MD3M: The master data management maturity model
Introduction
Information and data became increasingly important and a crucial competitive factor over time. Additional to the three sector theory, a quarternary sector has been defined. That additional sector contains information-based activities (Beniger, 1986, Kenessey, 1987). This shows the development towards an information-centered economy. Research in this area therefore has a significant economic and societal relevance.
Master data are the data describing the most relevant business entities, on which the activities of an organization are based, e.g. counterparties, products or employees. In contrast to transactional data (invoices, orders, etc.) and inventory data, master data are oriented towards the attributes. They describe the main characteristics of objects in the real world. Single master data entities are rarely being changed, for instance the properties of some kind of material. Instances of master data classes are relatively constant, especially if they are compared with transactional data. Master data is the reference for transactional data. There would not be a single order or delivery without master data (Otto & Hüner, 2009).
Starting from a particular size, every organization has to deal with the question of how to integrate master data from different units or areas. Furthermore, the organizational setup of the firm plays a big role. Does the firm operate in a data-intense business? In areas with strict regulations on the traceability of events and accountability (like pharmaceutical industries, finance, trading), with data as a main source for added value (like the finance industry), or with the urgent need for efficiency and agility (production, technical industries), a systematic integration of data is crucial for the business (Wegener, 2008). The recent development shows that organizations have to cope with short innovation cycles and market launch times. Furthermore, the complexity is increasing due to globally harmonized business processes and global customer services. This results in shorter decision cycles basing on more information (Kumar, 2010, Otto and Hüner, 2009).
As stated before, there are many reasons why a company might consider Master Data Management (MDM), which we define as “the management of the consistent and uniform subset of business entities that describe the core activities of an enterprise”. It is general consensus and common sense that correct, available and timely data are of great importance and can be a competitive advantage (Borghoff and Pareschi, 1997, Kahn et al., 2002, Otto and Hüner, 2009). However, many companies have insufficient data management strategies. Especially bigger companies struggle with the huge amount of data and have no sufficient strategy to exploit the data (Davenport and Prusak, 2000, Otto and Hüner, 2009).
The objectives of this research paper are of both a practical and an academic nature. From a corporate point of view, the objective is to give organizations the possibility to assess their own MDM maturity and benchmark against other organizations. This situation leads to the following research question: How can a company’s current state in Master Data Management be measured to identify potential improvement areas?
The remainder of this paper is structured as follows. The following chapter introduces the research approach that was followed and on which the whole research is based on. Then the maturity model is presented. Afterwards, the validation is presented. The research is discussed and conclusions are drawn as well as fields for further research presented.
Section snippets
Research approach
To assess the maturity of the master data management of an enterprise, we propose the MDM maturity model. The MDM maturity model is a means of assessing the whole process of master data management including the data point of view and also focusing on the whole operational process.
In order to keep this research consistent according to academic requirements, the development will be based on guidelines and frameworks from academia. As already mentioned in earlier sections, this research is based
Maturity levels
The following Table 1 gives an overview of the amount of maturity levels and their meaning based on IT Governance Institute, 2000, Butler, 2011, Kumar, 2010, Loshin, 2010, respectively.
From this information, the decision was taken to exclude a level with a non-existing maturity. The ignorance of existing issues within the organization concerning master data management will be considered as no maturity at all and therefore not be considered in the matrix as a unique level. Hence, the first level
Case study: NRGCORP
To validate the model and provide practical information, the whole process was executed at a large case company. This section describes the practical implementation of the before mentioned MDM Maturity Model. We will first introduce the case company, then describe the assessment process and, finally, elaborate on the MD3M assessment results as shown in Table 3.
The case company where our research took place, will now be described anonymously where possible. We will refer to the company as
Validation
To ensure validity of this research, the research was evaluated along Yin’s criteria (Yin, 2003). Yin proposes for single case study research to ‘Construct Validity’ through the usage of multiple evidence sources and through establishing a chain of evidence (Yin, 2003). This takes place when collecting data. The MD3M is based on several sources of academia and models from practice. These were investigated and compared to serve as a basis for the developed model. Additionally, it is suggested to
Discussion
The MD3M consists of five levels of maturity. The levels’ descriptions are kept rather broad because they are used to describe the maturity level of very distinct capabilities situated in different topics. The level aims at being able to describe all capabilities properly.
The first level is called ‘initial’. It describes a first awareness on the topic of MDM for the different focus areas. The second one displays a ‘repeatable’ state, meaning that insular measures have been initialized in
Conclusion and future work
Our extensive case study provides an example of iterative human learning, behavior and collaboration resulting from technological needs in a large-scale infrastructural network. However, one possible limitation of the research at hand is the small amount of case companies. This might lead to restricted generalizability. Therefore, a higher amount of case studies at different companies would uncover possible shortcomings. Thus, applying the MD3M at different companies would give comparable
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2022, HeliyonCitation Excerpt :Thus, if one of the main challenges for organizations is to devise and implement mechanisms and practices that allow efficient management of their data (Parviainen et al., 2017), the other is to implement the same approach for their master data, which is normally generated in a set of different business/functional areas or systems (Ofner et al., 2013). According to Spruit and Pietzka (2015), for organizations that operate in data-intensive scenarios, in extremely complex regulatory environments, and that have an urgent need to be continuously efficient and agile (like the financial sector), it is even critical to implement focused and structured master data management practices, as data tends to represent the main source of added value. Despite being well characterized in the existing literature, from our perspective, one of the most straightforward definitions of the concept of Master Data Management (MDM) is presented by White et al. (2006), according to whom “MDM is a workflow-driven process in which business units and IT collaborate to harmonize, cleanse, publish and protect common information assets that must be shared across the enterprise.
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