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