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<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>

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

What we think, we become. Proverb. For decades, leadership was reserved for a select few that others thought had been gifted with innate talent and the exclusive right to lead. Today, we know better. Everyone leads in their personal life, their workplaces, and in their communities. Every person has the potential to be a better leader, to positively influence others, and to make a difference in our world. LeadAbility examines the realities of leading in our modern world. Discover the Six Leadership Choices and their LeadAbilities that you can develop to become A Better Leader at home and at work. So, the question has changed. We are no longer asking Are you a leader? but rather What kind of leader will you be? The choice is yours.

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.

Shift from application-centric to data-centric to enable your organization to develop more efficient and successful Enterprise Information Systems.This book is the first part of a trilogy to follow Software Wasteland. In Software Wasteland, we detailed the current poor state of application software development. We offered some tactical advice for reducing some of the worse of the excess. This is the first book in the "what to do instead" trilogy."Even if the thought of data modeling makes you cringe, Dave McComb's latest book makes the case that it is a necessary exercise for the data-driven organization. The 'Data-Centric Revolution' shows how to be data-driven in an extensible, flexible way that is baked-into organizational culture, rather than taking a typical project-by-project approach. The book is a fun, insightful and meaty read, well-illustrated, and with endless wonderful examples." Doug Laney, Principal, Data & Analytics Strategy, Caserta, and author of the best-seller, "Infonomics: How to Monetize, Manage, and Measure Information for Competitive Advantage"

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.

In our distant past, we attempted to create wealth by turning everyday substances into gold. This was early alchemy, and ultimately it did not work. But the world has changed. Today we have a type of modern alchemy that really can create gold. We can transform voluminous text into a wealth of knowledge. Text is a common fabric of society, yet it is still challenging for our technology to make sense of text. This is where taxonomies can help. In this book, legendary Bill Inmon will introduce you to the concept of taxonomies and how they are used to simplify and understand text. We emphasise the practical aspects of taxonomies, and the subsequent usage of taxonomies as a basis for textual analytics. This book is for managers who have to deal with text, students of computer science, programmers who need to understand taxonomies, systems analysts who hope to draw business value out of a body of text, and especially those who are struggling to decode data lakes. Hopefully for those individuals (and many more), this book will serve as both an introduction to taxonomies and a guide to how taxonomies can be used to bring text into the realm of corporate decision-making. This book will introduce you to the world of taxonomies, as well as explore: Simple and complex taxonomies; Ontologies; Obtaining taxonomies; Changing taxonomies; Taxonomies and data models; Types of textual data; Textual analytics. In addition, several case studies are presented from industries as diverse as banking, call centres, and travel.