Books published by Technics Publications LLC

Are you struggling with the formal design of your organisation''s data resource? Do you find yourself forced into generic data architectures and universal data models? Do you find yourself warping the business to fit a purchased application? Do you find yourself pushed into developing physical databases without formal logical design? Do you find disparate data throughout the organisation? If the answer to any of these questions is Yes, then you need to read Data Resource Design to help guide you through a formal design process that produces a high quality data resource within a single common data architecture. Most public and private sector organisations do not consistently follow a formal data resource design process that begins with the organisation''s perception of the business world, proceeds through logical data design, through physical data design, and into implementation. Most organisations charge ahead with physical database implementation, physical package implementation, and other brute-force-physical approaches. The result is a data resource that becomes disparate and does not fully support the organisation in its business endeavours. This book describes how to formally design an organisation''s data resource to meet its current and future business information demand. It builds on "Data Resource Simplexity", which described how to stop the burgeoning data disparity, and on "Data Resource Integration", which described how to understand and resolve an organisation''s disparate data resource. It describes the concepts, principles, and techniques for building a high quality data resource based on an organisation''s perception of the business world in which they operate. Like "Data Resource Simplexity" and "Data Resource Integration", Michael Brackett draws on five decades of data management experience building and managing data resources, and resolving disparate data in both public and private sector organisations. He leverages theories, concepts, principles, and techniques from a wide variety of disciplines, such as human dynamics, mathematics, physics, chemistry, philosophy, and biology, and applies them to properly designing data as a critical resource of an organisation. He shows how to understand the business environment where an organisation operates and design a data resource that supports the organisation in that business environment.

Business data integration is a complex problem that must be solved when organizations change or enhance their internal structures. The goal of this book is to present a simple yet thorough resource that describes the challenges of business data integration and the solutions to these challenges such as schema integration, illustrated through an Operational Data Store (ODS) case study. This book contains three sections spanning ten chapters. Section I, Foundational Concepts, will provide you with the necessary basic concepts and discuss schema integration. Section II, Preparation and Design, introduces the case study and we will reverse engineer each of the data sources to create a set of data dictionary reports which will provide us with the meta data we need to apply the schema integration process. Section III, Physical Implementation, will present scripts to populate each of the source databases and spreadsheets and use reports to create Extract, Transform, and Load (ETL) specifications. The ten chapters within these three sections are:Chapter 1 – Introduction and RoadmapChapter 2 – What is an Operational Data Store (ODS)?Chapter 3 – What is Schema Integration?Chapter 4 – The Role of the ODS within DW ArchitecturesChapter 5 – Reverse Engineering the four Source SchemaChapter 6 – Designing the Interim SchemaChapter 7 – Preparing the ETL SpecificationsChapter 8 – Designing the Physical ODS Database ModelChapter 9 – Designing Our ETL processes with SSISChapter 10 – Data Quality Profiling

Do you enjoy completing puzzles? Perhaps one of the most challenging (yet rewarding) puzzles is delivering a successful data warehouse suitable for data mining and analytics. The Analytical Puzzle describes an unbiased, practical, and comprehensive approach to building a data warehouse which will lead to an increased level of business intelligence within your organisation. New technologies continuously impact this approach and therefore this book explains how to leverage big data, cloud computing, data warehouse appliances, data mining, predictive analytics, data visualisation and mobile devices. This book describes an unbiased, practical, and comprehensive approach to building a data warehouse which will lead to an increased level of business intelligence within your organisation. New technologies continuously impact this approach and therefore this book explains how to leverage big data, cloud computing, data warehouse appliances, data mining, predictive analytics, data visualisation and mobile devices.

Many Cultures, One Team is an essential aid for anyone who leads, is a member of, consults with, or supports global teams.  Starting from the premise that the concept of team is culturally bound, Catherine Mercer Bing provides guidance for improving team function and performance. Drawing on her extensive experience in supporting global teams, she challenges team leaders and members to reflect on their cultural assumptions – to improve their cultural metacognition – and provides key advice concerning engagement, productivity, and human process interactions on teams. Global competition is fierce, and the timeframe within which businesses maintain their competitive advantage is now counted in months rather than years. One important source of competitive advantage is human behavior. Team leaders that manage the subtle, but powerful, forces of group dynamics and culture achieve better business outcomes. Team leaders that fail to identify and manage these subtle forces in real-time risk having their plans thwarted.

Here you will learn how to develop an attractive, easily readable, conceptual, business-oriented entity/relationship model, using a variation on the UML Class Model notation. This book has two audiences: Data modelers (both analysts and database designers) who are convinced that UML has nothing to do with them; and UML experts who don't realize that architectural data modeling really is different from object modeling (and that the differences are important).David Hay's objective is to finally bring these two groups together in peace. Here all modelers will receive guidance on how to produce a high quality (that is, readable) entity/relationship model to describe the data architecture of an organization. The notation involved happens to be the one for class models in the Unified Modeling Language, even though UML was originally developed to support object-oriented design. Designers have a different view of the world from those who develop business-oriented conceptual data models, which means that to use UML for architectural modeling requires some adjustments. These adjustments are described in this book. David Hay is the author of Enterprise Model Patterns: Describing the World, a comprehensive model of a generic enterprise. The diagrams were at various levels of abstraction, and they were all rendered in the slightly modified version of UML Class Diagrams presented here. This book is a handbook to describe how to build models such as these. By way of background, an appendix provides a history of the two groups, revealing the sources of their different attitudes towards the system development process.

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: Build an unstructured data warehouse using the 11-step approach Integrate text and describe it in terms of homogeneity, relevance, medium, volume, and structure Overcome challenges including blather, the Tower of Babel, and lack of natural relationships Avoid the Data Junkyard and combat the "Spider's Web" Reuse techniques perfected in the traditional data warehouse and Data Warehouse 2.0,including iterative development Apply essential techniques for textual Extract, Transform, and Load (ETL) such as phrase recognition, stop word filtering, and synonym replacement Design the Document Inventory system and link unstructured text to structured data Leverage indexes for efficient text analysis and taxonomies for useful external categorization Manage large volumes of data using advanced techniques such as backward pointers Evaluate technology choices suitable for unstructured data processing, such as data warehouse appliances

Join Simon on a quest to uncover the value of Enterprise Architecture (and it's not what you think it is!)Simon is a seasoned Enterprise Architect who joins a corporation in New York as their first-ever Vice President of Enterprise Architecture. On his very first day, he meets the global Chief Executive Officer (CEO) who asks Simon 'What do you do?' Simon's reply triggers the CEO to respond in a way that our hero least expects. What follows is a journey across continents and oceans in which Simon uncovers the true meaning of Enterprise Architecture, who is doing it, and how successful they are.On his travels, Simon teams up with senior executives around the world to integrate Enterprise Architecture into their strategies and business plans, and to innovate in the architecture of their enterprise. Everyone he meets has some wisdom to offer, and is looking for his in return. Finally, Simon has to make a choice between the kind of Enterprise Architect he used to be and the one he has become.Join the characters in this sequel to the highly-acclaimed business novel fruITion, as they contribute to Simon's journey and he makes his final choice. Share in his thoughts and experiences, and join the author in observing key messages along the journey.

Rediscover how your organization works and where it can be improved by using simple, yet powerful techniques!How Work Gets Done will provide the business or IT professional with a practical working knowledge of Business Process Management (BPM). This book is written in a conversational style that encourages you to read it from start to finish and master these objectives: Learn how to identify the goals and drivers important to your organization and how to align these with key performance measures Understand how business strategies, business policies, and operational procedures need to be connected within a Business Process Architecture Know the basic building blocks of any business process - Inputs, Outputs, Guides, and Enablers Learn how to create a BPM Center of Excellence in your organization Acquire the skills to establish a BPM methodology addressing Enterprise-level, Process-Level, and Implementation-Level priorities Learn how to build a Process Competency Framework encompassing all BPM stakeholders Obtain the knowledge to improve a process step-by-step with easy to use techniques and templates such as swimlanes and flowcharts

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 is 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, organise, and utilise data about products, customers, competitors, and employees. Fortunately, improving your data quality does not have to be such a mammoth task. This book is a must read for anyone who needs to understand, correct, or prevent data quality issues in their organisation. Skipping theory and focusing purely on what is practical and what works, this text contains a proven approach to identifying, warehousing, and analysing data errors. Master techniques in data profiling and gathering metadata, designing data quality rules, organising rule and error catalogues, and constructing the dimensional data quality scorecard. David Wells, Director of Education of the Data Warehousing Institute, says "This is one of those books that marks a milestone in the evolution of a discipline. Arkady''s insights and techniques fuel the transition of data quality management from art to science -- from crafting to engineering. From deep experience, with thoughtful structure, and with engaging style Arkady brings the discipline of data quality to practitioners."

This book is for practitioners and academics who have learned the conventions and rules of data modelling and are looking for a deeper understanding of the discipline. The coverage of theory includes a detailed review of the extensive literature on data modelling and logical database design, referencing nearly 500 publications, with a strong focus on their relevance to practice. The practice component incorporates the largest-ever study of data modelling practitioners, involving over 450 participants in interviews, surveys and data modelling tasks. The results challenge many long-standing held assumptions about data modelling and will be of interest to academics and practitioners alike. Graeme Simsion brings to the book the practical perspective and intellectual clarity that have made his "Data Modelling Essentials" a classic in the field. He begins with a question about the nature of data modelling (design or description), and uses it to illuminate such issues as the definition of data modelling, its philosophical underpinnings, inputs and deliverables, the necessary behaviours and skills, the role of creativity, product diversity, quality measures, personal styles, and the differences between experts and novices. "Data Modeling: Theory and Practice" is essential reading for anyone involved in data modelling practice, research, or teaching.