The Discipline of Data

Pulling aside the curtain of 'Big Data' buzz, this book introduces C-suite and other non-technical senior leaders to the essentials of obtaining and maintaining accurate, reliable data, especially for decision-making purposes.

Autorentext

Jerry Savin is Founder, President, and CEO of Cambridge Technology Consulting Group, Inc., which specializes in Automated Business Software, including assessment, selection, implementation, and troubleshooting, Data Architecture, IT Auditing and Compliance, and Judicial Expert Witness Testimony. Jerry has taught at UCLA Extension for 30+ years, the UCR Graduate School of Business for 10 years, and California State University Northridge for 9 years teaching course in Information Systems, IT Auditing and general IS classes.

Klappentext

Pulling aside the curtain of 'Big Data' buzz, this book introduces C-suite and other non-technical senior leaders to the essentials of obtaining and maintaining accurate, reliable data, especially for decision-making purposes. Bad data begets bad decisions, and an understanding of data fundamentals - how data is generated, organized, stored, evaluated, and maintained - has never been more important when solving problems such as the pandemic-related supply chain crisis. This book addresses the data-related challenges that businesses face, answering questions such as: What are the characteristics of high-quality data? How do you get from bad data to good data? What procedures and practices ensure high-quality data? How do you know whether your data supports the decisions you need to make? This clear and valuable resource will appeal to C-suite executives and top-line managers across industries, as well as business analysts at all career stages and data analytics students.

Inhalt

1 Preface. 2 Data - Introduction. 3 The Many Facets of Data. 3.1 Basic Concepts. 3.2 Basic Terms and Terminology. 4 Domain Specific Topics. 4.1 Data Governance. 4.2 Data Architecture. 4.3 Databases. 4.4 Master Data and Master Data Management. 4.5 Metadata and Metadata Management. 4.6 Data Quality. 4.7 Null Values. 4.8 Data Modeling and Design. 4.9 Data Integration and Interoperability. 4.10 Data Security. 4.11 Data at Rest and Data in Motion. 4.12 Data Wrangling and Data Storage. 5 Data: Past, Present and Future. 5.1 Data - The Past. 5.2 Data - The Present. 5.3 Data - The Future. 6 The New Reality. 7 Data - Use Cases.8 To Sum Up. 9 Data - Optimization. 10 Epilog