Books published by Technics Publications LLC

The Data Whisperer''s practical guide to explaining and understanding the strategic value of data management. The need for data management is everywhere across your company. The value of every digitally transformative customer-facing initiative, every data science and analytics-based project, every as-a-service offering, every foray into e-commerce, and every enterprise software implementation is inextricably linked to the successful output of data management efforts. Although it is a simple function of garbage in garbage out, that slogan rarely drives any sustainable executive action. We need to tell a better data story. Data Storytelling is probably the hottest non-technical trend in the technology-related space. But it does not directly support data management because it is focused on analytics or telling stories with data. So, it is time to expand the realm of Data Storytelling to recognize the role of data management by telling stories about data. Learn how to secure stakeholder involvement and executive commitment to fund and support data management as a systematic, consistent, fundamental part of your business. This book is for:Data management leaders trying to explain your value to C-Level and business stakeholders. As a practitioner, you may already know how to fix your data, but your business leaders ignore your advice. When you explain data management to the business, they may nod "yes" on the outside, but they nod off on the inside.Business stakeholders trying to comprehend why data management is important. Many business people may be frightened, threatened, intimidated, or at the very least confused and bewildered by the techno-babble often associated with data-related conversations. If you want to know more about why data management needs to be a strategic imperative in your organization, you''ll learn it here in simple terms.Data scientists looking to understand better how you connect to "The Business." A recurring struggle I hear from data scientists is the need to get "closer to business." If you are a data scientist, then you need to understand your company''s data story. The more you can align your work to the core value your company delivers, the more successful you will be. This book will help you discover the essence of why data brings value to your business.Anyone interested in understanding the business value of data management. I offer simple explanations about why data management is essential for your organization. Without going deep into technical concepts and processes, I focus on the business-related outputs. I share ways you can think about what foundational data does. Its importance is vital for the future of your enterprise.Since this is a book about telling data stories, I share it through stories divided into five sections:My data story. Why I know what I know and why you should listen to me.Everyone''s data story. A collection of classic, foundational data situations relevant to all enterprises.Framing your data story. A set of simple frameworks about data value.Selling your data story. Tips on creating a compelling narrative.Building your data story. Why you must align with the strategic intentions of your enterprise.

I programmi di Data Governance si concentrano sull''autorit├á e la responsabilit├á per la gestione dei dati come asset di valore per l''organizzazione.La Data Governance non dovrebbe riguardare comando-e-controllo, ma a volte potrebbe diventare invasiva o minacciosa per il lavoro, le persone e la cultura di un''organizzazione. Non-Invasive Data Governance si concentra sul formalizzare la responsabilit├á esistente per la gestione dei dati e migliorare le comunicazioni formali, la protezione, e gli impegni per la qualit├á attraverso un efficace cura delle sorgenti di dati.Non-Invasive Data Governance offre un set completo di tool per supportare l''esecuzione di un programma di data governance di successo. Imparerai come:I compiti degli Steward possono essere identificati e riconosciuti, formalizzati e accolti in accordo con le loro responsabilit├á esistenti piuttosto che essere assegnati o affidati a persone come lavoro aggiuntivo.La Governance dell''informazione pu├▓ essere applicata a policy esistenti, procedure operative standard, pratiche e metodologie piuttosto che essere introdotta o promossa come processo o metodologia nuova.La Governance dell''informazione pu├▓ supportare tutta la data integration, il risk management, la business intelligence e le attivit├á di master data management piuttosto che imporre un rigore incoerente a queste iniziative.Un approccio pratico e non-minaccioso pu├▓ essere applicato per governare le informazioni e promuovere la stewardship dei dati come un asset cross-organizzazione.Le Best Practice e i concetti chiave di questo approccio non-minaccioso possono essere comunicati efficacemente per sfruttare i punti di forza e indirizzare le opportunit├á per il miglioramento. 

Some things seem so obvious that they don''t need to be spelled out in detail. Or do they? In computing, at least (and probably in any discipline where accuracy and precision are important), it can be quite dangerous just to assume that some given concept is "obvious," and indeed universally understood. Serious mistakes can happen that way! The first part of this book discusses features of the database field-equality, assignment, naming-where just such an assumption seems to have been made, and it describes some of the unfortunate mistakes that have occurred as a consequence. It also explains how and why the features in question aren''t quite as obvious as they might seem, and it offers some advice on how to work around the problems caused by assumptions to the contrary. Other parts of the book also deal with database issues where devoting some preliminary effort to spelling out exactly what the issues in question entailed could have led to much better interfaces and much more carefully designed languages. The issues discussed include redundancy and indeterminacy; persistence, encapsulation, and decapsulation; the ACID properties of transactions; and types vs. units of measure. Finally, the book also contains a detailed deconstruction of, and response to, various recent pronouncements from the database literature, all of them having to do with relational technology. Once again, the opinions expressed in those pronouncements might seem "obvious" to some people (to the writers at least, presumably), but the fact remains that they''re misleading at best, and in most cases just flat out wrong.  

Als u een organisatie leidt of als u met uw managers moet communiceren over datamanagement, dan is Navigeren door het Labyrint uw gids. Organisaties die waarde willen halen uit hun data moeten die data goed beheren. Maar voor de meeste leidinggevenden lijkt datamanagement obscuur, gecompliceerd, en zeer technisch. Je hebt geen tijd om alle details te leren of hypes te herkennen. Navigeren door het Labyrint helpt je verder, gebaseerd op de best practices van DAMA''s Data Management Body of Knowledge (DMBOK2). Het legt de grondbeginselen uit en zegt waarom ze zo belangrijk zijn. Het vertelt u wat u moet weten om in uw organisatie vertrouwen in data op te bouwen en hoe u waarde uit data kan halen. Over DAMA DAMA International is een leveranciersonafhankelijke vereniging zonder winstoogmerk bestaande uit datamanagement professionals die zich toeleggen op het bevorderen van datamanagementconcepten en best practices ter ondersteuning van de bedrijfsstrategie. Via lokale verenigingen, zoals DAMA Nederland biedt DAMA International deze best practices aan via een netwerk van verbonden personen en organisaties die idee├½n, trends en oplossingen delen. Zij zien DAMA als de vertrouwde, collaboratieve centrale bron voor iedereen. Bezoek dama.org of dama-nl.org voor meer informatie.Over Laura Laura Sebastian-Coleman heeft, sinds 2003, gewerkt aan datakwaliteit van grote analytical data warehouses in de gezondheidszorg. Zij heeft datakwaliteitskencijfers en -rapportages ge├»mplementeerd, teams voor datakwaliteit gevormd, en data-afnemers getraind. Ze heeft datastandaarden gedefinieerd en het beheer van metadata voor grote analytische datawarehouses en voor Big data gemanaged.In 2015 werd ze benoemd tot DAMA Publications Officer en was ze eindredacteur voor DMBOK2. Hiervoor heeft ze de prestigieuze jaarlijkse prijs van DAMA International voor ''Outstanding Service to the Data Management Profession'' ontvangen. Ze is ook auteur van Measuring Data Quality for Ongoing Improvement.

La Gu├¡a del Conocimiento para la Gesti├│n de Datos (DAMA-DMBOK2) presenta una visi├│n exhaustiva de los desaf├¡os, complejidades y valor de la gesti├│n eficaz de los datos.Las organizaciones de hoy en d├¡a reconocen que la gesti├│n de los datos es fundamental para su ├⌐xito. Reconocen que los datos tienen valor y quieren aprovechar ese valor. A medida que nuestra capacidad y deseo de crear y explotar datos ha aumentado, tambi├⌐n lo ha hecho la necesidad de pr├ícticas de gesti├│n de datos confiables.La segunda edici├│n de la Gu├¡a del Conocimiento para la Gesti├│n de Datos de DAMA International actualiza y aumenta el exitoso DMBOK1. DMBOK2, un libro de referencia accesible y autorizado, escrito por los principales pensadores en el campo y ampliamente revisado por los miembros de DAMA, re├║ne materiales que describen exhaustivamente los desaf├¡os de la gesti├│n de datos y c├│mo cumplirlos mediante:┬╖        Definir un conjunto de principios rectores para la gesti├│n de datos y describir c├│mo se pueden aplicar estos principios dentro de las ├íreas funcionales de gesti├│n de datos.┬╖        Proporcionar un marco de referencia funcional para la implementaci├│n de pr├ícticas de gesti├│n de datos empresariales, incluyendo pr├ícticas, m├⌐todos y t├⌐cnicas ampliamente adoptadas, funciones, roles, entregables y m├⌐tricas.┬╖        Establecer un vocabulario com├║n para los conceptos de gesti├│n de datos y servir de base para las mejores pr├ícticas para los profesionales de la gesti├│n de datos.

Let the masters guide you on your Chief Data Officer (CDO) journey!The CDO is a new business role, and just starting to appear in state, local, and federal governments. Like many new roles, the CDO''s responsibilities are continuously evolving and changing. Most institutions are not consistent with assigning CDO responsibilities, and this lack of standardization is of great concern. Also, some say public and private sector CDOs are dramatically different. We don''t believe that. We think CDOs are more similar than dissimilar. More importantly, though, we think private and public CDOs can learn from each other. We''ve created this book to help all CDOs, public and private. This book is about ideas and recommendations which have broad application-on the shop floor, in a conference room, or even in legislative chambers. Apply our advice to smooth your ride during the CDO journey.Use our ideas and suggestions in ways that work for you, because every organization and data journey is different. These ideas are here to help you avoid some of our frustrations. We hope this can reach executives, managers, legislators, and others leading organizations to help bridge explanations of new vocabulary, ideas, and processes. Ultimately, we think this book will help you create data-centric value for your organization.

Deliver enterprise data analytics success by following Prashanth's prescriptive and practical techniques.Today, organizations across the globe are looking at ways to glean insights from data analytics and make good business decisions. However, not many business enterprises are successful in data analytics. According to Gartner, 80% of analytics programs do not deliver business outcomes. Mckinsey consulting says, less than 20% of the companies have achieved analytics at scale.So, how can a business enterprise avoid analytics failure and deliver business results? This book provides ten key analytics best practices that will improve the odds of delivering enterprise data analytics solutions successfully. It is intended for anyone who has a stake and interest in deriving insights from data analytics. The three key differentiating aspects of this book are: Practicality. This book offers prescriptive, superior, and practical guidance.Completeness. This book looks at data analytics holistically across the four key data analytics domains - data management, data engineering, data science, and data visualization.Neutrality. This book is technologically agnostic and looks at analytics concepts without any reference to commercial analytics products and technologies.Dr. Southekal proves why he is one of the leading thinkers on data and analytics today. 'Analytics Best Practices' is an indispensable guide for business leaders and those looking to get into the analytics field on the nuances, challenges, and immense opportunities with data.Douglas B. LaneyPrincipal, Data & Analytics Strategy, Caserta, and author of "Infonomics"

Data governance is broken.  It’s time we fix it.                                   Why is data governance so ineffective?  The truth is data governance programs aren’t designed for the way we run our data teams, they aren’t even designed for a modern organization at all.  They were designed when reports still came through inter-office mail.The flow of data into, within, and out of today’s organizations is a tsunami breaking through rigid data governance methods.  Yet our programs still rely on that command and control approach.  Have you ever tried to control a tsunami?  Every organization that uses data knows that they need a data governance program. Data literacy efforts and legislation like GDPR have become the bellwethers for our governance functions.  But we still sit in data governance meetings without enough people and too many questions to move things forward.  There’s no agility to the program because we imply a degree of frailty to the data that doesn’t exist.  We continue to insist on archaic methods that bring no value to our organizations.  Achieving deep insights from data can’t happen without good governance practices. Laura Madsen shows you how to redefine governance for the modern age.  With a casual, witty style Madsen taps on her decades of experience, shares interviews with other best-in-field experts and grounds her perspective in research.  Witness where it all fell apart, challenge long-held beliefs, and commit to a fundamental shift—that governance is not about stopping or preventing usage but about supporting the usage of data.  Be able to bring back trust and value to our data governance functions, and learn the:People-driven approach to governanceProcesses that support the tsunami of dataCutting edge technology that’s enabling data governance

This book will demystify Enterprise Architecture (EA), demonstrate its usefulness, and empower you to make EA an integral part of your organization's business management and forward planning.An organization is like a living organism. The architecture of an organism's internal structures must allow that organism to thrive within the environment in which it is operating. These "internal structures" within an organism might be organs or tissues; in an organization, though, they are the "information systems".As an organism's environment changes, its internal systems and structures must adapt. We will use this analogy as a starting point to discuss the "why" and "what" questions of enterprise architecture for information systems in organizations. To begin this process, we must switch from the traditional EA approach of looking only at internal factors, to a new, holistic view that considers the external environment. In other words, while most EA discussions are "inside-out," in this book, we will attempt to go "outside-in."Capturing the Organization Organism: Outlines a structure for organizations which is common to all organizations, regardless of the enterprise that they are involved with. Uses data subject areas from one part of enterprise architecture, the enterprise data model artifact, to describe what is internal and what is external to the organization. Provides connections between what is external and what is internal. This means describing how change is transmitted from external to internal environments, and how that change affects the architecture. Defines the enterprise architecture of business functions and business application systems that, at a broad level, are common to all organizations. Explores how common business application systems for organizations need to be different due to the different business environments in which they operate. Explains the integration requirements across an organization's business application systems, and how to address these requirements with a disparate COTS-based portfolio, while also exploring the Artificial Intelligence (AI) possibilities of an integrated environment. Reveals six key questions to help get started in understanding the organization and its operating environment.

The Principle Based Enterprise Architecture (PBEA) Method is a proven approach for implementing an enterprise-wide architecture practice in large- and medium-sized technology organizations. The method begins with a set of architecture objectives linked to concepts that matter to the business. It then lays out how to build technology platforms from components we call assets and how to manage those assets over time, through the calculation and management of technical debt.The PBEA method is a pragmatic approach to enterprise technology architecture which is based on the fundamental tenet that technology is never perfect, compromises must be made, and one of the most valuable functions an enterprise architecture group can provide for a company is a method for managing those compromises. We call the cost of these compromises "technical debt". It is essentially the difference between what we should have spent on technology and what we did spend. The PBEA method grew from the experience of watching how large technology organizations function (or do not function as the case may be). You will learn about such essential topics as: Best practices for building, managing, and ultimately evolving an enterprise architecture. Defining principles and golden rules to guide the high-quality creation of the building blocks of products and platforms (assets). Calculating technical debt and assessing the business risk associated with carrying that debt. Identifying and managing the actions required to pay off technical debt and mitigate any associated business risk.If you have witnessed products and platforms 'collapsing under the burden of technical debt', then this book is for you. If you have seen technology organizations fail to learn from their mistakes, then this book is also for you. If you have been involved in the development of products where Version 2 required almost a rewrite of Version 1 or worked in technology organizations that spend an excessive portion of their budget on maintenance, then the PBEA method may provide both insight and benefit. Or if you are an enterprise architect and have witnessed one or more Enterprise Architecture functions get eliminated because they were seen as 'too ivory tower' and too distant from the customer, then this book will provide you with a concrete, fact-based approach for building an enterprise architecture function that is fully aligned with business objectives and that delivers real measurable benefit to the corporation.

You want the rigor of good data architecture at the speed of agile? Then this is the missing link - your step-by-step guide to Data Vault success.Success with a Data Vault starts with the business and ends with the business. Sure, there's some technical stuff in the middle, and it is absolutely essential - but it's not sufficient on its own. This book will help you shape the business perspective, and weave it into the more technical aspects of Data Vault modeling.You can read the foundational books and go on courses, but one massive risk still remains. Dan Linstedt, the founder of the Data Vault, very clearly directs those building a Data Vault to base its design on an "enterprise ontology". And Hans Hultgren similarly stresses the importance of the business concepts model. So it's important. We get that. But:What on earth is an enterprise ontology/business concept model, 'cause I won't know if I've got one if I don't know what I'm looking for? If I can't find one, how do I get my hands on such a thing?Even if I have one of these wonderful things, how do I apply it to get the sort of Data Vault that's recommended?It's actually not as hard as some would fear to answer all of these questions, and it's certainly worth the effort. This book just might save you a world of pain. It's a supplement to other material on Data Vault modeling, but it's the vital missing link to finding simplicity for Data Vault success.

What is data governance? And what are the principles and techniques you can leverage as a business or IT professional to make data governance successful within your organization?Data Governance will answer these questions and provide you with insights and approaches to improve the "data fitness" of your organization. Gain control of your data and assign responsible parties to ensure the data remains well-understood and protected, by applying the content within this book's six chapters: Chapter 1, Understanding Data Governance, looks at the broad definitions of data governance along with issues within data governance. Chapter 2, Owning Data Governance, looks at Ownership, Wider Perspectives, and Roles, and explores how transparent data governance can simplify the complexities of data ownership. Chapter 3, Data Confidence, explores using tools (e.g., standards, strategies, and policies) to clearly align business objectives with realistic IT deliverables and produce meaningful outcomes. Chapter 4, Getting Data Fit, covers the basic elements required to make data governance work for your specific organization. There are five steps required to achieve a basic level of data fitness and effective governance. Chapter 5, Approach and Stakeholders, covers various ways to implement data governance to ensure there are clear milestones and trigger points for key stakeholders to approve each phase. A data scorecard is introduced as a tool to help guide an organization through the data governance process. Chapter 6, A Case Study, concerns a fictitious company, D474, used to illustrate the various examples and scenarios for implementing data governance.

Emily is feeling rebellious. Emily - the embodiment of many young business people the authors have worked with on system projects - faces a wall of "you don't understand how complex it is". She is told: "You do not have enough experience to make changes", "Best we keep going with the current work the way it is", and "We will think about improvements later." Emily becomes disillusioned and disempowered.Emily's Rebellion presents a new method of removing the complexity from business processes and information systems called the 'Transaction Pattern'. Emily has learned about Service Design and loves it, but she needs a way to bridge the gap between her customer-focused service blueprint and the technical-minded developers.The Transaction Pattern is Emily's bridge. It breaks down a service design into transactions and then into a generic pattern of phases and tasks that commonly recur. This structured approach, based on the pattern, readily specifies business requirements for system development and process implementation.Emily's Rebellion seeks to embolden people like Emily who are required to inhabit the space between the everyday operations of their business and technology 'improvement' and digitization projects. You can effect change today with simple steps - it does not have to be so complex. Walk with Emily as she discovers a new path to get better business outcomes from IT projects.

Master the approaches and principles of Artificial Intelligence (AI) algorithms, and apply them to Data Science projects with Python and Julia code.Aspiring and practicing Data Science and AI professionals, along with Python and Julia programmers, will practice numerous AI algorithms and develop a more holistic understanding of the field of AI, and will learn when to use each framework to tackle projects in our increasingly complex world.The first two chapters introduce the field, with Chapter 1 surveying Deep Learning models and Chapter 2 providing an overview of algorithms beyond Deep Learning, including Optimization, Fuzzy Logic, and Artificial Creativity.The next chapters focus on AI frameworks; they contain data and Python and Julia code in a provided Docker, so you can practice. Chapter 3 covers Apache's MXNet, Chapter 4 covers TensorFlow, and Chapter 5 investigates Keras. After covering these Deep Learning frameworks, we explore a series of optimization frameworks, with Chapter 6 covering Particle Swarm Optimization (PSO), Chapter 7 on Genetic Algorithms (GAs), and Chapter 8 discussing Simulated Annealing (SA).Chapter 9 begins our exploration of advanced AI methods, by covering Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Chapter 10 discusses optimization ensembles and how they can add value to the Data Science pipeline.Chapter 11 contains several alternative AI frameworks including Extreme Learning Machines (ELMs), Capsule Networks (CapsNets), and Fuzzy Inference Systems (FIS).Chapter 12 covers other considerations complementary to the AI topics covered, including Big Data concepts, Data Science specialization areas, and useful data resources to experiment on.A comprehensive glossary is included, as well as a series of appendices covering Transfer Learning, Reinforcement Learning, Autoencoder Systems, and Generative Adversarial Networks. There is also an appendix on the business aspects of AI in data science projects, and an appendix on how to use the Docker image to access the book's data and code.The field of AI is vast, and can be overwhelming for the newcomer to approach. This book will arm you with a solid understanding of the field, plus inspire you to explore further.

Learn how blockchain works, where to use it within your organization, and how it will impact data management.This book contains three parts: Explanation. Part I will explain will explain the concepts underlying blockchain. A precise and concise definition is provided, distinguishing blockchain from blockchain architecture. Variations of blockchain are explored based upon the concepts of purpose and scope. Usage. Now that you understand blockchain, where do you use it? The reason for building a blockchain application must include at least one of these five drivers: transparency, streamlining, privacy, permanence, or distribution. Usages based upon these five drivers are shown for finance, insurance, government, manufacturing and retail, utilities, healthcare, nonprofit, and media. Process diagrams will illustrate each usage through inputs, guides, enablers, and outputs. Also examined are the risks of applying these usages, such as cooperation, incentives, and change. Impact. Now that you know where to use blockchain, how will it impact our existing IT (Information Technology) environment? Part III explores how blockchain will impact data management. The Data Management Body of Knowledge 2nd Edition (DAMA-DMBOK2) is an amazing book that defines the data management field along with the often complex relationships that exist between the various data management disciplines. Learn how blockchain will impact each of these 11 disciplines: Data Governance, Data Architecture, Data Modeling and Design, Data Storage and Operations, Data Security, Data Integration and Interoperability, Document and Content Management, Reference and Master Data, Data Warehousing and Business Intelligence, Metadata Management, and Data Quality Management.Once you understand blockchain concepts and principles, you can position yourself, department, and organization to leverage distributed ledger technology.

The Data Management Body of Knowledge (DAMA-DMBOK2) presents a comprehensive view of the challenges, complexities, and value of effective data management. Today's organizations recognize that managing data is central to their success. They recognize data has value and they want to leverage that value. As our ability and desire to create and exploit data has increased, so too has the need for reliable data management practices.The second edition of DAMA International's Guide to the Data Management Body of Knowledge updates and augments the highly successful DMBOK1. An accessible, authoritative reference book written by leading thinkers in the field and extensively reviewed by DAMA members, DMBOK2 brings together materials that comprehensively describe the challenges of data management and how to meet them by: Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas. Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics. Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals.DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties The value of data can be and should be expressed in economic terms Managing data means managing the quality of data It takes metadata to manage data It takes planning to manage data Data management is cross-functional and requires a range of skills and expertise Data management requires an enterprise perspective Data management must account for a range of perspectives Data management is data lifecycle management Different types of data have different lifecycle requirements Managing data includes managing risks associated with data Data management requirements must drive information technology decisions Effective data management requires leadership commitmentChapters include: Data Management Data Handling Ethics Data Governance Data Architecture Data Modeling and Design Data Storage and Operations Data Security Data Integration & Interoperability Document and Content Management Reference and Master Data Data Warehousing and Business Intelligence Metadata Management Data Quality Management Big Data and Data Science Data Management Maturity Assessment Data Management Organization and Role Expectations Data Management and Organizational Change ManagementStandardization of data management disciplines will help data management professionals perform more effectively and consistently. It will also enable organizational leaders to recognize the value and contributions of data management activities.