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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.

The data lakehouse is the next generation of the data warehouse and data lake, designed to meet today's complex and ever-changing analytics, machine learning, and data science requirements.Learn about the features and architecture of the data lakehouse, along with its powerful analytical infrastructure. Appreciate how the universal common connector blends structured, textual, analog, and IoT data. Maintain the lakehouse for future generations through Data Lakehouse Housekeeping and Data Future-proofing. Know how to incorporate the lakehouse into an existing data governance strategy. Incorporate data catalogs, data lineage tools, and open source software into your architecture to ensure your data scientists, analysts, and end users live happily ever after.

Master the most agile and resilient design for building analytics applications: the Unified Star Schema (USS) approach. The USS has many benefits over traditional dimensional modeling. Witness the power of the USS as a single star schema that serves as a foundation for all present and future business requirements of your organization.Data warehouse legend Bill Inmon and business intelligence innovator, Francesco Puppini, explain step-by-step why the Unified Star Schema is the recommended approach for business intelligence designs today, and show through many examples how to build and use this new solution. This book contains two parts. Part I, Architecture, explains the benefits of data marts and data warehouses, covering how organizations progressed to their current state of analytics, and to the challenges that result from current business intelligence architectures. Chapter 1 covers the drivers behind and the characteristics of the data warehouse and data mart. Chapter 2 introduces dimensional modeling concepts, including fact tables, dimensions, star joins, and snowflakes. Chapter 3 recalls the evolution of the data mart. Chapter 4 explains Extract, Transform, and Load (ETL), and the value ETL brings to reporting. Chapter 5 explores the Integrated Data Mart Approach, and Chapter 6 explains how to monitor this environment. Chapter 7 describes the different types of metadata within the data warehouse environment. Chapter 8 progresses through the evolution to our current modern data warehouse environment.Part II, the Unified Star Schema, covers the Unified Star Schema (USS) approach and how it solves the challenges introduced in Part I. There are eight chapters within Part II:Chapter 9, Introduction to the Unified Star Schema: Learn about its architecture and use cases, as well as how the USS approach differs from the traditional approach. Chapter 10, Loss of Data: Learn about the loss of data and the USS Bridge. Understand that the USS approach does not create any join, and for this reason, it has no loss of data. Chapter 11, The Fan Trap: Get introduced to the Oriented Data Model convention, and learn the dangers of a fan trap through an example. Differentiate join and association, and realize that an "in-memory association" is the preferred solution to the fan trap. Chapter 12, The Chasm Trap: Become familiar with the Cartesian product, and then follow along with an example based on LinkedIn, which illustrates that a chasm trap produces unwanted duplicates. See that the USS Bridge is based on a union, which does not create any duplicates. Chapter 13, Multi-Fact Queries: Distinguish between multiple facts "with direct connection" versus multiple facts "with no direct connection". Explore how BI tools are capable of building aggregated virtual rows.Chapter 14, Loops: Learn more about loops and five traditional techniques to solve them. Follow along with an implementation, which will illustrate the solution based on the USS approach.Chapter 15, Non-Conformed Granularities: Learn about non-conformed granularities, and learn that the Unified Star Schema introduces a solution called "re-normalization". Chapter 16, Northwind Case Study. Witness how easy it is to detect the pitfalls of Northwind using the ODM convention. Follow along with an implementation of the USS approach on the Northwind database with various BI tools. 

If you are leading an organization or if you need to communicate with leaders about data management, Navigating the Labyrinth is your guide. Organizations that want to get value from their data need to manage that data well. But to most executives, data management seems obscure, complicated, and highly technical. You don't have time to learn all the detail or cut through the hype. Navigating the Labyrinth helps you get there. Based on best practices from DAMA's Data Management Body of Knowledge (DMBOK2), it explains the fundamentals and says why they are important. It focuses your attention on what you need to know to help your organization build trust in and get value out of its data.

Imagine a group of prehistoric hunters armed with stone-tipped spears. Their primitive weapons made hunting large animals, such as mammoths, dangerous work. Over time, however, a new breed of hunters developed. They would stretch the skin of a previously killed mammoth on the wall and throw their spears, while observing which spear, thrown from which angle and distance, penetrated the skin the best. The data gathered helped them make better spears and develop better hunting strategies. Quality data is the key to any advancement, whether it is from the Stone Age to the Bronze Age. Or from the Information Age to whatever Age comes next. The success of corporations and government institutions largely depends on the efficiency with which they can collect, organise, and utilise data about products, customers, competitors, and employees. Fortunately, improving your data quality does not have to be such a mammoth task. This book is a must read for anyone who needs to understand, correct, or prevent data quality issues in their organisation. Skipping theory and focusing purely on what is practical and what works, this text contains a proven approach to identifying, warehousing, and analysing data errors. Master techniques in data profiling and gathering metadata, designing data quality rules, organising rule and error catalogues, and constructing the dimensional data quality scorecard. David Wells, Director of Education of the Data Warehousing Institute, says "This is one of those books that marks a milestone in the evolution of a discipline. Arkady''s insights and techniques fuel the transition of data quality management from art to science -- from crafting to engineering. From deep experience, with thoughtful structure, and with engaging style Arkady brings the discipline of data quality to practitioners."

Build a Business Architecture Framework to enable your organization to grow and gracefully accommodate change. The practical approach described in this book can help you as a business architect, analyst, or manager, create reusable, adaptable, and manageable knowledge of your organization. Apply the full lifecycle from business strategy through implementation, and identify the required knowledge domains. Convert business strategy into usable and effective business designs which optimize investment decisions. Articulate what domain knowledge (the dots) needs to be collected, how these are connected, and which combinations provide the greatest opportunity if corrected. The book covers the main business architecture stages of 'Define the Business', 'Design the Business', 'Build the Business', and 'Operate the Business'. Build models of the external ecosystem, business stakeholders, business information, business processes, business capabilities, change prioritization, and performance management systems to support your change journey.This book is an essential companion guide for new business architects and analysts, and a valuable reference for experienced architects to enhance their practice.

Si vous êtes à la tête d'une organisation ou si vous devez communiquer avec des dirigeants au sujet de la gestion des données, S'orienter dans le labyrinthe est votre guide. Les organisations qui veulent tirer de la valeur de leurs données doivent bien les gérer. Mais pour la plupart des cadres, la gestion des données semble obscure, compliquée et très technique. Vous n'avez pas le temps d'apprendre tous les détails ou d'aller au-delà du battage médiatique. S'orienter dans le labyrinthe vous aide à y parvenir. Basé sur les meilleures pratiques du Data Management Body of Knowledge (DMBOK2) de DAMA, il explique les principes fondamentaux et explique pourquoi ils sont importants. Il attire votre attention sur ce que vous devez savoir pour aider votre organisation à faire confiance à ses données et à en tirer de la valeur.À propos de DAMADAMA International est une association à but non lucratif, indépendante des fournisseurs, composée de professionnels techniques et métiers qui se consacrent à l'avancement des concepts et pratiques liés à la gestion des données et des informations au soutien de la stratégie commerciale. Avec des chapitres dans le monde entier, DAMA International encourage les meilleures pratiques à travers un réseau d'individus et d'organisations connectés qui partagent des idées, des tendances, des problèmes et des solutions et qui considèrent DAMA comme une ressource centrale fiable et collaborative pour tout ce qui concerne les données. Visitez dama.org pour en savoir plus.

Do you know what a "PIDD" is? A PIDD is a Perpetual Involuntary Data Doner. Surveillance capitalists love PIDDs because PIDDs materially support the data extraction industry by unwittingly surrendering their personal information, paying for data transport and storage, and tolerating poor Internet/technology performance. Today, this quiet industry collects massive data about people to modify and control their societal behavior. Surveillance capitalists control behavior by exploiting people's low data literacy. Three things increase the magnitude of the challenge: Data volume continues faster than we can process. Poor data interchange costs drain citizen and organizational resources and productivity. Society's reliance on technologies has not materially addressed the gap.Our Digital Civics Framework (DCF) presents a guide to increasing the data literacy of billions of citizens or at least those connected to the internet. We outline the levels and types of data knowledge that society needs and propose exercises that will help citizens interact productively within a data-driven society. Unfortunately, far too many PIDDs allow surveillance capitalists to monitor their data. This type of monitoring comes at an expense to individuals, our communities, and society writ large. Completing this material will equip readers with a shared understanding of society and the role data plays in it.

Implement and perform data governance on any scale using the book's flexible and proven data governance framework. This book is a culmination of decades of experience and trial and error implementations of data governance. There are many different ways to "do" data governance, so this is a consolidated framework that has been researched, developed, honed, piloted, and implemented at many different sizes of enterprises across many industries. Mold the book's flexible framework to jumpstart your data governance program. This book is a reference guide for Data Owners, Data Stewards, Data Custodians, Data Analysts, Application Developers, Database Administrators, Data Producers, and Data Consumers. Use it as a resource when you need to answer questions about data governance. It will guide you to successfully manage the creation, transformation, and usage of data owned by or in the care of your enterprise.

Do you fully understand your business? Do you fully understand the competition in the business niche where you operate? Do you fully understand the dynamic nature of society today, with the current pandemic, raging wildfires, violent storms, changing economy and politics, and climate change? Is your business operation as effective and efficient as it could be? Is your business agile enough to adapt to constant change, be successful, and survive?Most public and private sector organizations are not. They face a huge and increasing disparity in the understanding and operation of their business. That disparity results in decreased business understanding, increased uncertainty, decreased agility, and the increased possibility of less-than-successful business operation. Many organizations are going out of business, or suffering limited business because of that disparity.An organization's business understanding and operation is contained in its data resource and processes resources. However, those two resources are typically managed separately, by a variety of different organization units, outside of any formal processes managing those resources. The result is the rampant disparity seen today.Business Understanding and Agility takes a fresh look at how an organization can achieve and maintain a thorough business understanding, remain agile, and be successful. The integrated management of all data and processes, within a single organization-wide data architecture and a single organization-wide processes architecture, provides a base for thorough business understanding, agility, and success. Formally managing data and processes as a critical resource of the organization is the only way an organization can thoroughly understand its business, remain agile, and be successful.The book presents a new paradigm with an overarching architecture construct and an underlying foundation for business understanding and agility. The paradigm consists of data and processes resource management supported by a data and processes resource culture. The paradigm emphasizes computer support of routine tasks, leaving the real hard-thinking, decision-making tasks to people.Mr. Brackett brings together the knowledge and experience from his long professional venture into a capstone book about the formal management of a combined data and processes resource as the critical resource of an organization.

"To teach the reader how to make the data we interact with more useful than it was before we were involved"--

Learn the secrets required to advance the technology industry beyond digital transformation.Today''s technology industry is in its growth phase, on its journey to maturity. How quickly can the industry accelerate to be ready to take on the challenges of the next industrial revolution? What are the key patterns, characteristics, and methods that can allow for the accelerated growth and increased market share of a technology business?Looking at foundational lessons from the past through the lens of more established and mature industries, businesses, technologists, and practitioners will gather insights into how organizations can make significant advancements and expand their market share. The book reveals patterns and characteristics that can help businesses succeed. Start with an idea and transform it into a compelling product-what methods can help you deliver faster, attract new customers, and retain their loyalty? Forming a value-driven culture where your people are central to ever-improving cycles of change requires adopting the well-documented approaches described in this book.The author, Nana Fifield, a senior technology leader, has spent nearly three decades working with many technology organizations, dedicating significant time to researching the methods that successful and mature industries have adapted to continue thriving, evolving, and innovating over centuries. 

"When the future knocks on the door, it never walks away."Market and Competitive Intelligence (MCI) is the process of enhancing marketplace competitiveness through a greater understanding of a firm''s competitive environment as well as leveraging the capabilities of preparedness for the future.What if you could:Spend more time creating forward-looking actionable intelligence, thanks to smarter tools?Collaborate better and develop hypotheses that can be tested and validated by data analysis?Become more in line with future market expectations as predictions are more accurate?Use human analysis to put information into context, ask relevant questions, and create the best algorithms for machines?"This book digs deep into the challenges and opportunities of how to succeed with technology platforms for market and competitive intelligence."The authors draw on their extensive experience and real-life cases to present their "Garden of Intelligence" framework. These insights will make your organization healthier and more fit to reap the benefits of opportunities and handle disruptions in the future."With this book, we aim to bring intelligence into the future and the future into intelligence."

What value does data modeling contribute to an organization, and how can that value be increased? Starting with analogies from Native American storytelling traditions and drawing on his experiences as an officer in Toastmasters (the International public speaking organization), Larry shows us how we can be data modeling Shamans. We can use data models to bring people together to solve business problems, reengineer inefficient business processes, forge agreements on data meanings, create new business opportunities, and promote data quality and reuse.Drawing from a host of disciplines, from storytelling to cognitive behavioral therapy, from landscaping to Human-Centered Design, from business process reengineering to domain-driven development, from Agile to object-oriented design, Larry weaves all these threads together into a compelling narrative of using data to get us successfully from where we are now to where we want to be!If you're a data professional, the bad news is that change is inevitable. But the good news is that change is not only possible, but fun! Larry's book shows how to navigate the shoals of business and technology change, from Agile development to NoSQL databases to Domain-Driven Development to microservices to cloud computing. The Journey starts here.

E. F. Codd''s relational model of data has been described as one of the three greatest inventions of all time (the other two being agriculture and the scientific method), and his receipt of the 1981 ACM Turing Award, the top award in computer science, for inventing it was thoroughly deserved. The papers in which Codd first described his model were staggering in their originality; they had, and continue to have, a huge impact on just about every aspect of the way we do business in the world today. And yet few people, even in the professional database community, are truly familiar with those papers. This book-a thorough overhaul and rewrite of an earlier book by the same name-is an attempt to remedy this sorry state of affairs. In it, well known author C. J. Date provides a detailed examination of all of Codd''s major database publications, explaining the nature of his contribution in depth, and in particular highlighting not only the many things he got right but also some of the things he got wrong. Database theory and practice have evolved considerably since Codd first defined his relational model, back in 1969. This book draws on decades of experience to present the most up to date treatment of the material possible. Anyone with a professional interest in databases can benefit fr

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.

"Fifty years of relational. It's hard to believe the relational model has been around now for over half a century! But it has-it was born on August 19th, 1969, when Codd's first database paper was published. And Chris Date has been involved with it for almost the whole of that time, working closely with Codd for many years and publishing the very first, and definitive, book on the subject in 1975. In this book's title essay, Chris offers his own unique perspective (two chapters) on those fifty years. No database professional can afford to miss this one of a kind history. But there's more to this book than just a little personal history. Another unique feature is an extensive and in depth discussion (nine chapters) of a variety of frequently asked questions on relational matters, covering such topics as mathematics and the relational model; relational algebra; predicates; relation valued attributes; keys and normalization; missing information; and the SQL language. Another part of the book offers detailed responses to critics (four chapters). Finally, the book also contains the text of several recent interviews with Chris Date, covering such matters as RM/V2, XML, NoSQL, The Third Manifesto, and how SQL came to dominate the database landscape"--

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"