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.

The growing importance of product data management and master data necessitate companies to have practices for deriving product master data from their corporate strategy. Business drivers need to be understood from the perspective of corporate strategy to capture product master data in relevant systems in a straightforward manner. Ideally master data is created only once and used through the life-cycle of the product. This study clarifies the foundations for determining one product data from corporate strategy. Data definitions are analysed to understand its linkages to business drivers, whereas main business processes are used to support categorisation. The practices of three companies are analysed to understand how business drivers for new products impact product data requirements. The results highlight the importance of business drivers in defining one product data based on the product master data, business-process related product data and IT systems over the product life-cycle.

Productivity in the construction industry (both houses and infrastructure) has not been improving as expected, while other industries have been able to improve their productivity significantly. The appropriate use of building information modelling (BIM) technologies brings several benefits and advantages to construction projects. The main challenges of project efficiency emerge in the form of numerous requests for information during the construction project, which are considered to be waste in the processes. This highlights the need for a practical process model to plan the information flow for BIM-based projects. The main aim of this study is to propose a model to plan the flow of project information among primary stakeholders especially in infrastructure projects. Our main findings are firstly, the foundation for data management starts from defining unified one data for the product and the for the process. Unified data means one single repository of data – all stakeholders use the same unified data. It is also essential that data responsibilities and ownership are defined. Secondly, we found that the biggest challenges are that the data needs are not planned beforehand, resistance to change, difficulty receiving existing data and data must be modified before use. As a whole, it seems sometimes that the technology on data transfer is more important that what has been transferred and why. Finally our construction, the life cycle model for data flow originates from one data to all stakeholders, single data repository must be updated along the life- cycle of the object covering also the operations and maintenance, where the data has to be updated through the whole life-cycle. This new approach is intended to enable the early involvement of maintenance stakeholders in designing product data for a project lifecycle perspective. The model helps to change the current information flow and gain the benefits that a BIM-based process can offer. This study is based on case studies and is qualitative in nature and naturally needs more validation.

He has 25 years career in leading global manufacturers at several different positions both in business and in IT organisations. During his career, he has participated in new product programs, global business change programs in program management positions as well as leading departments. Currently his research relates to data driven organisations and business models. Dr Janne Harkonen received his Bachelor's degree (1st Class Honours

The growing importance of product data management and master data necessitate companies to have practices for deriving product master data from their corporate strategy. Business drivers need to be understood from the perspective of corporate strategy to capture product master data in relevant systems in a straightforward manner. Ideally master data is created only once and used through the life-cycle of the product. This study clarifies the foundations for determining one product data from corporate strategy. Data definitions are analysed to understand its linkages to business drivers, whereas main business processes are used to support categorisation. The practices of three companies are analysed to understand how business drivers for new products impact product data requirements. The results highlight the importance of business drivers in defining one product data based on the product master data, business-process related product data and IT systems over the product life-cycle.