Business Intelligence & Operational Data

The research begins by highlighting the central challenge: the overwhelming size of operational databases in US credit unions. These databases store extensive transactional data, encompassing checking and savings accounts, credit cards, loans, and mortgages, accessed via various channels such as ATMs, phones, and the internet. The sheer volume of data stored within these operational databases (Dass, 2006; Jukic, 2006), makes it exceedingly difficult for human analysts to effectively utilize this information for strategic decision-making. This data overload significantly hinders the credit union's ability to leverage valuable member transaction histories for informed business decisions. The inability to effectively analyze this readily available data represents a significant obstacle to improving efficiency and growth within the credit union sector. The research posits that this data, if properly analyzed, holds the key to unlocking significant opportunities for improvement.

The document then examines existing research on credit union growth, emphasizing the common bond as a crucial factor. Studies typically attribute credit union growth to either asset growth or membership growth (Goddard, McKillopp, & Wilson, 2002). The common bond, a defining characteristic of credit unions, can act as both an opportunity and a constraint. It limits membership, hindering growth for credit unions nearing their full potential membership (Walter, 2006; Goddard et al., 2002). Conversely, the common bond presents an opportunity, as a significant portion of potential members remain untapped (Freeman, 2008). Research suggests that growth is more significantly tied to credit union assets and the ability to increase business with existing members, rather than solely attracting new members (Goddard et al., 2002). This analysis lays the groundwork for understanding how business intelligence can play a pivotal role in supplementing traditional growth strategies.

The core of the research problem centers on the application of business intelligence (BI) to address the challenges of data analysis within credit unions. The study defines BI as both a process and a product. The BI process encompasses the methodologies used to create useful information for organizational success (Jourdan, Rainer, & Marshall, 2008). The BI product is the resulting information itself, enabling organizations to better understand current behaviors and predict future trends across various aspects of their business environment (Vedder et al., 1999). The study emphasizes how BI can provide credit unions with valuable insights into trends and relationships within their operational data, facilitating improved customer management and satisfaction (O'Hara & Brohman, 2002). By effectively managing, analyzing, and integrating data into the decision-making process, credit unions can achieve a competitive advantage (Gorla, 2003), improving member retention and market penetration (Dass, 2006). The study underscores the potential for BI to significantly enhance decision-making related to membership and asset growth, ultimately improving overall credit union performance.

The intended audience for this research comprises information technology (IT) managers within credit unions responsible for membership and asset growth. The study highlights the direct benefits of using BI for both supporting and planning activities (Vedder et al., 1999), including managing IT resources to support organization-wide BI initiatives. Additionally, the research is relevant to any credit union manager or executive who utilizes business intelligence for decision-making. The study acknowledges that effective pricing of products and services, along with focused marketing and promotions, are essential drivers of membership and asset growth (Goddard et al., 2008). Neglecting this aspect can lead to significant market share losses (Bauer, 2008). This section emphasizes the broad applicability of the research and its potential to significantly impact various levels of credit union management.

The literature review begins by exploring existing research on credit union performance. A significant portion focuses on the National Credit Union Administration's (NCUA) CAMEL rating system. This system evaluates credit unions across five dimensions: Capital adequacy, Asset quality, Management, Earnings, and Asset/liability management. CAMEL ratings range from 1 (sound) to 5 (insolvent), providing a measure of credit union financial health (Fried, Lovell, & Yaisawarng, 1999; "NCUA letter to credit unions," 2000). While not intended as a direct performance comparison, CAMEL ratings are frequently used as an indicator of credit union performance in the literature (Bauer, Miles, & others). The review further examines the impact of regulatory changes and common bond requirements on credit union growth, including studies examining how changes impact the growth of both membership and assets (Goddard et al., 2002; Goddard et al., 2008; Leggett & Strand, 2002). These studies reveal that credit union growth is not random, but rather systematically influenced by various factors. The existing literature helps establish a baseline for understanding credit union performance and how different elements contribute to its success or challenges.

The literature review then delves into Business Intelligence (BI) solutions. The review establishes a conceptual understanding of BI, defining it as the use of technology to collect and effectively utilize information for improved business effectiveness (Jaffri & Nadeem, 2004). It highlights key BI technologies including data warehousing, data mining, and online analytical processing (OLAP). Data warehousing is identified as crucial for making operational data more accessible for analysis (O'Hara & Brohman, 2002). Data mining is described as a computer-assisted process for extracting actionable information from data (Gunnarsson et al., 2007), while OLAP facilitates real-time analysis of pre-aggregated data (Hamel, 2005). The literature review explores the challenges of implementing data warehousing, including data preparation and the effort involved in moving data from operational databases to data warehouses (Gunnarsson et al., 2007; Little & Gibson, 2003; Jukic, 2006; Castellanos et al., 2009). This section lays the groundwork for understanding how these BI techniques can contribute to improved credit union performance.

The literature review presents findings from relevant case studies, analyzing how BI has been implemented in similar contexts. The review notes a scarcity of studies directly addressing BI applications within the credit union industry, with only a limited number of relevant references (O'Hara & Brohman, 2002; Dass, 2006; Jaffri & Nadeem, 2004). To compensate, the review incorporates insights from studies on BI applications in the broader banking and financial services sectors (Dass, 2006; Jaffri & Nadeem, 2004). Case studies such as the one by Jaffri and Nadeem (2004) which examines a BI implementation at the State Bank of Pakistan's Credit Information Bureau and demonstrates the use of data warehousing and OLAP, are examined. The review emphasizes the challenges of applying BI in organizations, highlighting the importance of managing, analyzing, and integrating data effectively into the decision-making process (Gorla, 2003). This synthesis provides a critical context for applying BI principles to the credit union setting and highlights the potential of using BI to understand member behavior, identify their needs and create better products and services (Jaffri & Nadeem, 2004).

The review details the research methodology and literature search strategy employed to identify relevant sources. The research employed a structured approach, categorizing sources into themes related to credit union performance, business intelligence solutions, and BI case studies. Databases such as Academic Search Premier, Business Source Premier, EconLit, and others were searched along with Google Scholar to obtain a wide range of information (Obenzinger, 2005). The research utilized a synthesis of two fields review methodology to gain insights. The study acknowledges the limited existing research directly addressing BI within the credit union context, indicating the need for an inferential approach drawing on similar or related research (Obenzinger, 2005). Key sources include multiple credit union industry publications such as the Credit Union Directors Newsletter, Credit Union Journal, Credit Union Magazine, and Credit Union Management, along with Federal Reserve Bank publications. The methodology and resource identification are clearly documented to ensure transparency and reproducibility of the research. The study recognizes the limitations posed by the limited amount of specific research related to BI within credit unions, necessitating the synthesis of information from related fields.

The proposed framework for implementing business intelligence (BI) in credit unions is presented as a multi-phased process. The initial phase focuses on defining business objectives, which is critical for guiding the entire BI implementation. It emphasizes that a clear understanding of the desired outcomes, such as improved membership growth or enhanced asset management, is paramount. This phase includes detailed planning and the alignment of IT infrastructure with overall business goals (Gorla, 2003). Securing top-level management support is crucial because many technical decisions, such as hardware platform selection, database management system choice, and data refresh frequency, need to be made during this phase. The effectiveness of the entire BI implementation hinges on the clarity of these initial objectives and the level of organizational commitment from leadership.

The second phase involves building and populating the data warehouse. The data warehouse serves as the central repository of information, providing structured data for business analytics. This phase requires considerable effort, often estimated at 70% or more of the total BI implementation effort (Castellanos et al., 2009; Jukic, 2006; Gunnarsson et al., 2007). The significant effort is mainly attributed to the challenges of data preparation. This involves identifying and mapping existing operational database structures, accommodating different data types and time intervals, and ensuring data accuracy and consistency (Little & Gibson, 2003). The complexity of this phase increases proportionally with the diversity of data sources and the intricacies of organizational operations. Effective planning and resource allocation during this phase are critical to the overall success of the project.

The final phase focuses on data analysis and predictive modeling using Online Analytical Processing (OLAP) and data mining. This phase describes an iterative process combining the summarization capabilities of OLAP with the model-building potential of data mining. The process begins with OLAP queries to identify suitable segments within the membership base and establish key decision points. These decision points then inform the development of predictive profiles using data mining. These profiles enhance understanding of how specific decisions align with business objectives, particularly in driving membership and asset growth. The outcomes of data mining, specifically the creation of predictive models, are then fed back into refining future OLAP queries, creating a continuous improvement cycle. This iterative approach allows for continuous refinement of the analytical process and ensures that the BI solution remains aligned with evolving business needs and objectives.

In summary, the proposed framework is designed to allow credit unions to leverage their existing operational transactional and member-specific data to support membership and asset growth. This framework uses both the process and product of business intelligence. The BI process is the method by which a credit union develops useful information (Jourdan et al., 2008), while the BI product is the resulting information that allows a credit union to understand current and predict future member behaviors (Vedder et al., 1999). The successful implementation will ultimately result in a robust BI system capable of providing data-driven insights to facilitate informed decision-making, leading to enhanced competitiveness and increased member value. The framework provides a structured approach for credit union IT managers to navigate the implementation of a BI solution, offering a clear pathway to achieving significant organizational growth.

The conclusion reiterates the highly competitive environment in which credit unions operate. To maintain competitiveness and remain viable against other retail financial services organizations, credit unions must actively pursue strategies to increase both membership and assets. The study's purpose was to demonstrate the potential of leveraging already existing operational data within credit union information systems. The implementation of a business intelligence (BI) solution is presented as a key method for achieving this goal. This highlights the potential of transforming existing operational data into actionable insights through the use of a data warehouse, data mining, and OLAP processes. The successful integration of this data-driven approach into decision-making and strategic planning is expected to significantly enhance credit union competitiveness and increase member value.

The conclusion emphasizes the transformative potential of business intelligence (BI) in leveraging the operational data already stored within credit union information systems. The study successfully demonstrates that credit unions can effectively use their existing operational data to gain a substantial competitive advantage. A key element of this approach is the implementation of a comprehensive BI solution. This involves a multi-step process that includes the creation of a data warehouse, the application of data mining techniques to uncover meaningful insights, and the use of OLAP for real-time analysis of the resulting data. The study underscores the importance of this transition from raw operational data to a fully functional BI system to support improved decision-making and strategic planning.

The conclusion highlights the practical value of the proposed framework for credit union IT managers. The framework provides a clear and actionable pathway for these managers to plan and execute the implementation of a BI solution specifically aimed at driving membership and asset growth. This involves not just the technical aspects of data warehousing, data mining, and OLAP, but also the critical step of securing high-level management support for the project. The framework emphasizes the importance of aligning technical decisions with overall business objectives. The framework offers a practical and effective approach for credit union IT managers to strategically leverage their existing data resources for substantial organizational growth and enhanced competitive positioning in the market.