers, IT professionals, and stakeholders in the banking industry about the efficacy and implications of incorporating OLAP for decision support.

1.3 Objectives of the Study

The main objective of the study is to examine the effect of online analytical processing for decision support in a banking system. Specific objectives of the study are:

  1. 1.  Analyze the Effectiveness of OLAP in Enhancing Banking Decision-Making.
  2. 2.  Investigate the Challenges and Opportunities of Implementing OLAP in Banking Systems.
  3. Explore the Impact of OLAP on Competitive Advantage and Strategic Decision-Making.

1.4 Research Questions

To guide the study and achieve the objectives of the study, the following research questions were formulated:

  1. To what extent does implementing OLAP improve the accuracy and timeliness of decision-making in specific banking functions (e.g., loan risk assessment, credit card marketing, fraud detection)?
  2. What are the major technical, organizational, and cultural challenges faced by banks when implementing OLAP systems?
  3. How can banks leverage OLAP insights to develop personalized financial products and services, targeting specific customer segments?

1.5 Research Hypothesis

The following research hypothesis was developed and tested for the study:

Ho: There is no statistical significant relationship between online analytical processing and decision support in a banking system.

1.6 Significance of the Study

The study is important for many reasons. The following are the major stakeholders this paper through its practical and theoretical implications and findings will be of great significance:

Firstly, the paper will benefit major stakeholders and policy makers in the business administration sector. The various analysis, findings and discussions outlined in this paper will serve as a guide in enabling major positive changes in the industry and sub-sectors.

Secondly, the paper is also beneficial to the organizations used for the research. Since first hand data was gotten and analysed from the organization, they stand a chance to benefit directly from the findings of the study in respect to their various organizations. These findings will fast track growth and enable productivity in the organisations used as a case study.

Finally, the paper will serve as a guide to other researchers willing to research further into the subject matter. Through the conclusions, limitations and gaps identified in the subject matter, other student and independent researchers can have a well laid foundation to conduct further studies.

1.7 Scope of the Study

The study is delimited to GT Bank. Findings and recommendations from the study reflects the views and opinions of respondents sampled in the area. It may not reflect the entire picture in the population.

1.8 Limitations of the Study

The major limitations of the research study are time, financial constraints and delays from respondents. The researcher had difficulties combining lectures with field work. Financial constraints in form of getting adequate funds and sponsors to print questionnaires, hold Focus group discussions and logistics was recorded. Finally, respondents were a bit reluctant in filling questionnaires and submitting them on time. This delayed the project work a bit.


1.9 Organization of the Study

The study is made up of five (5) Chapters. Chapter one of the study gives a general introduction to the subject matter, background to the problem as well as a detailed problem statement of the research. This chapter also sets the objectives of the paper in motion detailing out the significance and scope of the paper.

Chapter Two of the paper entails the review of related literature with regards to corporate governance and integrated reporting. This chapter outlines the conceptual reviews, theoretical reviews and empirical reviews of the study.

Chapter Three centers on the methodologies applied in the study. A more detailed explanation of the research design, population of the study, sample size and technique, data collection method and analysis is discussed in this chapter.

Chapter Four highlights data analysis and interpretation giving the readers a thorough room for the discussion of the practical and theoretical implications of data analyzed in the study.

Chapter Five outlines the findings, conclusions and recommendations of the study. Based on objectives set out, the researcher concludes the paper by answering all research questions set out in the study.



Inmon, W. H., & Hackathorn, R. D. (1994). Using the Data Warehouse. New York: John Wiley & Sons.

Kimball, R., & Ross, M. (2002). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. New York: John Wiley & Sons.

Sharda, R., Delen, D., & Turban, E. (2014). Business Intelligence: A Managerial Perspective on Analytics. Upper Saddle River, NJ: Prentice Hall.