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The purpose of the paper is to propose a reference model describing a holistic view of the master data lifecycle, including strategic, tactical and operational aspects. The Master Data Lifecycle Management (MDLM) map provides a structured approach to analyze the master data lifecycle.

Embedded in a design oriented research process, the paper applies the Component Business Model (CBM) method and suggests a reference model which identifies the business components required to manage the master data lifecycle. CBM is a patented IBM method to analyze the key components of a business domain. The paper uses a participative case study to evaluate the suggested model.

Based on a participative case study, the paper shows how the reference model makes it possible to analyze the master data lifecycle on a strategic, a tactical and an operational level, and how it helps identify areas of improvement.

The paper presents design work and a participative case study. The reference model is grounded in existing literature and represents a comprehensive framework forming the foundation for future analysis of the master data lifecycle. Furthermore, the model represents an abstraction of an organization's master data lifecycle. Hence, it forms a “theory for designing”. More research is needed in order to more thoroughly evaluate the presented model in a variety of real‐life settings.

The paper shows how the reference model enables practitioners to analyze the master data lifecycle and how it helps identify areas of improvement.

The paper reports on an attempt to establish a holistic view of the master data lifecycle, including strategic, tactical and operational aspects, in order to provide more comprehensive support for its analysis and improvement.

This paper aims to provide a framework of the multidimensional concept of one master data. Preconditions required for successful one master data implementation and usage in large high‐tech companies are presented and related current challenges companies have today are identified.

This paper is qualitative in nature. First, literature was studied to find out the elements of one master data. Second, an interview study was carried out in eight high‐tech companies and in three expert companies.

One master data management framework is the composition of data, processes and information systems. Accordingly, the key challenges related to the data are that the definitions of master data are unclear and overall data quality is poor. Challenges on processes related to managing master data are inadequately defined data ownership, incoherent data management practices and lack of continuous data quality practices. Integrations between applications are fundamental challenge to tackle when constructing an holistic one master data.

Studied companies are vanguards in the area of master data management (MDM), providing good views on topical issues in large companies. This study offers a general view of the topic but not describes special company situations as companies need to adapt the presented concepts for their specific case. Significant implication for future research is that MDM can no more be classified and discussed as only an IT problem but it is a managerial challenge which requires structural changes on mindset how issues are handled.

This paper provides a better understanding over the issues which are impacting on the implementation of one master data. The preconditions of implementing and executing one master data are: an organization wide and defined data model; clear data ownership definitions; pro‐active data quality surveillance; data friendly company culture; the clear definitions of roles and responsibilities; organizational structure that supports data processes; clear data process definitions; support from the managerial level; and information systems that utilize the unified data model. The list of preconditions is wide and it also describes the incoherence of current understanding about MDM. This list helps business managers to understand the extent of the concept and to see that master data management is not only an IT issue.

The existing practical research on master data management is limited and, for example, the general challenges have not been reported earlier. This paper offers practical research on one master data. The obtained results illustrates the extent of the topic and the fact that business relevant data management is not only an IT (application) issue but requires understanding of the data, its utilization in organization and supporting practices such as data ownership.

While few would disagree that high data quality is a precondition for the efficiency of a company, this remains an area to which many companies do not give adequate attention. Thus, this paper aims to identify which are the most important barriers preventing companies from achieving high data quality. By improving awareness of barriers on which to concentrate, companies are put in a better position to achieve high quality data.

First, a literature review of data quality and data quality barriers is carried out. Based on this literature review, the paper identifies a set of overall barriers to ensuring high data quality. The significance of these barriers is investigated by a questionnaire study, which includes responses from 90 Danish companies. Because of the fundamental difference between master data and transaction data, the questionnaire is limited to focusing only on master data.

The results of the survey indicate that a lack of delegation of responsibilities for maintaining master data is the single aspect which has the largest impact on master data quality. Also, the survey shows that the vast majority of the companies believe that poor master data quality does have significant negative effects.

The contributions of this paper represent a step towards an improved understanding of how to increase the level of master data quality in companies. This knowledge may have a positive impact on the data quality in companies. However, since the study presented in this paper appears to be the first of its kind, the conclusions drawn need further investigation by other research studies in the future.

This paper identifies the main barriers for ensuring high master data quality and investigates which of these factors are the most important. By focusing on these barriers, companies will have better chances of increasing their data quality.

The study presented in this paper appears to be the first of its kind, and it represents an important step towards understanding better why companies find it difficult to achieve satisfactory data quality levels.

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.

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.

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.

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.

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.

The development of IT has enabled organizations to collect and store many times more data than they were able to just decades ago. This means that companies are now faced with managing huge amounts of data, which represents new challenges in ensuring high data quality. The purpose of this paper is to identify barriers to obtaining high master data quality.

This paper defines relevant master data quality barriers and investigates their mutual importance through organizing data quality barriers identified in literature into a framework for analysis of data quality. The importance of the different classes of data quality barriers is investigated by a large questionnaire study, including answers from 787 Danish manufacturing companies.

Based on a literature review, the paper identifies 12 master data quality barriers. The relevance and completeness of this classification is investigated by a large questionnaire study, which also clarifies the mutual importance of the defined barriers and the differences in importance in small, medium, and large companies.

The defined classification of data quality barriers provides a point of departure for future research by pointing to relevant areas for investigation of data quality problems. The limitations of the study are that it focuses only on manufacturing companies and master data (i.e. not transaction data).

The classification of data quality barriers can give companies increased awareness of why they experience data quality problems. In addition, the paper suggests giving primary focus to organizational issues rather than perceiving poor data quality as an IT problem.

Compared to extant classifications of data quality barriers, the contribution of this paper represents a more detailed and complete picture of what the barriers are in relation to data quality. Furthermore, the presented classification has been investigated by a large questionnaire study, for which reason it is founded on a more solid empirical basis than existing classifications.

Accounting education is re-inventing itself as technology impacts the practical aspects of accounting in the real world and education tries to keep up. Big Data and data analytics have begun to influence elements of accounting including audit, accounting preparation, forensic accounting and general accountancy consulting. The purpose of this paper is to qualitatively analyse the current skills provision in accounting Masters courses linked to data analytics compared to academic and professional expectations of the same.

The academic expectations and requirements of the profession, related to the impact of Big Data and data analytics on accounting education were reviewed and compared to the current provisions of this accounting education in the form of Masters programmes. The research uses an exploratory, qualitative approach with thematic analysis.

Four themes were identified of the skills required for the effective use of Big Data and data analytics. These were: questioning and scepticism; critical thinking skills; understanding and ability to analyse and communicating results. Questioning and scepticism, as well as understanding and ability to analyse, were frequently cited explicitly as elements for assessment in various forms of accounting education in the Masters courses. However, critical thinking and communication skills were less explicitly cited in these accounting education programmes.

The research reviewed and compared current academic literature and the requirements of the professional accounting bodies with Masters programmes in accounting and data analytics. The research identified key themes relevant to the accounting profession that should be explicitly developed and assessed within accounting education for Big Data and data analytics at both university and professional levels. Further analysis of the in-depth curricula, as opposed to the explicitly stated topic coverage, could add to this body of research.

This paper considers the potential combined role of professional qualification examinations and master’s degrees in skills provision for future practitioners in accounting and data analysis. This can be used to identify the areas in which accounting education can be further enhanced by focus or explicit mention of skills that are both developed and assessed within these programmes.

The paper considers the interaction between academic and professional practice in the areas of accounting education, highlighting skills and areas for development for students currently considering accounting education and data analytics.

While current literature focusses on integrating data analysis into existing accounting and finance curricula, this paper considers the role of professional qualification examinations with Masters degrees as skills provision for future practitioners in accounting and data analysis.

The purpose of this paper is to analyse current challenges and to articulate the preconditions for data-driven, fact-based product portfolio management (PPM) based on commercial and technical product structures, critical business processes, corporate business IT and company data assets. Here, data assets were classified from a PPM perspective in terms of (product/customer/supplier) master data, transaction data and Internet of Things data. The study also addresses the supporting role of corporate-level data governance.

The study combines a literature review and qualitative analysis of empirical data collected from eight international companies of varying size.

Companies’ current inability to analyse products effectively based on existing data is surprising. The present findings identify a number of preconditions for data-driven, fact-based PPM, including mutual understanding of company products (to establish a consistent commercial and technical product structure), product classification as strategic, supportive or non-strategic (to link commercial and technical product structures with product strategy) and a holistic, corporate-level data model for adjusting the company’s business IT (to support product portfolio visualisation).

The findings provide a logical and empirical basis for fact-based, product-level analysis of product profitability and analysis of the product portfolio over the product life cycle, supporting a data-driven approach to the optimisation of commercial and technical product structure, business IT systems and company product strategy. As a virtual representation of reality, the company data model facilitates product visualisation. The findings are of great practical value, as they demonstrate the significance of corporate-level data assets, data governance and business-critical data for managing a company’s products and portfolio.

The study contributes to the existing literature by specifying the preconditions for data-driven, fact-based PPM as a basis for product-level analysis and decision making, emphasising the role of company data assets and clarifying the links between business processes, information systems and data assets for PPM.

The purpose of this paper is to share the experience of the document discovery process, during the implementation of an asset management system for a rail company. This system will deliver comprehensive enterprise asset management information from a single source, with information provided to mobile devices, for use by field workers. This case study presents the challenges encountered in the search, retrieval and management of documentation for use on a daily basis for civil standard maintenance tasks.

Evidence gathered for this paper was a result of direct and participant observation over a period of 18 months from 2014 to 2016. As a member of the project team, certain privileges were accorded to the researcher who was placed in a unique position to act as the main research instrument, able to collect data on the systems used as well as the everyday practices on information capture and document production.

Document quality and standards can be overlooked or deemed as not crucial; the value, significance and importance of documentation are lost when no one takes ownership; the understanding and application of standards, quality management and governance can have a direct bearing on the effective management and control of documents and subsequent records produced.

Research is limited, as this is a single case study.

By highlighting the challenges faced and the resolutions used, this paper hopes to offer a level of practical guidance with the detection process for maintenance tasks for the civil assets discipline for a rail network.

This case study contributes to the understanding of quality management and the role it plays in document management and in turn the search and retrieval process. It provides evidence that documents must be systematically managed and controlled to limit risk both internally and externally.

A low power flip-flop circuit is designed for energy-efficient devices. Digital sequential circuits are in huge demand because every processor has most of the parts of digital circuit. The sequential circuits consist of a basic data storing element, a latch is used to store single bit data. The flip-flop takes a sufficient portion of the total chip area and overall power consumption as well. This study aims to the low power energy-efficient applications like laptops, mobile phones and palmtops.

This paper proposes a new type of flip-flop that consists of the only 16 transistors with a single-phase clock. The flip-flop has two blocks, master and slave latch. In this design, the authors have focused on only master latch, which includes a level restoring circuit. It is used to help the master latch in data retention process. The latch circuit has two inverters in back-to-back arrangement. The proposed flip-flop is implemented on 65 nm complementary metal oxide semiconductor technology using Cadence Virtuoso environment and compared with other reported flip-flops.

The proposed flip-flop architecture outperformed the peak percentage, i.e. 79.25% as compared to transmission gate flip-flop and a minimum of 20.02% compared to 18 T true single phase clocking (TSPC) improvement in terms of power. It also improved C to Q delay and power delay product. In addition, by reducing the number of transistors the total area of the proposed flip-flop is reduced by a minimum of 13.76% with respect to 18TSPC and existing flip-flop. For reliability checking the Monte Carlo simulation is performed for thousand samples and it is compared with the recently reported 18TSPC flip-flop.

This work is tested by using a test circuit with a load capacitor of 0.2 fF. The proposed work uses a new topology to work as master-slave. Power consumption of this technique is very less and it is best suitable for low power applications. This circuit is working properly up to 2 GHz frequency.