The impact of Master Data Management in an organization to improve decision making

This research deals with assessing the importance of Master Data Management in an organization and the impacts it has when making decisions. I will present the methodological framework which allows a centered master data to flow to different systems. This research will use the qualitative approach and interpretive paradigm.

Master Data Management – MDM

Keywords: Master Data Management, Data Quality, Information Management, Data Governance

Background of the study

The focus to Master Data Management is to provide relevant information for decision making using the various systems in the organization. Based on the increasing number of expected benefits of systems that they will deliver a single version of key organizational entities. “MDM represents the set of policies, governance, standards, processes and tools that define and manage the master and reference data of a business organization to provide a single point of reference.“(Subotic, Jovanovic, Poscic, 2014)

Years of using and retaining data in different data stores have led to conflicts in data descriptions, in the way data is structured, and the values of data, which makes it impossible or hard for an organization to understand and properly use its key data(Cleven & Wortmann).

Data are used in almost all the activities of companies and constitute the basis for decisions on operational and strategic levels. Poor quality data will have significantly negative effects on the competence of an organization, while high quality data are often crucial to a company’s success (Haug, Zachariassen & van Liempd).

Integrating those business definitions and data records across business lines and across subsidiaries is no simple task, demanding rearranging of data ownership and governance, however also requiring advanced technology for policies and business rules to be enforced(Scheidl 2011).

The goal of MDM is to create and maintain consistent and complete business data for all stakeholders in a controlled and single-view capable manner across the whole organization which will help them figure out the improvement areas so they become more efficient and competitive. MDM is meant to provide organizations with the ability to integrate, analyze and exploit the value of their data assets, regardless of where that information was collected (Milanov & Njengus 2012).

Research Problem

Organizations deal with different data which may be scatted across the whole organization where systems do not communicate with each other causing the organization to make decisions based on data that is not accurate.

Research Objectives

Master Data Management seeks to consolidate data that resides in various systems that do not communicate with each other, in such a way that accurate and up to date organizational data is available in a single place (Reichet, Otto, Ostele, 2013).

This research will discuss on how Master Data Management can manage the organizations data and outline the benefits of using MDM which will permit the organization to understand their key data. Consequently, the lack of a suitable master data management may lead to severe problems like operational faults, inadequate decision making. The bigger the company, the bigger the issue of managing the data will be. If companies grow the data landscape gets more complicated and managing data becomes an issue that is hard to deal with (K Pietzka). Companies need to make decisions based on the data that they understand and has qualities of data quality dimensions – Timely, Consistent, Completeness, Integrity, Accuracy, Conformity. To be successful in business, you need to make decisions fast and based on the right information. (Thatipamula 2013)

Preliminary Literature Review

Master data form the basis for business processes. Master data represent a company’s essential basic data which remain unchanged over a specific period of time. Which include customer, material, employee and supplier data. Inconsistent master data cause process errors resulting in higher costs. With proper governance, the master data can be regarded as a unified set of data that all applications can rely on for consistent, high quality information (Hamilton G).

Theoretical Framework

This framework will allow master data to flow from the Master Data System to the applications, making sure that all applications use the same data. It ensures that the data used is always created the same way and is unique. Which will then allow all linked applications to use the centralized master data.

By: C LOSER, Dr. C LEGNER, D GIZANIS showing Central Master Data System

Research Questions

Asking the right questions will enable the organization to better the way they make decision and the processes involved in their every day to day transactions (Cleven & Wortmann). This research will use a qualitative research to answer the following research questions:

How to maintain high data quality?

The impacts in business for not having high quality data
What causes data redundancy in the organization

Maintaining high data quality

Achieving better insight into the performance of processes involved in the everyday running of the business, the customers they have and product profitability and market share is one of the goals. These reporting understandings are the base for key decision making, however, the quality of the reporting is directly impacted by the quality of the data. Data that is not of high quality leads to under-informed decisions. Also, the return on costly investments in business intelligence is partly diminished if the source data is corrupt (C Loser, Dr. C Legner and D Gizanis 2004)

A major factor of any company’s day-to-day business is the data that is used in business processes and is available to the operational staff. If it happens that this data is not available, out of date or incorrect, the business may suffer delays or financial losses. “The implications of poor quality data carry negative effects to business users through: less customer satisfaction, increased running costs, inefficient decision-making processes, lower performance and lowered employee job satisfaction“(Haug A, Zachariassen F, van Liempd D, 2011)

Data in an organization needs to be controlled and managed. Without having specific rules in place or enforcing data governance, this will cause data to be redundant in different places/systems across the organization. Data governance specifies the framework for decision rights and accountabilities to encourage desirable behavior in the use of data (Otto B, 2009)

When wrong data is identified in the system, some analysis would have to be performed to find out why it happened and how it can be prevented from happening again. The process of correcting the issues will take time and organizational resources.

Benefits of Master Data Management

The key benefit of Master Data Management is to integrate similar data management processes, consolidate the critical information that is scattered across the organization, improve the integrity of data which makes sure that the data available is complete and accurate and business will be able to make more effective business decisions from the data with integrity. (Al-Zhrani, 2010)

Challenges of Master Data Management

Master Data Management challenges consists of managing the data as there is continuously increasing amount of data which will come with unclear processes of how to collect and maintain that data. Because master data is often used by multiple applications and processes, an error in master data can have a huge effect on the business processes. (Gustafsson, Franke, Johnson, Lilliesköld)

Hypothesis

Having implemented MDM effectively, the following can be made from the evidence gathered.

Hypothesis 1(H1): Understanding the company’s customers will lead to increase in market share

Hypothesis 2(H1): Data inconsistencies are caused by the distribution of data ownership across different business and function areas and across IT systems.

Hypothesis 3(H0): Companies that explicitly create a master data stewardship program are significantly more successful in terms of data governance

Research Methodology

Research design

For this research I will be using interpretive research design, as its main idea is to assist in understanding, recognizing and restructuring the subject meanings that already exist in the social world so we can use that understanding as steps into theorizing (Goldkuhl, 2012)

Research approach and strategy

Qualitative approach will be used for this research as it is suitable for discovering and gaining insight about a problem (Scheidl H, 2011)

Data collection

The data collection technique used for this research was one-on-one interview using a semi structured questionnaire. This technique is appropriate to this research as it allows elicitation of information that is not yet uncovered. Time kept for each interview is 45 min for each respondent. (Scheidl H, 2011)

Sampling and population

For this research I will use everyday decision makers so to get an understanding of how data influence the decisions that they make.

Data analysis

After having the interviews, I will then summaries the whole interview by making notes of the key points that were given by the respondents. This will assist in making sure that I familiarizes myself with my research.

Knowledge gap

In recent studies there have been increased focus on the importance of Master Data Management. This research contributes to the improvement of the scientific body of knowledge since it explores a part where only little previous study are available.

Conclusion

Poor data quality means there will be difficulties where decision makers have to build trust in the company data. Therefore, addressing and integrating MDM at the start should be part of an operational excellence initiative, in order to solve part of the process inefficiencies.

References

Milanov G & Njengus A, (2012) Using scrum in master data management development projects. Journal of Emerging Trends in Computing and Information Sciences, VOL. 3, NO.11 Nov, 2012 Available from http://www.cisjournal.org [Accessed: 15 April 2015]

Cleven A, Wortmann F (2010) Uncovering four strategies to approach master data management, Proceedings of the 43rd Hawaii International Conference on System Sciences – 2010 Available at www.computer.org/csdl/proceedings/hicss/2010/3869/00/09-01-05.pdf [Accessed: 16 May 2015]

Subotic D, Jovanovic V and Poscic P (2014), Data warehouse and master data management evolution –a meta-data-vault approach, Issues in Information Systems, Volume 15, Issue II, pp. 14-23, 2014

Cervo D, Allen M (2011) Master data management in practice: Achieving true customer MDM, John Wiley & Sons Inc., New Jersey, ISBN 978-0-470-91055-9

Al-Zhrani S, (2010) Management Information Systems Role in Decision-Making during Crises: Case Study, Journal of Computer Science 6 (11): 1247-1251, 2010 [Accessed: 11 March 2015]

Rahm E, Hai Do H Data Cleaning: Problems and Current Approaches, University of Leipzig, Germany Available at http://dbs.uni-leipzig.de [Accessed: 16 May 2015]

Gustafsson P, Franke U, Johnson P, Lilliesköld J, Identifying IT impacts on organizational structure and business value, Proceedings of BUSITAL’08, Available at people.dsv.su.se/~pajo/busital2008/paper4.pdf [Accessed: 23 May 2015]

Duff A 2015 Master data management roles – their part in data quality implementation. 10th International Conference on Information Quality, 2005 [Accessed: 25 March 2015]

Reichert A, Otto B and Österle H, 2013 A Reference Process Model for Master Data Management. 11th International Conference on Wirtschaftsinformatik [Accessed: 2 April 2015]

Jonker R, Kooistra F, Cepariu D, van Etten J and Swartjes S, Effective master data management Available at www.compact.nl/pdf/C-2011-0-Jonker.pdf [Accessed: 28 May 2015]

Scheidl H (2011), Master data management maturity and technology assessment, Master’s Thesis, Turku school of Economics

Kawulich B. (2005) Participant observation as a data collection method Volume 6, No. 2, Art. 43 – May 2005

Otto B, Schmidt A, ENTERPRISE MASTER DATA ARCHITECTURE: DESIGN DECISIONS AND OPTIONS Available at http://www.researchgate.net/profile/Boris_Otto/publication/50222231_Enterprise_Master_Data_Architecture_Design_Decisions_and_Options/links/0a85e53205fc23566a000000.pdf [Accessed: 25 March 2015]

Weber K, Otto B and ¨Osterle H 2009 One size does not fit all—a contingency approach to data governance. ACM J. Data Inform. Quality 1, 1, Article 4 (June 2009),

Haug A, Zachariassen F and van Liempd D 2011, the costs of poor data quality, Journal of Industrial Engineering and Management. Available from doi:10.3926/jiem.2011.v4n2.p168-193 [Accessed: 18 May 2015]

Kamel A, Lakhder D and Ammar Z 2012, The organizational impacts of information systems:

Analysis and proposal of a methodological framework, British Journal of Arts and Social Sciences, ISSN: 2046-9578, Vol.9 No.II (2012), Available from http://www.bjournal.co.uk/BJASS.aspx [Accessed: 18 May 2015]

Liang T, You J 2009, and Resource-based View in Information Systems Research: A Meta-Analysis, Pacific Asia Conference on Information Systems Available from http://aisel.aisnet.org/pacis2009/72 [Accessed: 18 May 2015]

Nowduri S, Management information systems and business decision making: review, analysis, and recommendations, Journal of Management and Marketing Research Available at www.aabri.com/manuscripts/10736.pdf [Accessed: 16 May 2015]

Mishra M, Das T 2011, A Study on challenges and opportunities in master data management. International Journal of Database Management Systems ( IJDMS ), Vol.3, No.2, May 2011

C Loser, Dr. C Legner, D Gizanis 2004 Master Data Management for Collaborative Service Processes Available http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.454.7987&rep=rep1&type=pdf [Accessed: 03 June 2015]

Goldkuhl G 2012, Pragmatism vs interpretivism in qualitative information systems research, European Journal of Information Systems, (21), 2, 135-146.Available at http://liu.diva-portal.org/smash/get/diva2:515141/FULLTEXT01.pdf [Accessed: 04 June 2015]

Order Now