Asset Lifecycle Data Governance Framework

Haider, Abrar

doi:10.1007/978-3-319-06966-1_27

Cited by 6 publications

(6 citation statements)

References 2 publications

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“…Other papers cover the issues of data management in relation to asset lifecycle management (e.g. Ruitenburg et al, 2014;Haider, 2015) and the challenge of simultaneous use of building information modeling (BIM) in asset management (P€ arn et al, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Implementing asset data management in power companies

Gavrikova

Volkova

Yegor

2021

IJQRM

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PurposeThe purpose of this paper is to design a framework for asset data management in power companies. The authors consider asset data management from a strategic perspective, linking operational-level data with corporate strategy and taking into account the organizational context and stakeholder expectations.Design/methodology/approachThe authors conducted a multiple case study based on a literature review and three series of in-depth interviews with experts from three Russian electric power companies.FindingsThe main challenge in asset data management for electric power companies is the increasing amount and complexity of asset data, which is frequently incomplete or inaccurately collected, hard to translate to managerial language, focused primarily on the operational level. Such fragmented approach negatively affects strategic decision-making. The proposed framework introduces a holistic approach, provides context and accountability for decision-making and attributes data flows, roles and responsibilities to different management levels.Research limitations/implicationsThe limitations of our study lie in the exploratory nature of case study research and limited generalization of the observed cases. However, the authors used multiple sources of evidence to ensure validity and generalization of the results. This article is a first step toward further understanding of the issues of transformation in power companies and other asset intensive businesses.Originality/valueThe novelty of the framework lies in the scope, focus and detailed treatment of asset data management in electric power companies.

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Section: Literature Reviewmentioning

confidence: 99%

Implementing asset data management in power companies

Gavrikova

Volkova

Yegor

2021

IJQRM

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“…The survey found that processes and software for asset related data quality management were missing in a majority of organisations interviewed; in addition organisations did not have a strategy in place regarding data quality (Lin et al, 2007). Haider (2015) propose a framework for asset lifecycle management data governance, stating that organisations need policies to ensure data quality is inherent in their operation. Woodall et al (2015) define seven information quality dimensions for organisations to audit their operations by to establish an actual level of data quality and potentially identify areas for further improvement.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Decision Support for Maintenance: A New Role for Audit Trails

Turner

Emmanouilidis

Tomiyama

et al. 2020

Engineering Assets and Public Infrastructures in the Age of Digitalization

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The changing nature of manufacturing, in recent years, is evident in industries willingness to adopt network connected intelligent machines in their factory development plans. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by Internet of Things create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of comprehensive framework for its processing, analysis and use should be an important goal in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data and the provision of a comprehensive framework for its processing analysis and use. The concept of 'Human in the loop' is also reinforced with the use of audit trails, allowing streamlined access to decision making and providing the ability to mine decisions.

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“…Despite the 219 Product-level profitability strategic nature of data, data ownership and management within organisations remain unclear (Aiken and Billings, 2013). For efficiency and effectiveness, standardised and corporate-level data management practices are recommended (Silvola et al, 2019;Otto et al, 2007;Schaffer and Leyh, 2017;Gao et al, 2010;Haider, 2015), and in some cases, comprehensive data governance may be the only thing that differentiates a company from its competitors (Allen and Cervo, 2015). Faultless and trustworthy data shared quickly and automatically are a survival tool in the competitive context of time-to-market, profitability and labour costs (Sriti et al, 2015;Saaksvuori and Immonen, 2008;Terzi et al, 2010).…”

Section: Data Governancementioning

confidence: 99%

Product-level profitability

Hannila

Koskinen

Härkönen

et al. 2019

JEIM

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Purpose 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. Design/methodology/approach The study combines a literature review and qualitative analysis of empirical data collected from eight international companies of varying size. Findings 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). Practical implications 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. Originality/value 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.

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Asset Lifecycle Data Governance Framework

Cited by 6 publications

References 2 publications

Implementing asset data management in power companies

Implementing asset data management in power companies

Intelligent Decision Support for Maintenance: A New Role for Audit Trails

Product-level profitability

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