What question did this study set out to answer?

The aim is to identify challenges and opportunities in improving data quality within organizations, particularly concerning unstructured data.

February 21, 2026Open Access

Identification of data patterns using Data quality techniques

Key Points

The aim is to identify challenges and opportunities in improving data quality within organizations, particularly concerning unstructured data.
Conducted a systematic literature review of existing research articles.
Focused on themes such as data quality dimensions and management models.
Synthesized findings to address key research questions.
Identified gaps in current methodologies related to unstructured and big data.
Highlighted essential dimensions of data quality, including completeness and accuracy.
Discussed limitations of existing data quality assessment strategies.
Identified emerging research areas in data quality for the internet and big data.
Showed that current models primarily focus on structured data, limiting broader applications.

Abstract

The aim of this paper is to spotlight problems in classification of Address data and customer details in unstructured data files, quality analysis and to debate potential research chance to attain high data quality inside a corporation. The review adopted systematic literature review methodology supported analysis articles printed in journals and conference proceedings. we tend to developed a review strategy supported specific themes like current analysis space in knowledge quality, essential dimensions in knowledge quality, knowledge quality management model and methodologies and data quality assessment strategies. Supported the review strategy, we tend to choose relevant analysis articles, extract and synthesis the knowledge to answer our analysis queries. The review highlights the advancement of knowledge quality analysis to correspond its real world application and discuss the accessible gap for future analysis. Research gap like organizations management, data quality impact towards the organization and information connected technical solutions for data quality dominated the first years of knowledge quality analysis. However, since the web is currently going down because the new data supply the rising of latest analysis areas like knowledge quality assessment for internet and massive data is inevitable. This review conjointly identifies and discusses essential knowledge quality dimensions in organization like knowledge completeness, consistency, accuracy and timeliness. We tend to conjointly compare and highlight gaps in knowledge quality management model and methodologies. Existing model and methodologies capabilities are restricted to the structured knowledge kind and limit its ability to assess data quality in internet and massive data. Finally, we tend to uncover accessible strategies in knowledge quality assessment and highlight its limitation for future analysis. This review is very important to spotlight and analyze limitation of existing knowledge quality analysis associated with the recent wants in data quality like unstructured data kind and massive data. Keywords: Data Quality, Information Quality Management Model, Assessment strategies, Database.

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