Unlock the Secrets to Effective Data Quality Management: A Practical Guide

Data is the lifeblood of any organization in this digital age. With the exponential growth of data, maintaining high-quality data has become crucial for businesses to derive accurate insights and make informed decisions. In this practical guide, we will explore the key principles and strategies to effectively manage data quality.

Chapter 1: Understanding Data Quality

Before diving into the management aspect, it's important to grasp the concept of data quality. Data quality refers to the accuracy, completeness, consistency, and relevancy of data. Learn how to identify common data quality issues and their impact on business operations.

Managing Data Quality: A practical guide

by Tim King (Kindle Edition)

4.3 out of 5

Language	:	English
File size	:	11239 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	160 pages

FREE

Chapter 2: Importance of Data Quality Management

Discover why data quality management is vital in today's competitive landscape. Discover how poor data quality can lead to increased costs, missed opportunities, and damaged reputation. Learn how effective data quality management can enhance decision-making processes and drive business growth.

Chapter 3: Building a Data Quality Framework

A solid data quality framework is essential for managing data effectively. This chapter provides practical guidance on establishing a framework that includes data governance, data standards, data profiling, and data cleansing.

Chapter 4: Data Profiling Techniques

Data profiling helps uncover hidden patterns, errors, and inconsistencies within datasets. Explore various data profiling techniques such as column profiling, value frequency analysis, and data lineage analysis. Learn how to leverage these techniques to identify data quality issues and prioritize remediation efforts.

Chapter 5: Data Cleansing Strategies

Data cleansing is a critical step in maintaining data quality. Discover the best practices and techniques for data cleansing, including deduplication, standardization, and validation. Understand how to develop automated data cleansing workflows to ensure ongoing data quality improvement.

Chapter 6: Data Quality Measurement and Reporting

Monitoring and measuring data quality is key to the success of your data quality management program. Learn how to define meaningful data quality metrics and establish a comprehensive reporting system. Gain insights into data quality dashboards and scorecards to effectively communicate data quality status within your organization.

Chapter 7: Data Quality Maintenance and Continuous Improvement

Data quality management is an ongoing process that requires continuous effort. Discover strategies for maintaining high-quality data over time and ensuring long-term success. Understand the role of data stewards and the importance of stakeholder engagement in the data quality improvement journey.

Managing data quality is challenging but necessary for organizations to remain competitive in today's data-driven world. By implementing the principles and strategies outlined in this practical guide, you can unlock the secrets to effective data quality management and harness the true value of your data.

Managing Data Quality: A practical guide

by Tim King (Kindle Edition)

4.3 out of 5

Language	:	English
File size	:	11239 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	160 pages

FREE

Data is an increasingly important business asset and enabler for organisational activities. With growth in data sets and data volumes, it's becoming ever harder to manage. Data quality - the fitness for purpose of data - is a key aspect of data management and failure to understand it increases organisational risk and decreases efficiency and profitability.
This book explains data quality management in practical terms, focusing on three key areas - the nature of data in enterprises, the purpose and scope of data quality management, and implementing a data quality management system, in line with ISO 8000-61.