Imagine a group of prehistoric hunters armed with stone-tipped spears. Their primitive weapons made hunting large animals, such as mammoths, dangerous work. Over time, however, a new breed of hunters developed. They would stretch the skin of a previously killed mammoth on the wall and throw their spears, while observing which spear, thrown from which angle and distance, penetrated the skin the best. The data gathered helped them make better spears and develop better hunting strategies. Quality data is the key to any advancement, whether it’s from the Stone Age to the Bronze Age. Or from the Information Age to whatever Age comes next. The success of corporations and government institutions largely depends on the efficiency with which they can collect, organize, and utilize data about products, customers, competitors, and employees. Fortunately, improving your data quality doesn't have to be such a mammoth task. DATA QUALITY ASSESSMENT is a must read for anyone who needs to understand, correct, or prevent data quality issues in their organization. Skipping theory and focusing purely on what is practical and what works, this text contains a proven approach to identifying, warehousing, and analyzing data errors – the first step in any data quality program. Master techniques in:<ul>
<li>Data profiling and gathering metadata </li> <li>Identifying, designing, and implementing data quality rules </li> <li>Organizing rule and error catalogues </li> <li>Ensuring accuracy and completeness of the data quality assessment </li> <li>Constructing the dimensional data quality scorecard </li> <li>Executing a recurrent data quality assessment</li>
</ul>

Data Quality Assessment

Business & Money

Maydanchik, Arkady

Management & Leadership

Technics Publications, LLC

Learn about the High-Level Data Model and master the techniques for building one, including a comprehensive ten-step approach and hands-on exercises to help you practice topics on your own. In this book, we review data modeling basics and explain why the core concepts stored in a high-level data model can have significant business impact on an organization. We explain the technical notation used for a data model and walk through some simple examples of building a high-level data model. We also describe how data models relate to other key initiatives you may have heard of or may be implementing in your organization. This book contains best practices for implementing a high-level data model, along with some easy-to-use templates and guidelines for a step-by-step approach.

Data Modeling for the Business: A Handbook for Aligning the Business with IT using High-Level Data Models (Take It with You Guides)

Computers & Technology

Burbank, Donna

Hoberman, Steve

Bradley, Chris

Databases & Big Data

Written by over 120 data management practitioners, the DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK) is the most impressive compilation of data management principals and best practices, ever assembled. It provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure. The equivalent of the PMBOK or the BABOK, the DAMA-DMBOK provides information on: 
<ul>
<li>Data Governance </li> <li>Data Architecture Management </li> <li>Data Development</li> <li>Database Operations Management</li> <li>Data Security Management </li> <li>Reference &amp; Master Data Management</li> <li>Data Warehousing &amp; Business Intelligence Management</li> <li>Document &amp; Content Management </li> <li>Meta Data Management </li> <li>Data Quality Management</li> <li>Professional Development</li> </ul> As an authoritative introduction to data management, the goals of the DAMA-DMBOK Guide are: <ul>
<li>To build consensus for a generally applicable view of data management functions. </li> <li>To provide standard definitions for commonly used data management functions, deliverables, roles, and other terminology. </li> <li>To document guiding principles for data management. </li> <li>To present a vendor-neutral overview to commonly accepted good practices, widely adopted methods and techniques, and significant alternative approaches. </li> <li>To clarify the scope and boundaries of data management. </li> <li>To act as a reference which guides readers to additional resources for further understanding. </li> </ul> The Editors are Mark Mosley, Editor - Development, Michael Brackett, Editor - Production, Susan Earley, Assistant Editor, and Deborah Henderson, Project Sponsor. The Foreword is by John Zachman (DAMA I Lifetime Achievement Award recipient), the Preface is by John Schley (DAMA International President) and Deborah Henderson (DAMA Foundation President, DAMA International VP Education and Research), and the Afterword is by Michael Brackett (DAMA International Lifetime Achievement Award recipient). From the Foreword by John Zachman:
 The book is an exhaustive compilation of every possible subject and issue that warrants consideration in initiating and operating a Data Management responsibility in a modern Enterprise. It is impressive in its comprehensiveness. It not only identifies the goals and objectives of every Data Management issue and responsibility but it also suggests the natural organizational participants and end results that should be expected. 
 The publication began as a non-trivial, sorely needed compilation of articles and substantive facts about the little understood subject of data management orchestrated by some folks from the DAMA Chicago Chapter. It was unique at the time as there was little substantive reference material on the subject. It has grown to become this pragmatic practitioners handbook that deserves a place on every Data Management professionals bookshelf. There is a wealth of information for the novice data beginner, but it is also invaluable to the old timer as a check-list and validation of their understanding and responsibilities to ensure that nothing falls through the cracks! It is impressive in it breadth and completeness. 
 The DAMA-DMBOK Guide deserves a place on every Data Management professionals bookshelf and for the General Manager, it will serve as a guide for setting expectations and assigning responsibilities for managing and practicing what has become the very most critical resource owned by an Enterprise as it (the Enterprise) progresses into the Information Age: DATA!

The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK)

DAMA International

Master how to use the Julia language to solve business critical data science challenges. After covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function. Specialized script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover: 
<ol>
<li>An overview of the data science pipeline along with an example illustrating the key points, implemented in Julia </li>
<li>Options for Julia IDEs </li>
<li>Programming structures and functions </li>
<li>Engineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data preprocessing </li>
<li>Data visualization and some simple yet powerful statistics for data exploration purposes </li>
<li>Dimensionality reduction and feature evaluation </li>
<li>Machine learning methods, ranging from unsupervised (different types of clustering) to supervised ones (decision trees, random forests, basic neural networks, regression trees, and Extreme Learning Machines) </li>
<li>Graph analysis including pinpointing the connections among the various entities and how they can be mined for useful insights. </li>
</ol>Each chapter concludes with a series of questions and exercises to reinforce what you learned. The last chapter of the book will guide you in creating a data science application from scratch using Julia.

Julia for Data Science

Voulgaris PhD, Zacharias

Computer Science

The Path to AGI: Artificial General Intelligence: Past, Present, and Future

Thompson, John K.

Introduction to Database Systems Modeling and Administration

Reneau Ph.D., James M.

Programming Languages

Written by over 120 data management practitioners, the DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK) is the most impressive compilation of data management principals and best practices, ever assembled. It provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure. 
 This print edition is also available in electronic PDF format on a CD (see ISBN 9780977140084). 
The equivalent of the PMBOK or the BABOK, the DAMA-DMBOK provides information on: 
<ul>
<li>Data Governance </li> <li>Data Architecture Management </li> <li>Data Development</li> <li>Database Operations Management</li> <li>Data Security Management </li> <li>Reference &amp; Master Data Management</li> <li>Data Warehousing &amp; Business Intelligence Management</li> <li>Document &amp; Content Management </li> <li>Meta Data Management </li> <li>Data Quality Management</li> <li>Professional Development</li> </ul> As an authoritative introduction to data management, the goals of the DAMA-DMBOK Guide are: <ul>
<li>To build consensus for a generally applicable view of data management functions. </li> <li>To provide standard definitions for commonly used data management functions, deliverables, roles, and other terminology. </li> <li>To document guiding principles for data management. </li> <li>To present a vendor-neutral overview to commonly accepted good practices, widely adopted methods and techniques, and significant alternative approaches. </li> <li>To clarify the scope and boundaries of data management. </li> <li>To act as a reference which guides readers to additional resources for further understanding. </li> </ul>

The DAMA Guide to the Data Management Body of Knowledge - Print Edition

Learn essential techniques from data warehouse legend Bill Inmon on how to build the reporting environment your business needs now!Answers for many valuable business questions hide in text. How well can your existing reporting environment extract the necessary text from email, spreadsheets, and documents, and put it in a useful format for analytics and reporting? Transforming the traditional data warehouse into an efficient unstructured data warehouse requires additional skills from the analyst, architect, designer, and developer. This book will prepare you to successfully implement an unstructured data warehouse and, through clear explanations, examples, and case studies, you will learn new techniques and tips to successfully obtain and analyze text.
Master these ten objectives:
<ul>
<li>Build an unstructured data warehouse using the 11-step approach</li>
<li>Integrate text and describe it in terms of homogeneity, relevance, medium, volume, and structure</li>
<li>Overcome challenges including blather, the Tower of Babel, and lack of natural relationships</li>
<li>Avoid the Data Junkyard and combat the Spider's Web</li>
<li>Reuse techniques perfected in the traditional data warehouse and Data Warehouse 2.0,including iterative development</li>
<li>Apply essential techniques for textual Extract, Transform, and Load (ETL) such as phrase recognition, stop word filtering, and synonym replacement</li>
<li>Design the Document Inventory system and link unstructured text to structured data</li>
<li>Leverage indexes for efficient text analysis and taxonomies for useful external categorization</li>
<li>Manage large volumes of data using advanced techniques such as backward pointers</li>
<li>Evaluate technology choices suitable for unstructured data processing, such as data warehouse appliances</li>
</ul>

Building the Unstructured Data Warehouse: Architecture, Analysis, and Design

Inmon, W.H.

Krishnan, Krish

This book teaches you how to capture and communicate both the abstract and concrete building blocks of your organization’s data, in order to provide a coherent and comprehensive foundation for systems development. 
“This book presents the most comprehensive treatment of high-level abstractions I've seen. Any event, business, and/or systems analyst should have this book available, both as a learning text and as an indispensible reference book. The knowledge packed away in this book takes decades to acquire and gestate. We are all fortunate to have it in a single volume.” James Odell Co-chair, OMG - Analysis and Design "UML and SoaML" Task Force
"David addresses a key, difficult, challenge for data modelling (and ontology) in this book - extracting the common pattern that underlies and unifies the variety of real data models that people use. And, what is almost as important to many readers, he does this in a clear and understandable way." Chris Partridge Chief Ontologist, The BORO Centre
"A great data model, one that lays the essence of a business bare, is a thing of beauty. It simplifies process, eases communication, and brings order to chaos. A great data model serves for a lifetime. Powerful stuff, this." Tom Redman, President Navesink Consulting Group, LLC
"Finally, choosing a level of abstraction for a data model is addressed methodically. David should be applauded for grasping this thorny issue and producing a wonderfully readable book. Every data modeler should have one". Cliff Longman, President Adaptable Data
In 1995, David Hay published Data Model Patterns: Conventions of Thought - the groundbreaking book on how to use standard data models to describe the standard business situations. Enterprise Model Patterns: Describing the World builds on the concepts presented there, adds 15 years of practical experience, and presents a more comprehensive view.
This model addresses your enterprise via four levels of abstraction:
Level 0: An abstract template that underlies the Level 1 model, plus two meta models: Information Resources and Accounting. Each of these itself represents the rest of the enterprise, so to model it is to “model a model”, so to speak.
Level 1: An enterprise model that is generic enough to apply to any company or government agency, but concrete enough to be readily understood by all. It describes people and organizations, geographic locations, (physical) assets, activities, and time. 
Level 2: A more detailed model describing specific functional areas: facilities and other addresses, human resources, communications and marketing, contracts, manufacturing, and the laboratory.
Level 3: Examples of the details that can be added to a model to address what is truly unique in a particular industry. Here you see how to address the unique bits in areas as diverse as criminal justice, microbiology, banking, oil field production, and highway maintenance.

Enterprise Model Patterns: Describing the World (UML Version)

Hay, David C.

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