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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.

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.

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.

Il Data Management Body of Knowledge (DAMA-DMBOK2) presenta una vista complessiva delle sfide, complessità e valore di un'efficace gestione dei dati. Le organizzazioni odierne riconoscono che la gestione dei dati è fondamentale per il loro successo. Riconoscono il valore dei loro dati e cercano di sfruttare tale valore. Con l'aumento della nostra capacità di creare e sfruttare i dati, aumenta anche la necessità di pratiche affidabili di gestione dei dati.La seconda edizione della Guida di DAMA International al Data Management Body of Knowledge (DAMA-DMBOK2) aggiorna e accresce il DMBOK1, che ha avuto grande successo. Libro di riferimento accessibile e autorevole scritto da pensatori leader del settore e ampiamente recensito dai membri DAMA, il DMBOK2 riunisce materiali che descrivono in modo esaustivo le sfide del data management e come affrontarle: Definendo una serie di principi guida per il data management e descrivendo come questi principi possono essere applicati all'interno delle aree funzionali del data management. Fornendo un framework funzionale per l'implementazione dell'enterprise data management, includendo pratiche ampiamente adottate, metodi e tecniche, funzioni, ruoli deliverable e metriche. Stabilendo un vocabolario comune per i concetti del data management e fornendo il fondamento delle best practice per i data management professional.

<strong>Creating a precise diagram of business terms within your projects is a simple yet powerful communication tool for project managers, data governance professionals, and business analysts.</strong><br><br><br>With more and more data being created and used, combined with intense competition, strict regulations, and rapid-spread social media, the financial, liability, and credibility stakes have never been higher and therefore the need for a Common Business Language has never been greater. Appreciate the power of the BTM and apply the steps to build a BTM over the book's five chapters:<ol><li>Challenges. Explore how a Common Business Language is more important than ever with technologies like the Cloud and NoSQL, and Regulations such as the GDPR.</li><li>Needs. Identify scope and plan precise, minimal visuals that will capture the Common Business Language. </li><li>Solution. Meet the BTM and its components, along with the variations of relational and dimensional BTMs. Experience how several data modeling tools display the BTM, including CaseTalk, ER/Studio, erwin DM, and Hackolade.</li><li>Construction. Build operational (relational) and analytics (dimensional) BTMs for a bakery chain.</li><li>Practice. Reinforce BTM concepts and build BTMs for two of your own initiatives alongside a real example. </li></ol>

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.

Data governance-programma's richten zich op het uitoefenen van gezag en op verantwoordelijkheid voor het managen van data als waardevol bedrijfsmiddel. Data governance hoeft niet om controle en beheersing te draaien, maar kan soms invasief of bedreigend zijn voor het werk, de medewerkers en de cultuur van een organisatie. Non-Invasive Data GovernanceTM richt zich op formalisering van bestaande verantwoordelijkheden voor het managen van data en verbetering van de formele communicatie, beveiliging en kwaliteitsinspanningen via effectief stewardschap van databronnen.Scamander - The Data Liberation Company ® - heeft dit boek in het Nederlands vertaald, omdat wij geloven dat de enige succesvolle benadering een non-invasive benadering is.Non-Invasive Data Governance biedt u een complete set tools om een succesvol data-governance-programma op te zetten. Ontdek hoe: Verantwoordelijkheden en taken als steward niet als extra werk aan medewerkers hoeven te worden opgedragen of aangereikt, maar kunnen worden benoemd, herkend en ingezet in overeenstemming met hun bestaande verantwoordelijkheid. Governance van informatie niet als een nieuw proces of nieuwe methode hoeft te worden geïntroduceerd of benadrukt, maar kan worden geïntegreerd in bestaand beleid, reguliere werkprocedures, werkwijzen en methodieken. Governance van informatie geen kwestie hoeft te zijn van inconsequente discipline opgelegd aan activiteiten als data-integratie, risicomanagement, business intelligence en master data management, maar deze juist kan ondersteunen. Een praktische en niet-bedreigende benadering kan worden toegepast op de governance van informatie en het stimuleren van datastewardschap als een organisatiebreed asset. Best practices en kernconcepten van deze niet-bedreigende benadering effectief kunnen worden gecommuniceerd, zodat van sterke punten kan worden geprofiteerd en verbeterpunten worden aangepakt.

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.

As data holdings get bigger and questions get harder, data scientists and analysts must focus on the systems, the tools and techniques, and the disciplined process to get the correct answer, quickly! Whether you work within industry or government, this book will provide you with a foundation to successfully and confidently process large amounts of quantitative data.Here are just a dozen of the many questions answered within these pages: What does quantitative analysis of a system really mean? What is a system? What are big data and analystics? How do you know your numbers are good? What will the future data science environment look like? How do you determine data provenance? How do you gather and process information, and then organize, store, and synthesize it? How does an organization implement data analytics? Do you really need to think like a Chief Information Officer? What is the best way to protect data? What makes a good dashboard? What is the relationship between eating ice cream and getting attacked by a shark?The nine chapters in this book are arranged in three parts that address systems concepts in general, tools and techniques, and future trend topics. Systems concepts include contrasting open and closed systems, performing data mining and big data analysis, and gauging data quality. Tools and techniques include analyzing both continuous and discrete data, applying probability basics, and practicing quantitative analysis such as descriptive and inferential statistics. Future trends include leveraging the Internet of Everything, modeling Artificial Intelligence, and establishing a Data Analytics Support Office (DASO).Many examples are included that were generated using common software, such as Excel, Minitab, Tableau, SAS, and Crystal Ball. While words are good, examples can sometimes be a better teaching tool. For each example included, data files can be found on the companion website. Many of the data sets are tied to the global economy because they use data from shipping ports, air freight hubs, largest cities, and soccer teams. The appendices contain more detailed analysis including the 10 T's for Data Mining, Million Row Data Audit (MRDA) Processes, Analysis of Rainfall, and Simulation Models for Evaluating Traffic Flow.

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.

Understand the language and vocabulary of Data Architecture. The Data Architecture field is rife with terms that have become "fashionable". Some of the terms began with very specific, specialized, meanings - but as their use spread, they lost the precision of their technical definitions and become, well, "buzzwords".A buzzword is "a word or expression from a particular subject area that has become fashionable because it has been used a lot". Compliance is "the obeying of an accepted principle or instruction that states the way things are or should be done."The assignment is to take buzzwords and follow rules to use them correctly. We cut through the hype to arrive at buzzword compliance - the state where you fully understand the words that in fact have real meaning in the data architecture industry. This book will rationalize the various ways all these terms are defined.Of necessity, the book must address all aspects of describing an enterprise and its data management technologies. This includes a wide range of subjects, from entity/relationship modeling, through the semantic web, to database issues like relational and "beyond relational" ("NoSQL") approaches. In each case, the definitions for the subject are meant to be detailed enough to make it possible to understand basic principles-while recognizing that a full understanding will require consulting the sources where they are more completely described.The book's Glossary contains a catalogue of definitions and its Bibliography contains a comprehensive set of references.

Increase the awareness of your customer's behavior to survive and excel within your industry.One hundred years ago, the voice of the customer was easily and routinely heard by the shopkeeper. In small towns, the shopkeeper knew everyone. Today's world has gotten much bigger and much more complex. No longer does the store owner personally know everyone who comes into the store. Yet there are three important abilities technologies offer that make it possible to listen to the voice of the customer today: The ability to acquire, store, and manage huge amounts of data The ability to read and understand text in a computerized environment The ability to visualize dataThis book answers important questions such as: Where is the voice of the customer heard? How does the corporation find and capture the voice of the customer? How is the voice of the customer actually interpreted and understood? How do you cope with the volume of messages the customer is sending you? How do you separate noise from the important messages? How do you analyze the composite voice of the customer over thousands of customers? How do you reduce the voice of the customer to a visual format that is understood by management? How do you know when the message the customer is sending changes?After reading this book the reader will be able to manage, build, and operate a corporate infrastructure that listens to the voice of the customer.

Controlling the Chaos uses a deceptively simple "functional framework" approach that builds upon the groundwork laid down by Zachman, NIST, Spewack, TOGAF, COBIT, and ITIL, to create a vision of IT management that is easy to grasp and implement. Using this framework, the mind-numbing array of functionality that IT manages on behalf of the business is organized into a few simple, intuitive lifecycles. The framework then paints a clear, compelling picture of how to organize both your enterprise architecture and your IT governance efforts for all of these IT functions into a consistent, comprehensive program that is easy to understand and manage.This framework treats architecture and governance as two sides of the same coin: managing your future vision and the roadmap to get there (architecture) and managing your day-to-day operations using policies, standards, processes and roles (governance). This novel approach combines the holistic view of IT functions with the structures to manage each function's architecture and governance in a holistic program.After describing the functional framework concept, the text brings life to the structure, drawing practical insights from the author's 30 years of architecture and governance experience across several industries. The examples illustrate the difference between managing IT functions in isolation and managing them as part of an integrated enterprise solution, frankly admitting where and why architecture and governance programs are failing to provide their promised value. The book admits that the root causes of these failures are sometimes found in poor processes, but are just as often the result of poor staffing and office politics, all of which must be addressed to be successful.From a governance standpoint, the framework paints a picture of IT governance which brings a clarity and vision that has been sadly lacking in most industry discussions. It is an imminently practical approach to building a governance program that allows any IT organization to manage their infrastructure simply and consistently using a mature, time-tested approach. Using this framework will elevate your governance from vaguely defined departmental efforts to a coordinated, easily understood, easily managed enterprise-level program that simplifies the management of IT infrastructure while keeping it focused on supporting the business and returning value.From an architectural standpoint, the framework squarely faces challenges such as complying with emerging security standards, the increasing role of data analytics, and the virtualization of hardware and software into the cloud - discussing how and when to begin preparing for the disruptive technology of the near future. And the book addresses the frequent failure of enterprise architecture programs to provide business value, discussing the simple changes that must be made to transform these money-pits into valuable business partners.This is not a training manual for managing individual IT functions. Rather, it is a vision for how to manage all of these functions as parts of a single, integrated, comprehensive enterprise architecture and governance program that is focused on providing value to the business. This book is intended for both IT and business professionals, from executives to front-line technicians, who are trying to control their IT infrastructure.

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.

Master the concepts and strategies underlying success and progress in data science.From the author of the bestsellers, Data Scientist and Julia for Data Science, this book covers four foundational areas of data science. The first area is the data science pipeline including methodologies and the data scientist's toolbox. The second are essential practices needed in understanding the data including questions and hypotheses. The third are pitfalls to avoid in the data science process. The fourth is an awareness of future trends and how modern technologies like Artificial Intelligence (AI) fit into the data science framework.The following chapters cover these four foundational areas: Chapter 1 - What Is Data Science? Chapter 2 - The Data Science Pipeline Chapter 3 - Data Science Methodologies Chapter 4 - The Data Scientist's Toolbox Chapter 5 - Questions to Ask and the Hypotheses They Are Based On Chapter 6 - Data Science Experiments and Evaluation of Their Results Chapter 7 - Sensitivity Analysis of Experiment Conclusions Chapter 8 - Programming Bugs Chapter 9 - Mistakes Through the Data Science Process Chapter 10 - Dealing with Bugs and Mistakes Effectively and Efficiently Chapter 11 - The Role of Heuristics in Data Science Chapter 12 - The Role of AI in Data Science Chapter 13 - Data Science Ethics Chapter 14 - Future Trends and How to Remain RelevantTargeted towards data science learners of all levels, this book aims to help the reader go beyond data science techniques and obtain a more holistic and deeper understanding of what data science entails. With a focus on the problems data science tries to solve, this book challenges the reader to become a self-sufficient player in the field.