4 Institutions and Their Data Management and Reporting Services

 

Technology is a core factor in information governance and management, especially for quality assurance, suitability, and compliance with regulatory requirements. The integration of computing technologies such as network and storage systems, software, networks, devices, and applications can provide increased efficiencies and benefits. Many organizations are realizing the benefits of improved information management through the adoption of information management technologies that are rapidly becoming a part of business decision making. One potential model for information management and governance is shown in Figure 2.

In this first case study, we consider four institutions, each with different characteristics, mission, culture, and needs, but which are converging on two core principles: (a) an effective and efficient process for collecting and managing the large volume of counterparty data that must be processed and shared in order to effectively support their day-to-day operations; and (b) an audit trail approach to improving internal controls and in determining the risk of financial fraud. Our focus here is on the banks as a sample of organizational users. We note that the banks exhibit both internal and external characteristics of a data management and audit trail framework. The four institutions are banks in four regions: the South, the Northeast, the Pacific Northwest, and the Southwest.

All four banks had an IT function that fulfilling basic organizational objectives. They had established a need for more timely and accurate internal accounting processing, enhanced counterparty data, improved customer service, and compliance with federal, state, and local regulatory requirements. To meet their analytic data requirements, the banks had several options. First, they could consolidate their disparate data processing departments into a single data processing department with unified messaging, data collection, accounting, and reporting infrastructure. Second, they could hire an outside consultant to develop custom internal software packages.

By deploying internal software solutions, the banks avoided many of the organizational design challenges associated with implementing the consolidation initiatives. First, the deployment of these new solutions improved performance and increased customer satisfaction. Second, the improvements in operational efficiency enabled greater profit margins for the banks. Third, by combining internal software and analytic data sets, the banks were able to reduce total outsourcing costs. Fourth, by combining internal and external systems, the banks were able to better protect the assets of the institution.

The lessons learned from these experiences are critical to the development of any business. Consolidation initiatives require effective governance framework, a clearly defined strategic plan, a clear and defined objective, and a clear identification of risks. When developing our assessment process for banks we considered two major points. One was to determine which organizational area was the source of the problem and another was to develop a governance framework for each area. While we initially focused on the source areas, as this effort progressed we considered several other areas as well. Ultimately, however, the top three areas were due to the complexity of the internal and external management control processes, the significant risk inherent in relying on external sources, and the lack of a coordinated strategic plan.

Developing an effective data management framework is the first step toward developing an effective data management and reporting governance framework. In the absence of a strong and consistent data management framework, banks are unable to effectively monitor and report the health of their assets. The second step involves the implementation of an effective data management and reporting governance framework. This is particularly important for small banks, which have limited staff and poor IT resources.

A number of innovative approaches to make data management report have been developed since the early 1980s. One popular approach is to use an information-discipline approach to improving information management. Information science and computer science concepts such as a common data model, a decision tree, and a data model architecture have emerged to provide banks with a common data model to apply to all their data collection activities. Another approach called asset information management (AIM) has evolved from the earlier application of accounting techniques to provide a more complete picture of an enterprise's assets. In this system, a user creates a data model from unique attributes such as customer profiles, product specifications, and organizational needs. These attributes can then be linked to one another using relational and algebraic tools to build a database that is then used to perform complex analysis.

Developing an efficient data quality tool requires the coordination and collaboration of different personnel in different departments. This is particularly true for small banks that do not possess a large number of employees dedicated to the task. Data quality tools must be easy to install and implement, and they must have the capability of processing billions of records within a given time frame. In addition, data quality tools must have a good reporting functionality and be adaptable to a wide variety of sources.