PurposeThis 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.Design/methodology/approachThis 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.FindingsOne 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.Research limitations/implicationsStudied 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.Practical implicationsThis 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.Originality/valueThe 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.

Purpose-The purpose of this study is to provide tangible examples of product data management practices in large high tech companies, and to highlight current challenges. Design/methodology/approach-This research is a qualitative interview study. First, a product data management (PDM) system frame was defined to aid analyses. Secondly, an interview study was carried out in four companies to clarify the practical realisation of PDM, and the current challenges. The interviewees are experts in the field of PDM, currently holding significant related posts in their companies. Findings-Overall target of PDM activities are seen similar in all companies, however, there are some diversity in the realisation of these practices. PDM related challenges identified in this study are various, strongly influenced by company background and current organisational state. Research limitations/implications-This study includes interviews in four companies with different backgrounds, and a workshop, providing a good view on topical issues in the field of PDM. The obtained results could vary to some degree, should the sample size be larger, or especially should the products of the studied companies be less complex. Practical implications-This article provides managers and PDM system developers' better understanding over the issues that are affecting PDM solution development and on major system requirements, together with relevant insight to current challenges. Originality/value-The existing literature is relatively scarce in describing the practicalities of PDM. The obtained results highlight the significance of company background influencing the selection of PDM solutions.

Data quality has significance to companies, but is an issue that can be challenging to approach and operationalise. This study focuses on data quality from the perspective of operationalisation by analysing the practices of a company that is a world leader in its business. A model is proposed for managing data quality to enable evaluation and operationalisation. The results indicate that data quality is best ensured when organisation specific aspects are taken into account. The model acknowledges the needs of different data domains, particularly those that have master data characteristics. The proposed model can provide a starting point for operationalising data quality assessment and improvement. The consequent appreciation of data quality improves data maintenance processes, IT solutions, data quality and relevant expertise, all of which form the basis for handling the origins of products.

This article studies practical challenges experienced by ICT (Information and Communication Technology) companies when managing product configuration under the circumstances of various customer requirements, different product portfolios, and extensive product complexity. The analysis from interview results concentrates on the prioritised issues and how to ensure effective product configuration from practitioners' perspective. The results of this study indicate that typical challenges for product configuration formalisation include fuzzy product offering, lack of configuration strategy, mechanisms, and general product structure. This research highlights the need for industrial managers to adapt a top-down approach starting from business and strategy, instead of focusing on the challenges of single products when formalising product configuration. Companies need systematic configuration logic over their entire product portfolio and not to focus only single product variant options. Consequently, they need to define a generic product structure to support product configurations that covers all product types such as hardware, software and services. This study also highlights the need for better formalization of service products since they have become an integral part of ICT products. These findings are derived from actual business circumstances and their current difficulties.

Purpose The purpose of this paper is twofold: first, to understand data management challenges in e-maintenance systems from a holistically viewpoint through summarizing the earlier scattered research in the field, and second, to present a conceptual approach for addressing these challenges in practice. Design/methodology/approach The study is realized as a combination of a literature review and by the means of analyzing the practices on an industry leader in manufacturing and maintenance services. Findings This research provides a general understanding over data management challenges in e-maintenance and summarizes their associated proposed solutions. In addition, this paper lists and exemplifies different types and sources of data which can be collected in e-maintenance, across different organizational levels. Analyzing the data management practices of an e-maintenance industry leader provides a conceptual approach to address identified challenges in practice. Research limitations/implications Since this paper is based on studying the practices of a single company, it might be limited to generalize the results. Future research topics can focus on each of mentioned data management challenges and also validate the applicability of presented model in other companies and industries. Practical implications Understanding the e-maintenance-related challenges helps maintenance managers and other involved stakeholders in e-maintenance systems to better solve the challenges. Originality/value The so-far literature on e-maintenance has been studied with narrow focus to data and data management in e-maintenance appears as one of the less studied topics in the literature. This research paper contributes to e-maintenance by highlighting the deficiencies of the discussion surrounding the perspectives of data management in e-maintenance by studying all common data management challenges and listing different types of data which need to be acquired in e-maintenance systems.