A New Data Warehousing Strategy, Methodology and Guide to the Free Ready Made Template for Full Supply Chain and Sales & Operations Reporting

It is in the context of maturity that one needs to read the book - Master Data Management and Enterprise Engineering by Dr. M. Naoulo. The world of technology has several related yet distinct approaches - Martin's and Finklestein's information engineering, Ted Codd's relational technology, Inmon's data warehouse, and others. This book takes these approaches and others and does two important things - it blends them together and it takes the bodies of thought and turns them into an engineering approach. As such this book is another important step in the evolution of computer science. This is a next step in the maturation of computer science. I recommend it to any serious student of computer science or to any serious practitioner. =========== W.H. (Bill) Inmon. May 21, 2012. =========== The book establishes the Fundamentals of design, modeling, architecture, and management of Master, Transactional, and Process Data and the Principles of Enterprise Engineering. The book comprises innovative techniques and elegant approach and design to address: > Master Data Management thru grouping and classifying the Master Data in an innovative way that can be implemented across the Enterprise Data Architecture: Central Data Repository and Business and Enterprise Intelligence (BI & EI) Data Marts. This data classification is based on the questions: Why, How, Who, What, Where and When. > Separating the Master Data, Transactional Data, and Process Data. This separation enables the easiness in dealing with MDM, BI, and EI. The separation approach enormously facilitates the mapping and propagation between the Central Data Repository and the Business and Enterprise Data Marts. >The basics and techniques of the design of the Enterprise Engineering Model. This model supports the Transactional Systems, Business Intelligence, Business Process Management, Enterprise Intelligence, and Enterprise Engineering. > Synchronization and Integration of Master, Transactional, and Process Data across the enterprise: Legacy Systems, Central Data Repository, and Business and Intelligence Data Marts. > Assessing the different Enterprise Data Architectures. The first part encompasses the Enterprise Data Framework and its new Modeling Techniques. The Enterprise Data Framework illustrates and depicts the Data Architecture across the enterprise covering the integration and consolidation of the data of the Legacy Systems in a Central Data Repository and the propagation of this data into the Business and Enterprise Intelligence Data Marts. The Enterprise Engineering Model presents a clear and concise illustration depicting the Operational aspects of the Enterprise and their relations to the Enterprise's needs. It includes: > Master Data representing the main objects of an enterprise, > Transactional Data detailing the results of transactions occurring in an enterprise, and > Process Data capturing the data pertinent to the activities of the functioning of an enterprise. The second part encompasses the Enterprise Engineering Framework, Methodology, Guidelines, Deliverables, and Techniques. It bestows the blueprint of the functioning of enterprises. This part details the basics of Enterprise Engineering and its implementation thru the processing of the Enterprise Engineering Model. It provides the cost data (materiel cost, labor cost, time) reflecting the functioning of the Enterprise and point out the efficiency, performance, strengths, and weaknesses of its operation. Detailed Case Studies are presented supporting the theoretical aspect of Enterprise Engineering. These Case Studies provide clear and practical hands-on exercises reflecting the functioning of the Enterprises and illustrating the implementation of Enterprise Engineering.

This book has step-by-step instructions to solve data manipulation problems using PDI in the form of recipes. It has plenty of well-organized tips, screenshots, tables, and examples to aid quick and easy understanding. If you are a software developer or anyone involved or interested in developing ETL solutions, or in general, doing any kind of data manipulation, this book is for you. It does not cover PDI basics, SQL basics, or database concepts. You are expected to have a basic understanding of the PDI tool, SQL language, and databases.

This easy-to-understand tutorial covers Oracle Warehouse Builder from the ground up, and taps into the author's wide experience as a software and database engineer. Written in a relaxed style with step-by-step explanations, lots of screenshots are provided throughout the book. There are numerous tips and helpful hints throughout that are not found in the original documentation. By following this book, you can use Oracle Warehouse Builder in the best possible way and maximize your learning potential. This book is an update of Oracle Warehouse Builder 11g: Getting Started. This book is a good starting point for database engineers, administrators, and architects who are responsible for data warehouse projects and need to design them and load data into them. If you are someone who wants to learn Oracle Warehouse Builder and expand your knowledge of the tool and data warehousing, this is an ideal book for you. No prior data warehouse or database experience is presumed. All new database and data warehouse technical terms and concepts explained in clear easy-to-understand language.

Are you struggling with a disparate data resource? Are there multiple existences of the same business fact scattered throughout the data resource? Are those multiple existences out of synch with each other? Do you have difficulty finding the data you need to support business activities? Do the data you find have poor quality? If the answer to any of these questions is Yes, then you need this book to guide you toward creating an integrated data resource. Most public and private sector organisations have a disparate data resource that was created over many years. That disparate data resource contains multiple existences of business facts that are out of synch with each other, are of poor quality, and are difficult to locate. The traditional approach to dealing with a disparate data resource is to perform periodic and temporary data integration to support a specific application or business activity. Those piecemeal data integration efforts may meet a current need, but seldom solve the underlying problems with a disparate data resource, and sometimes make the situation worse. This book explains how to go about understanding and resolving a disparate data resource and creating a comparate data resource that fully meets an organisation's current and future business information demand. It builds on "Data Resource Simplexity", which described how to stop the burgeoning data disparity. It explains the concepts, principles, and techniques for understanding a disparate data resource within the context of a common data architecture, and resolving that disparity with minimum impact on the business. Like "Data Resource Simplexity", Michael Brackett draws on five decades of data management experience building and managing data resources, and resolving disparate data resources 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, and biology, and applies them to the process of understanding and resolving a disparate data resource. He shows you how to approach and resolve a disparate data resource, and build a comparate data resource that fully supports the business.

Business intelligence is a huge segment of the software world. Gartner Group estimates that sales in this area surpassed $10 billion in 2010, with Oracle as the second largest vendor in the category. At the heart of these analytical-oriented applications are dimensional data models with OLAP as a critical component for achieving high-performance.

If you want to do development work in this area or understand how to maximize its value, read this book. A professional in the world of analytics and business intelligence needs an understanding of OLAP's specific data modeling principles, its analysis capabilities, and its relationship to other analytical approaches. All are presented here in a systematic fashion written in an easy-to-follow style.

"The Multidimensional Data Modeling Toolkit" takes you on an instructional journey into the world of OLAP. It provides a comprehensive examination of data modeling and analytical techniques for the native multi-dimensional information storage framework that Oracle OLAP provides. You will get an in-depth look at OLAP's analytical possibilities as well as comparison with the approaches used in data mining and statistics. You will learn the design issues and get step-by-step programming instructions for solving real-world problems. Written by an expert with over 15 years experience using Oracle OLAP and its predecessors, the book explains critical techniques rarely taught in university or technical training programs.

"The Multidimensional Data Modeling Toolkit" takes you under the covers and shows you what happens inside of Oracle's Analytic Workspaces where the multidimensional magic occurs. Programming instruction is based on the Oracle 10g database, but most of the statements shown will work with other editions of the database, such as Oracle 9i and 11g, and even earlier editions of the technology found in stand-alone products such Oracle Financial Analyzer and Oracle Sales Analyzer

The data analysis principles presented are universal and can be applied to application that uses OLAP, for example OBIEE, Essbase, Cognos, Business Objects, and Microsoft Analysis Services (SSAS). Whether you are new to business intelligence or a seasoned practitioner, you should find The Multi-dimensional Data Modeling Toolkit with plenty of valuable insights to offer.

An easy-to-follow introduction to support vector machines

This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover:

Knowledge discovery environments

Describing data mathematically

Linear decision surfaces and functions

Perceptron learning

Maximum margin classifiers

Support vector machines

Elements of statistical learning theory

Multi-class classification

Regression with support vector machines

Novelty detection

Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.

According to leading analysts, Business Intelligence (BI) has been a top priority for worldwide organizations within the past five years, and it will continue to be a priority in the near future. With a global user base of millions, Cognos is known as a leading provider of Business Intelligence tools. Cognos' latest release is Cognos 8 BI, a powerful suite of modules that share one common infrastructure for the creation, management and deployment of queries, reports, analyses, scorecards, dashboards and alerts--all designed to support an organization's Business Intelligence objectives. As a consultant with over 10 years of Cognos BI experience, Juan A. Padilla is an expert in helping organizations develop and implement Business Intelligence solutions. As the first in a series of books on Cognos products, with Cognos 8 BI for Consumers, Padilla provides a step-by-step introductory guide for the key component of the Cognos infrastructure: Cognos Connection. This book walks the reader through the fundamentals of Cognos Connection, the powerful web portal that is the foundation for all users of Cognos 8 BI, from end users to administrators. The guide relies heavily on screen images that demonstrate product workflow and available features; even readers who do not have "live" access to Cognos software can learn about it This guide has been designed for: - companies evaluating Cognos as a potential BI solution and need to know the capabilities before purchasing; - those planning to use Cognos products professionally, e.g. job-seekers or consultants; - organizations using previous versions of Cognos who need to evaluate the latest version before upgrading or migrating; and - novice "hands-on" users of Cognos, for whom this guide will be an "anytime, anywhere" tutorial and reference source. In this book, you will discover: - how Cognos Connection functions; - how to work with reports, including scheduling, setting parameters and changing formats; - how to customize the look and feel of the interface to your preferences; - a simulation of Cognos' security features; and - report samples that show the powerful reporting capabilities of Cognos 8 BI.

* This is the first book to provide in--depth coverage of star schema aggregates used in dimensional modeling--from selection and design, to loading and usage, to specific tasks and deliverables for implementation projects* Covers the principles of aggregate schema design and the pros and cons of various types of commercial solutions for navigating and building aggregates* Discusses how to include aggregates in data warehouse development projects that focus on incremental development, iterative builds, and early data loads

Before SQL programmers could begin working with OLTP (On-Line Transaction Processing) systems, they had to unlearn procedural, record-oriented programming before moving on to SQL s declarative, set-oriented programming. This book covers the next step in your growth. OLAP (On-Line Analytical Processing), Data Warehousing and Analytics involve seeing data in the aggregate and over time, not as single transactions. Once more it is time to unlearn what you were previously taught.
This book is not an in-depth look at particular subjects, but an overview of many subjects that will give the working RDBMS programmers a map of the terra incognita they will face if they want to grow.
* Expert advice from a noted SQL authority and award-winning columnist, who has given ten years of service to the ANSI SQL standards committee and many more years of dependable help to readers of online forums.
* First book that teaches what SQL programmers need in order to successfully make the transition from transactional systems (OLTP) into the world of data warehouse data and OLAP.
* Offers real-world insights and lots of practical examples.
* Covers the OLAP extensions in SQL-99; ETL tools, OLAP features supported in DBMSs, other query tools, simple reports, and statistical software."

Data Warehousing 101: Concepts and Implementation will appeal to those planning data warehouse projects, senior executives, project managers, and project implementation team members. It will also be useful to functional managers, business analysts, developers, power users, and end-users. Data Warehousing 101: Concepts and Implementation, which can be used as a textbook in an introductory data warehouse course, can also be used as a supplemental text in IT courses that cover the subject of data warehousing. Data Warehousing 101: Concepts and Implementation reviews the evolution of data warehousing and its growth drivers, process and architecture, data warehouse characteristics and design, data marts, multi-dimensionality, and OLAP. It also shows how to plan a data warehouse project as well as build and operate data warehouses. Data Warehousing 101: Concepts and Implementation also covers, in depth, common failure causes and mistakes and provides useful guidelines and tips for avoiding common mistakes.

Three books by the bestselling authors on Data Warehousing The most authoritative guides from the inventor of the technique all for a value price.

The Data Warehouse Toolkit, 3rd Edition (9781118530801) Ralph Kimball invented a data warehousing technique called "dimensional modeling" and popularized it in his first Wiley book, The Data Warehouse Toolkit. Since this book was first published in 1996, dimensional modeling has become the most widely accepted technique for data warehouse design. Over the past 10 years, Kimball has improved on his earlier techniques and created many new ones. In this 3rd edition, he will provide a comprehensive collection of all of these techniques, from basic to advanced.

The Data Warehouse Lifecycle Toolkit, 2nd Edition (9780470149775) Complete coverage of best practices from data warehouse project inception through on-going program management. Updates industry best practices to be in sync with current recommendations of Kimball Group. Streamlines the lifecycle methodology to be more efficient and user-friendly

The Data Warehouse ETL Toolkit (9780764567575) shows data warehouse developers how to effectively manage the ETL (Extract, Transform, Load) phase of the data warehouse development lifecycle. The authors show developers the best methods for extracting data from scattered sources throughout the enterprise, removing obsolete, redundant, and innaccurate data, transforming the remaining data into correctly formatted data structures, and then physically loading them into the data warehouse. This book provides complete coverage of proven, time-saving ETL techniques. It begins with a quick overview of ETL fundamentals and the role of the ETL development team. It then quickly moves into an overview of the ETL data structures, both relational and dimensional. The authors show how to build useful dimensional stuctures, providing practical examples of beginning through advanced techniques.

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Master Oracle SOA Suite 12cDesign, implement, manage, and maintain a highly flexible service-oriented computing infrastructure across your enterprise using the detailed information in this Oracle Press guide. Written by an Oracle ACE director, Oracle SOA Suite 12c Handbook uses a start-to-finish case study to illustrate each concept and technique. Learn expert techniques for designing and implementing components, assembling composite applications, integrating Java, handling complex business logic, and maximizing code reuse. Runtime administration, governance, and security are covered in this practical resource. Get started with the Oracle SOA Suite 12c development and run time environment Deploy and manage SOA composite applications Expose SOAP/XML REST/JSON through Oracle Service Bus Establish interactions through adapters for Database, JMS, File/FTP, UMS, LDAP, and Coherence Embed custom logic using Java and the Spring component Perform fast data analysis in real time with Oracle Event Processor Implement Event Drive Architecture based on the Event Delivery Network (EDN) Use Oracle Business Rules to encapsulate logic and automate decisions Model complex processes using BPEL, BPMN, and human task components Establish KPIs and evaluate performance using Oracle Business Activity Monitoring Control traffic, audit system activity, and encrypt sensitive data

The Data Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical data warehousing failures. "Building a Scalable Data Warehouse" covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. The book discusses how to build the data warehouse incrementally using the agile Data Vault 2.0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss: How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes. Important data warehouse technologies and practices. Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture.

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Best Practices for Comprehensive Oracle Database SecurityWritten by renowned experts from Oracle's National Security Group, Oracle Database 12c Security provides proven techniques for designing, implementing, and certifying secure Oracle Database systems in amultitenant architecture. The strategies are also applicable to standalone databases. This Oracle Press guide addresses everything from infrastructure to audit lifecycle and describes how to apply security measures in a holistic manner. The latest security features of Oracle Database 12c are explored in detail with practical and easy-to-understand examples. Connect users to databases in a secure manner Manage identity, authentication, and access control Implement database application security Provide security policies across enterprise applications using Real Application Security Control data access with OracleVirtual Private Database Control sensitive data using data redaction and transparent sensitive data protection Control data access with Oracle Label Security Use Oracle Database Vault and Transparent Data Encryption for compliance, cybersecurity, and insider threats Implement auditing technologies, including Unified Audit Trail Manage security policies and monitor a secure databaseenvironment with Oracle Enterprise Manager Cloud Control

An introduction to the field of applied ontology with examples derived particularly from biomedicine, covering theoretical components, design practices, and practical applications. In the era of "big data," science is increasingly information driven, and the potential for computers to store, manage, and integrate massive amounts of data has given rise to such new disciplinary fields as biomedical informatics. Applied ontology offers a strategy for the organization of scientific information in computer-tractable form, drawing on concepts not only from computer and information science but also from linguistics, logic, and philosophy. This book provides an introduction to the field of applied ontology that is of particular relevance to biomedicine, covering theoretical components of ontologies, best practices for ontology design, and examples of biomedical ontologies in use. After defining an ontology as a representation of the types of entities in a given domain, the book distinguishes between different kinds of ontologies and taxonomies, and shows how applied ontology draws on more traditional ideas from metaphysics. It presents the core features of the Basic Formal Ontology (BFO), now used by over one hundred ontology projects around the world, and offers examples of domain ontologies that utilize BFO. The book also describes Web Ontology Language (OWL), a common framework for Semantic Web technologies. Throughout, the book provides concrete recommendations for the design and construction of domain ontologies.

Imagine spending a day with top analytical leaders and asking any question you want. In this book, Wayne Eckerson illustrates analytical best practices by weaving his perspective with commentary from seven directors of analytics who unveil their secrets of success. With an innovative flair, Eckerson tackles a complex subject with clarity and insight. Each of the books 20 chapters is a stand-alone essay on an analytical topic, yet collectively they form a concise methodology about how to implement a successful analytics program.

Let's step back to the year 1978. Sony introduces hip portable music with the Walkman, Illinois Bell Company releases the first mobile phone, Space Invaders kicks off the video game craze, and William Kent writes this book. We have made amazing progress in the last four decades in terms of portable music, mobile communication, and entertainment, making devices such as the original Sony Walkman and suitcase-sized mobile phones museum pieces today. Yet remarkably, the book Data and Reality is just as relevant to the field of data management today as it was in 1978. This book gracefully weaves the disciplines of psychology and philosophy with data management to create timeless takeaways on how we perceive and manage information. Although databases and related technology have come a long way since 1978, the process of eliciting business requirements and how we think about information remains constant. This book will provide valuable insights whether you are a 1970s data-processing expert or a modern-day business analyst, data modeller, database administrator, or data architect. This 3rd edition differs substantially from the first and second editions. Data modelling thought leader Steve Hoberman has updated many of the original examples and references and added his commentary throughout the book, including key points at the end of each chapter. The important takeaways in this book are rich with insight yet presented in a conversational and easy-to-grasp writing style. Here are just a few of the issues this book tackles: Has "business intelligence" replaced "artificial intelligence"? Why is a maps geographic landscape analogous to a data models information landscape? Where do forward and reverse engineering fit in our thought process? Why are we all becoming "data archaeologists"? What causes the communication chasm between the business professional and the information technology professional in most organisations, and how can the logical data model help bridge this chasm? Why do we invest in hardware and software to solve business problems before determining what the business problems are in the first place? What is the difference between oneness, sameness, and categories? Why does context play a role in every design decision? Why do the more important attributes become entities or relationships? Why do symbols speak louder than words? Whats the difference between a data modeller, a philosopher, and an artist? Why is the 1975 dream of mapping all attributes still a dream today? What influence does language have on our perception of reality? Can we distinguish between naming and describing?