Asset Master Data Management: Ensuring Accuracy and Consistency in Industrial Operations

The problems of data quality are topicalnowadays, and the tendencies of growing amounts of data bringcertain challenges to data management strategies. Also,technical tools of today allow for data that is more than mostfirms can administrate and different business solutions canresult in increased data density. Master data management istruly experiencing globalisation at an exponential rate becauseits relevance or a link to the results or income does not have adirect connection to the operation of an organisation. In thecontemporary world that is characterised by stiff economycompetition, proper management of master data is crucial tomaking right strategic decisions and management of anorganisation for efficient functioning. This paper aims atstudying the topic of Master Data Management (MDM) takinginto account the enhancement of dependability, consistency andquality of numerous fields of activity. Focusing on a singlescheme of Master Data Management and the integration ofvarious data sources, it describes MDM frameworks andimplementation methods. The study reviews different types ofMDM solutions—operational, analytic, and enterprise—anddiscusses key phases and approaches for successful MDMimplementation. Furthermore, it investigates current researchtrends and gaps in MDM literature, highlighting the need foradaptive and scalable MDM architectures tailored to evolvingorganizational needs

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-09-12
DOI
https://doi.org/10.5281/zenodo.13771045
Primary Topic
Data Quality and Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Asset Master Data Management: Ensuring Accuracy and Consistency in Industrial Operations

Zenodo (CERN European Organization for Nuclear Research)
Data Quality and Management
article

Asset Master Data Management: Ensuring Accuracy and Consistency in Industrial Operations

article en

Abstract

The problems of data quality are topicalnowadays, and the tendencies of growing amounts of data bringcertain challenges to data management strategies. Also,technical tools of today allow for data that is more than mostfirms can administrate and different business solutions canresult in increased data density. Master data management istruly experiencing globalisation at an exponential rate becauseits relevance or a link to the results or income does not have adirect connection to the operation of an organisation. In thecontemporary world that is characterised by stiff economycompetition, proper management of master data is crucial tomaking right strategic decisions and management of anorganisation for efficient functioning. This paper aims atstudying the topic of Master Data Management (MDM) takinginto account the enhancement of dependability, consistency andquality of numerous fields of activity. Focusing on a singlescheme of Master Data Management and the integration ofvarious data sources, it describes MDM frameworks andimplementation methods. The study reviews different types ofMDM solutions—operational, analytic, and enterprise—anddiscusses key phases and approaches for successful MDMimplementation. Furthermore, it investigates current researchtrends and gaps in MDM literature, highlighting the need foradaptive and scalable MDM architectures tailored to evolvingorganizational needs

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 48%
Data Quality and Management
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