How to design the master data architecture: Findings from a case study at Bosch

Risk Compliance and Master Data Management in Banking – A novel BCBS 239 compliance action-plan proposal

For some years now, master data has become extremely relevant to business success and continuity in an increasingly competitive and global business environment. The banking sector is one example of how the implementation of well-structured and designed master data management policies and initiatives is crucial for reaching positive results. One of the areas in which banks need to ensure extremely fruitful master data management approaches and data governance procedures is when dealing with risk-related data, as it not only ensures accurate and well-supported management and decision-making, but also because banks are required to do so by imposed regulations, such as the BCBS 239. Drawing on a DSR methodology supported research project, where banking and IS-related expertise was continuously merged with existing theoretical knowledge on MDM and BCBS 239 related topics, and a permanent focus on the technical and functional complexity associated with implementing master data management and well-established data governance procedures that ensure regulatory compliance, we propose a novel, six-phase action plan that will allow banks to ensure compliance with BCBS 239 and, consequently, ensure efficient and effective risk data management and reporting.

A new fuzzy approach for managing data governance implementation relevant activities

Purpose

The purpose of this paper is to propose an approach for managing relevant factors and activities for implementing data governance in an organization. The process of assessing the establishment of data governance in an organization is intrinsically imprecise, due to the characteristics of new problem settings, particularly in relation to newly generated alternatives or vaguely defined qualitative assessment criteria.

Design/methodology/approach

To reject the inherent subjectiveness and imprecision involved in the evaluation process, the authors use the concept of fuzzy logic in this approach for developing the assessment model and analyzing the model for allocating the management efforts in the most efficient way to improve the data governance deployment level.

Findings

This paper identifies relevant factors and activities for implementing data governance in an organization and evaluates the state of data governance based on causal relationships between influential factors. In this study, factors are prioritized for effective allocation of limited management efforts in any improvement plan.

Research limitations/implications

The interrelationships among factors are contextual and based on the perceptions of experts who may be biased as per their background and area of expertise. Meanwhile, lack of a data governance plan may cause failure during its implementation in an organization, as the worth of an organization's data will not be determined precisely. The paper has tremendous practical implications for organizations that intend to implement the data governance program and evaluate its state to design an improvement plan.

Originality/value

The paper proposes an approach for implementing data governance in an organization faced with limited resources for improvement.

Master data management and its organizational implementation

Purpose

Master data management (MDM) aims to improve the value of an organization’s most important data, such as customer data, by bridging the silos between organizational units and information systems. However, incorporating data management practices into an organization is not a simple task. The purpose of this paper is to provide a new understanding of the challenges in establishing and developing the MDM function within an organization.

Design/methodology/approach

This papers report an ethnographic study within a municipality. The data were collected from two consecutive MDM development projects over the time period of 32 months by observing MDM-related activities and interviewing appropriate actors. Observations, interviews, and impressions were documented to a diary that was later qualitatively analyzed. Various project documentation were also used.

Findings

In total 15 challenges were identified. Seven of these were not identified earlier in the literature. New challenges included legislation-driven challenges, mutual understanding of master data domains, and the level of granularity for those domains. Eight issues, such as data owner and data definitions, were MDM specific, others being more generic. All of the issues were identified as preconditions or as affecting factors for the others. Three of the issues were identified as pivotal. The issues emphasize strong alignment between the complex concept of MDM and the organization adopting it.

Research limitations/implications

This research was based on a single qualitative case study, and caution should be exercised with regard to generalizations. The findings increase understanding about the complex organizational phenomena. The study offers public sector and private sector practitioners insights of the organizational issues that establishing a MDM function can encounter.

Originality/value

The issues discovered in the research shed light on the strong alignment between the complex concept of MDM and the organization. The results of this study assist researchers in their endeavor to understand the organizational aspects of MDM, and to build theoretical models, frameworks, practices, and explanations.