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Data Quality: Perception Versus Reality Graham Rhine Sponsored by Capstan Ltd Data Quality: Perception Versus Reality TABLE OF CONTENTS Page Executive Summary 2 Introduction 3 Key Findings 4 Responsibility
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How to fill out data quality perception versus:

01
Start by gathering all relevant data points and information that needs to be compared.
02
Analyze each data point individually and determine its quality based on various factors such as accuracy, completeness, consistency, and timeliness.
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Compare the quality of each data point to other similar data points and create a perception versus chart or visual representation to showcase the differences.
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Who needs data quality perception versus:

01
Data analysts and data scientists who are responsible for evaluating and ensuring the quality of data.
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Business stakeholders who rely on accurate and reliable data to make informed decisions.
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Data governance teams and data management professionals who are tasked with maintaining data quality standards across an organization.
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Data quality perception versus is a measure of how accurate, complete, and consistent a set of data is.
All organizations that handle and manage data are required to file data quality perception versus.
Data quality perception versus can be filled out by assessing the quality of the data based on various criteria such as accuracy, completeness, and consistency.
The purpose of data quality perception versus is to ensure that the data being used for decision-making is reliable and of high quality.
Information such as data sources, data collection methods, data validation processes, and data quality metrics must be reported on data quality perception versus.
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