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What is Data Quality

Handbook of Research on Big Data Storage and Visualization Techniques
Data quality refers to the fitness of data with respect to a specific purpose of usage. Data quality is critical to confidence in decision making. As data are more unstructured and collected from a wider array of sources, the quality of data tends to decline (Lee, 2017 AU148: The in-text citation "Lee, 2017" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Published in Chapter:
Big Data Analytics and Visualization of Performance of Stock Exchange Companies Based on Balanced Scorecard Indicators
Iman Raeesi Vanani (Allameh Tabataba'i University, Iran) and Maziar Shiraj Kheiri (Allameh Tabataba'i University, Iran)
DOI: 10.4018/978-1-5225-3142-5.ch029
Abstract
One of the major concerns of managers at stock exchange companies is the maximum and efficient use of limited resources to meet the unlimited users' demands and in particular, investors and company owners. Achieving this goal gets more complex everyday due to the changing environment and multidimensional economic pressures. It is necessary that managers know the process of effective data oriented measurement in every single aspect of a successful business. One of the most accredited and useful methods for evaluating performance is the Balanced Scorecard (BSC). In this chapter, researchers have focused on providing a model that evaluates the performance of companies based on a combination of BSC indicators and big data analytics and algorithms. The chapter's purpose is to indicate which analytics algorithms are most appropriate for each BSC indicator based on a deep review of broad literature as a measurement guideline for future researchers and practitioners.
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More Results
A Data Quality Model for Web Portals
Data has quality when it has “fitness for use,” that is, when it meets user requirements.
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Investigating the Pixel Quality Influence on Forecasting Vegetation Change Dynamics: Application Case of Tunisian Olive Sites
The difference between the observed variable and the reality. The lower is the difference, the better is the quality.
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Strategic Approach to Business Intelligence and Its Impacts on Organizational Performance
Data quality is an important feature that determines how reliable data is to make decisions. Expresses the value of qualitative or quantitative variables.
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Building Information Modeling for Energy Retrofitting
The totality of features and characteristics of a data set that bear on its ability to satisfy the needs that result from the intended use of the data
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Data Quality Assessment
A set of data properties (features, parameters, etc.) describing their ability to satisfy user’s expectations or requirements concerning data using for information acquiring in a given area of interest, learning, decision making, etc.
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A Multidisciplinary View of Data Quality
A multifaceted concept in information systems research that focuses on the fitness for use of data by consumers. Data quality can be viewed in four categories: intrinsic (accuracy, objectivity, believability, and reputation), contextual (relevancy, timeliness, and appropriate amount of data), representational (format of the data), and accessibility (ease of access).
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Improving Data Quality in Intelligent eCRM Applications
The level of quality of data from E-CRM application (this is our subject).
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Data Mining in Public Administration
This refers to the accuracy and completeness of data.
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Quality and Effectiveness of ERP Software: Data Mining Perspective
Refers to the feature that data can be relied upon for accurate decision-making process, planning, and projections.
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Creating a Data Lakehouse for a South African Government-Sector Learning Control Enforcing Quality Control for Incremental Extract-Load-Transform Pipe
The degree to which data are accurate, complete, timely, and consistent, providing reliable and meaningful information for decision making.
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The Quality Attribution in Data, Information and Knowledge
The extent to which the information enacted from the available data fits to the information needed in a given context and situation.
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Adopting Robotic Process Automation (RPA) in the Construction Industry
It measures how “fit-for-purpose” the information is, encompassing its accuracy, completeness, and relevance for its intended use.
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