Master Data Management - Case Study Example

Master Data Management Introduction: Master Data management is very important in today’s complex and competitive global context because increasingly, suppliers, trading partners as well as customers are all becoming more demanding in their requirements, while Government and financial regulations are simultaneously becoming more stringent. MDM essentially deals with the notion of multiple data domains, multiple relationships among them and the multiple usage styles; in security professions in particular, it is even more important to keep a tab on data and its efficient usage, because this could impact upon the efficiency of operations related to security. Since technology has become an integral part of all business operations, the rationale underlining this study is that master data management is likely to play a significant role in improving security operations. As a result, investigating master data management and assessing its relevance to the security profession offers scope to evaluate how best it may be applied in this context. Since MDM is also among the recent important developments that have been emerging in the field of business, this course offers a valuable opportunity to explore the implications of MDM in my current profession and the resultant impact it is likely to have. Research proposal: During these tough recessionary times, Zomes (2009) recommends that implementing MDM may be one of the best ways to deal with the financial and other difficulties that arise, because it offers a cost effective solution for data applications. The price for these applications has reduced from where it was originally at $1 million for the software and $3 to 4 million for implementation services to the sub $500,000 range. This study therefore proposes to examine the advantages of implementing MDM solutions within the security industry and the concomitant beliefs that are likely to ensue. On this basis, this study will examine the following statement: Can Master Data Management can be efficiently implemented in the security industry? Background: The rapid advances that have been made in information technology have resulted in the availability of a plethora of information that is available in the form of data, most of which must be properly structured and accessible in order to be effectively used. It may be necessary to sort out the data and structure it properly within databases so that it can be accessed efficiently and used to advance the competitive advantage of an organization. In the case of an organization that deals with the issue of security, managing data properly is even more important because it could be invaluable in tracing security leaks and maintaining a tab on important information related to maintenance of security, which could result in an overall improvement in the operations of the firm. Master Data management is one of the most commonly discussed aspects of business intelligence and is actually one dimension of an enterprise management strategy (Politano, 2008). According to Griffin (2005) the technology behind the MDM framework is an explicit, layered path on which data moves through the framework. First, there is the data integration layer which retrieves data from all applicable systems and comprises tools for messaging, cross referencing, mapping and process management. Once the data is cleansed and transformed in the integration layer, it is then aggregated and is distributed into the data management layer. This is the main site of the Company’s data architecture; it houses the master data model that symbolizes the company’s information architecture. It is the central site where all data is consolidated and available in its true form. All the data which has been catalogued and aggregated then remains here until it is ready to be sent on to the data presentation layer, when the data is called for. Master Data management is even more important in the context of threats posed to data security. As Oltsik (2008) points out, recessionary trends have only further increased the threats to confidential data, and there is a need for incorporation of better security measures to protect the data. Studying relevance of MDM, together with its applicability is therefore quite significant in the context of data security, especially in the new global business atmosphere of intensive competition. Literature review: The best way to begin working on master data is to achieve a logical understanding; since master data is comprised of hundreds of elements, a four stage segmentation process needs to be followed first, i.e, (a) identification: of elements in the data that determine uniqueness; for example, a combination of keys such as social security number and customer number (b) Common elements: comprising elements that are commonly queried but not necessarily unique, such as attributive information to complement the identification level (c) Extended internal: comprising elements that are not heavily queried, neither are they used by a large group, especially if they are sourced from internal systems (d) Extended external: Outside data that is brought in house; i.e., data such as credit ratings, shared data, address correction files, etc. According to Power (2009), business process management should be a part of implementing Master data management, because it allows an organization to model and manage several critical applications which span different applications, departments and business partners, while MDM helps to create a more sophisticated process to deal with the same applications. As he points out, where an application is concerned, the BPM application allows all the data that is required for the process to be passed on. In the case of individual records, the MDM hub is accessed and it cleanses and standardizes the data in order to ensure that the new record that is created through the business process will not be a duplicate. This will also involve a risk management application that checks credit before a new record is added. In order to successfully implement an MDM project, it is necessary to avoid certain pitfalls, which could lead to a failed program otherwise. Cochrane(2009) has identified five pitfalls as follows: (a)Failing to define a corporate standard or the dimensions of the organization. When this happens, an organization tends to start an MDM application by focusing on the definition of data entities and elements rather than also defining organizational dimensions. (b) Failing to focus on business processes. When this happens, there are no clear business processes in place, starting from the point of data entry through to its consolidation and reporting, so that the flow of data is interrupted and its integrity is not preserved (Cochrane, 2009). (c) Lack of an overarching data governance discipline. If this is the case when an MDM process is implemented, its applicability will be restricted to solving a present problem, and will not be an ongoing process, wherein definite policies are set out in regard to decision rights, data stewardship, accountability, as well as corporate policies and standards. Moreover, the governance components for both pre-deployment and post-deployment stages will not be addressed. (d) Starting off big but ending up small. This happens when organizations are tempted to break down all the disjointed processes and start over afresh, at considerable expense, in order to create a central point of reference to manage all the data. While the initial idea to start off big may be creditable, it may not really be relevant in practice because if the MDM project is to be successful it must be managed in specific dimensions within a manageable scope. If the scope of the planned project is too big, but in practice cannot be handled except in small units, then it will not be successful.(Cochrane, 2009) (e) lack of validation of data: If an MDM application is to be successful, it would require validation of data at different points in the architecture. If the validation process is restricted only to the inputs and outputs of the MDM solution, it may not function efficiently; hence end to end data validation testing must be completed. Research Questions and Objective: The objective of this study is to assess how well MDM can be applied within a security business. Hence, the research questions that are posed in the study are as follows: (a) How can MDM be applied in the security industry? (b) How can an MDM project be most efficiently implemented? Methodology and methods of enquiry: This study will be based on a qualitative method but utilize a quantitative analysis method, because the subject of the study is MDM application at an organization that is in the business of security. Qualitative lend themselves well to those studies where a special understanding of the issues is sought to be achieved and in the generation of a level of information that is fairly comprehensive. (Troachim, 2001). Qualitative data is primarily concerned with the collection of individual opinions and the preferred method that is often selected for use with this kind of research is structured or semi structured interviews. Quantitative data research is useful when general trends have to be identified or measured (Trochim, 2001), when the issues under study impact upon a significant number of people and may be widely applicable to the general population. A quantitative research method aims to gather larger volumes of data in a numerical format, which can be subjected to statistical analysis and where there is no ambiguity in the data, which is measured on an ordinal scale. The research strategy proposed to be used in this study is survey questionnaires. There may be some problems associated with the qualitative interview method, involving a detailed structured or semi structured interview in the case of this particular study. For example, this method is likely to be time consuming when considered against the results sought to be achieved. The thrust of this study is to discover overall patterns within a large corporation that is in the business of security, within a limited time frame. The primary data is collected from the questionnaire/survey, which is a favoured tool used in research because it can provide a cheap and effective method of collecting data in a structured and manageable way. (Wilkinson and Birmingham, 2003). Using a Likert style questionnaire would also help to rank subjective participant attitudes in a numerical format, because for every statement provided in the questionnaire, a range of responses can be provided with a numerical code against them. For instance, the degree of agreement could range from “Strongly agree” to “Strongly disagree” on a numerical scale of 1 to 5. The participants in this study are proposed to be gathered from various levels of one corporation that is in the business of security. The questionnaire would contain questions about the present processes of data management in existence within the organization and how the MDM system is being used. The questions would also aim to assess how efficiently the implementation has taken place and whether any of the pitfalls in implementation which are included in the literature review are also evident in the participant organization. Ethical considerations: Saunders, Lewis and Thornhill (2003: 311) highlight ethical concerns that may arise in a research study, such as the need to maintain the privacy of individuals and to solicit only voluntary participation - allowing the right to withdraw any time, the need to maintain the confidentiality of the data and the objectivity of the researcher. Therefore, the consent of the participants will be solicited first before gathering their views, in order to ensure that they have no objections to participate in the study. The participants will also be informed that the study is for research purposes only and that they can respond freely without the danger of their identity being revealed. This is important to ensure, because most of the participants would be individuals working in the corporation, who are likely to be afraid of adverse consequences arising out of their superiors becoming aware of their criticism, if any, of internal data management processes within the organization. In order to preserve the anonymity of the respondents, they will not be required to provide their names as a part of the questionnaire, neither will they be expected to state which department they are working in, but only their gender and age. Since both the permission and free participation of respondents will be ensured and their anonymity preserved, the ethical considerations necessary for the study may be satisfied. Special resources required: This study basically examines data management processes within the organization, therefore it will be necessary to gain access to the central database, as well as to other business processes that are involved in the MDM implementation process. As a result, it may also be necessary to have access to any special passwords that may be required to log into the databases. New knowledge to be acquired: This study is likely to be quite an eye opener on Master data management, because this is a fairly new phenomenon developing in the field of data management and business intelligence. It is likely to shed light on effective ways and means to manage the vast amounts of data that most corporations have to deal with, in view of the flood of information that is available through the Internet. It is likely to provide insight into the relevance of MDM in the security industry, i.e, the collection, cataloguing, aggregation and access to data and the manner in which all of the above are to be regulated. This project could be enlightening on matters as the construction of the MDM architecture, pitfalls to be avoided if desired objectives are to be achieved, the synchronization and consolidation of MDM data and how access and use of business intelligence can be improved through the use of MDM. The convergence of enterprise performance management and master data management may be yet another aspect that could be unearthed through this study. Since this study is set within an organization that specifically deals with security, it is also likely to provide information on an area that is currently sparsely researched – the implementation and impact of MDM operations in the actual day to day business of an organization. Since this study seeks to gather the views of participant employees and managers within an organization that is implementing or has implemented MDM, it could provide valuable feedback on the practical implications of MDM implementation, and the problems, if any, that are associated with its use. It could also serve to provide some valuable pointers for other organizations considering MDM implementation on what to avoid in order to achieve a better result. References: Cochrane, Mike, 2009. “Master data management: Avoiding five pitfalls of MDM; A careful strategy will position your MDM program for success”, Information Management, 19(1):49 Dych, Jill and Levy, Evan, 2006. “Serving many masters: a closer look at MDM: as important to business strategy as EDW was, the processing of the item master was ultimately much more critical to business operations”, DM Review, 16(10): 29 Griffin, Jane, 2005. “Building a data management strategy Part 2: technology”, DM Review, 15 (11): 58 Oltsik, Jon, 2008. “Security industry moves forward on job security”, CNET News, Retrieved November 24, 2009 from: http://news.cnet.com/8301-1009_3-10118105-83.html Politano, Tony, 2008. “Performance management and MDM convergence”, DM Review, 18(3):20 Power, Dan, 2009. “Business process management and MDM; BPM is the foundation of a successful strategy for integrating an MDM hub in the enterprise”, Information management, 19(1): 47 Trochim, W. , 2001. The Research Methods Knowledge base (2nd Ed.) Atomic Dog Publishing Wilkinson, D. and Birmingham, P., 2003. Using research instruments: A Guide for researchers, Routledge Zomes, Aaron, 2009. “MDM for tough times; 5 trends to strengthen organizations during recession”, Information Management, 19(6): 20 Read More