Author: Dave Knifton

Publisher: Paragon Publishing

ISBN: 1782223266

Category: Computers

Page: 318

View: 6293

Are you looking to make better use of data captured within your organisation or want to learn more about how Data Architecture can transform your operations? Answering these questions is at the very heart of Navigating the Data Architecture Landscape. By reading this book you will learn how to: Introduce or improve the Data Architecture function of your organisation Enhance your skills in this domain to deliver more from your data. You may be wondering how a book can do this if it knows nothing about where you are now, or where you want to be? It can, because by leveraging its principles you will discover how to create optimised potential routes to achieve your own Data Architectural objectives. Basic building blocks, concepts and models are defined, enabling you to create new or adapt existing frameworks appropriate for any data landscape. Practical tips and suggestions are also detailed throughout, helping you gain immediate improvements from the way you work and enhance the benefits your organisation can derive from its data. So if you are a Data Architect or deal with data in your organisation and want to learn how to transform the positive yield from its data, then this book is a must read for you! “David has been there and dealt with the issues, which is why this book is an outstanding resource for Data Architects and indeed anyone dealing with the serious challenges of an enterprise data landscape.” – Richard Rendell, Technical Services Director, AgeSmart “An essential read for anyone wishing to practically achieve more benefit from data for their organisation within today’s constraints.” – Reem Zahran - Director, Offering Development, IMS Health “This book provides a comprehensive set of tools enabling you to improve the business outcomes from your organisation’s use of data.” – Andrew Rowland, Global Head Database Engineering, UBS This book is an essential read for Data Architects or indeed anyone wanting to improve the benefit that their organisation can derive from its data usage. It does this by providing principles and models that are appropriate to use within any framework, or even the absence of one. The book is designed to be practical and contains many tips and suggestions as well as examples that can be used as the basis for the reader's own Data Architectural definitions. The breadth of the book covers contemporary themes for Data Architecture and the chapters include; Data Modelling, Enterprise Data Models, Data Governance, Master Data Management and Big Data
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Simple Skills To Model The Real World

Author: Dave Knifton

Publisher: Paragon Publishing

ISBN: 1782224734

Category: Computers

Page: 348

View: 8673

Adopting the latest technological and data related innovations has caused many organisations to realise they don’t have a firm grasp on their basic operational data. This is a problem that Logical Data Models are uniquely qualified to help them solve. The realisation of the need to define a Logical Data Model may be driven by any number of reasons including; trying to link Big Data Analytics to operational data, plunging into Digital Marketing, choosing the best SaaS solution, carrying out a core Data Migration, developing a Data Warehouse, enhancing Data Governance processes, or even just trying to get everyone to agree on their Product specifications! This book will provide you with the skills required to start to answer these and many similar types of questions. It is not written with a focus on IT development, so you don’t need a technical background to get the most from it. But for any professional working in an organisation’s data landscape, this book will provide the skills they need to define high quality and beneficial data models quickly and easily. It does this using a wealth of practical examples, tips and techniques, as well as providing checklists and templates. It is structured into three parts: The Foundations: What are the solid foundations necessary for building effective data models? The Tools: What Tools are required to enable you to specify clear, precise and accurate data model definitions? The Deliverables: What processes will you need to successfully define the models, what will they deliver, and how can we make them beneficial to the organisation? “In this data-rich era, it is even more critical for organisations to answer the question of what their data means and the value it can bring. Those who can, will gain a competitive advantage through their use of data to streamline their operations and energise their strategies. Core to revealing this meaning, is the data model that is now, more than ever, the lynchpin of success. The Data Model Toolkit provides the essential knowledge and skills that will ensure this success.” – Reem Zahran, Global IT Platform Director, TNS “We work with many enterprise customers to help them transform their technology and it always starts with data. The key is a clear definition of their data quality, completeness and governance. This book shows you step by step how to define and use Data Models as powerful tools to define an organisation’s data and maximise its business benefit.” – John Casserly, CEO, Xceed Group
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From Zen to Reality

Author: Charles Tupper

Publisher: Elsevier

ISBN: 9780123851277

Category: Computers

Page: 448

View: 5802

Data Architecture: From Zen to Reality explains the principles underlying data architecture, how data evolves with organizations, and the challenges organizations face in structuring and managing their data. Using a holistic approach to the field of data architecture, the book describes proven methods and technologies to solve the complex issues dealing with data. It covers the various applied areas of data, including data modelling and data model management, data quality, data governance, enterprise information management, database design, data warehousing, and warehouse design. This text is a core resource for anyone customizing or aligning data management systems, taking the Zen-like idea of data architecture to an attainable reality. The book presents fundamental concepts of enterprise architecture with definitions and real-world applications and scenarios. It teaches data managers and planners about the challenges of building a data architecture roadmap, structuring the right team, and building a long term set of solutions. It includes the detail needed to illustrate how the fundamental principles are used in current business practice. The book is divided into five sections, one of which addresses the software-application development process, defining tools, techniques, and methods that ensure repeatable results. Data Architecture is intended for people in business management involved with corporate data issues and information technology decisions, ranging from data architects to IT consultants, IT auditors, and data administrators. It is also an ideal reference tool for those in a higher-level education process involved in data or information technology management. Presents fundamental concepts of enterprise architecture with definitions and real-world applications and scenarios Teaches data managers and planners about the challenges of building a data architecture roadmap, structuring the right team, and building a long term set of solutions Includes the detail needed to illustrate how the fundamental principles are used in current business practice
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Author: Andy Graham

Publisher: Koios Associates Limited

ISBN: 9780956582911

Category: Computers

Page: 160

View: 3305

Wouldn't it be great to understand all the data in your organisation? Just imagine being able to define, agree and manage information concepts that impact on business strategy? Then image that these information concepts can be linked to the physical database attributes that ultimately are used to create them. That's what this book is about. It focuses on the data model as the foundation for achieving this understanding. This book provides a framework for the enterprise data model, the business reasons behind it and the differences between conceptual, logical and physical data models. The question of how, and why, to use a data model artifact as part of the data governance toolkit for the whole enterprise is also addressed. This publication is not an in-depth manual on how to model data for a new database system or your next design project. It instead focuses at a level above these implementation projects and addresses the issues that organisations typical struggling with such as: * How do we provide a framework within which we can manage our data assets? * How do we develop applications that adhere to a set of data standards; without creating a nightmare of administration and governance that is both unwieldy and unusable? * How can we get business value from our enterprise data? Chapter headings are: * Chapter 1 - Introduction * Chapter 2 - Information and Data * Chapter 3 - Pillars of Value * Chapter 4 - An Overview of Data Modelling * Chapter 5 - Data Architecture * Chapter 6 - The Enterprise Data Model * Chapter 7 - Build the Model one Project at a Time * Chapter 8 - Master Data * Chapter 9 - Data Governance * Chapter 10 - The Enterprise Data Framework This 2nd edition revises the original text to add extra details around key areas such as the enterprise data model framework and the pillars of value. It also improves the quality of the original text.
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Simple Skills To Model The Real World

Author: Dave Knifton

Publisher: Paragon Publishing

ISBN: 1782224734

Category: Computers

Page: 348

View: 8430

Adopting the latest technological and data related innovations has caused many organisations to realise they don’t have a firm grasp on their basic operational data. This is a problem that Logical Data Models are uniquely qualified to help them solve. The realisation of the need to define a Logical Data Model may be driven by any number of reasons including; trying to link Big Data Analytics to operational data, plunging into Digital Marketing, choosing the best SaaS solution, carrying out a core Data Migration, developing a Data Warehouse, enhancing Data Governance processes, or even just trying to get everyone to agree on their Product specifications! This book will provide you with the skills required to start to answer these and many similar types of questions. It is not written with a focus on IT development, so you don’t need a technical background to get the most from it. But for any professional working in an organisation’s data landscape, this book will provide the skills they need to define high quality and beneficial data models quickly and easily. It does this using a wealth of practical examples, tips and techniques, as well as providing checklists and templates. It is structured into three parts: The Foundations: What are the solid foundations necessary for building effective data models? The Tools: What Tools are required to enable you to specify clear, precise and accurate data model definitions? The Deliverables: What processes will you need to successfully define the models, what will they deliver, and how can we make them beneficial to the organisation? “In this data-rich era, it is even more critical for organisations to answer the question of what their data means and the value it can bring. Those who can, will gain a competitive advantage through their use of data to streamline their operations and energise their strategies. Core to revealing this meaning, is the data model that is now, more than ever, the lynchpin of success. The Data Model Toolkit provides the essential knowledge and skills that will ensure this success.” – Reem Zahran, Global IT Platform Director, TNS “We work with many enterprise customers to help them transform their technology and it always starts with data. The key is a clear definition of their data quality, completeness and governance. This book shows you step by step how to define and use Data Models as powerful tools to define an organisation’s data and maximise its business benefit.” – John Casserly, CEO, Xceed Group
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Author: Dave Knifton

Publisher: Paragon Publishing

ISBN: 1782223800

Category: Computers

Page: 182

View: 9350

How exactly do you start to design a system with using Oracle Database technology? This book is the first in a series that answers just this question. If you are a Developer just starting, or even with several years of Oracle experience, this book will cut through the myriad of alternatives, to provide you with a practical and effective development approach. It explains the design basis of Oracle system development in a way that is not only relevant for Developers, but also extremely beneficial to System Designers, Architects, Development Managers and Project Managers. Simple explanations will guide you through the creation of a Foundation Layer that provides a solid basis for system delivery. This Layer will deliver significant gains in agility and development productivity, whilst slashing maintenance costs. The design features of Oracle Views, Materialized Views, Partitioning and Virtual Private Database are revealed, enabling you to deliver enhanced real system outcomes. The book is structured into two parts: A Theory Part describes the design considerations that underpin the best Oracle development approaches and allow you to create designs appropriate to your own requirements and constraints. A Practice Part provides Case Studies that take you step by step through how to construct such system Foundations. These worked examples can help you to fast track your own implementations.
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Author: Tomcy John,Pankaj Misra

Publisher: Packt Publishing Ltd

ISBN: 1787282651

Category: Computers

Page: 596

View: 2924

A practical guide to implementing your enterprise data lake using Lambda Architecture as the base About This Book Build a full-fledged data lake for your organization with popular big data technologies using the Lambda architecture as the base Delve into the big data technologies required to meet modern day business strategies A highly practical guide to implementing enterprise data lakes with lots of examples and real-world use-cases Who This Book Is For Java developers and architects who would like to implement a data lake for their enterprise will find this book useful. If you want to get hands-on experience with the Lambda Architecture and big data technologies by implementing a practical solution using these technologies, this book will also help you. What You Will Learn Build an enterprise-level data lake using the relevant big data technologies Understand the core of the Lambda architecture and how to apply it in an enterprise Learn the technical details around Sqoop and its functionalities Integrate Kafka with Hadoop components to acquire enterprise data Use flume with streaming technologies for stream-based processing Understand stream- based processing with reference to Apache Spark Streaming Incorporate Hadoop components and know the advantages they provide for enterprise data lakes Build fast, streaming, and high-performance applications using ElasticSearch Make your data ingestion process consistent across various data formats with configurability Process your data to derive intelligence using machine learning algorithms In Detail The term "Data Lake" has recently emerged as a prominent term in the big data industry. Data scientists can make use of it in deriving meaningful insights that can be used by businesses to redefine or transform the way they operate. Lambda architecture is also emerging as one of the very eminent patterns in the big data landscape, as it not only helps to derive useful information from historical data but also correlates real-time data to enable business to take critical decisions. This book tries to bring these two important aspects — data lake and lambda architecture—together. This book is divided into three main sections. The first introduces you to the concept of data lakes, the importance of data lakes in enterprises, and getting you up-to-speed with the Lambda architecture. The second section delves into the principal components of building a data lake using the Lambda architecture. It introduces you to popular big data technologies such as Apache Hadoop, Spark, Sqoop, Flume, and ElasticSearch. The third section is a highly practical demonstration of putting it all together, and shows you how an enterprise data lake can be implemented, along with several real-world use-cases. It also shows you how other peripheral components can be added to the lake to make it more efficient. By the end of this book, you will be able to choose the right big data technologies using the lambda architectural patterns to build your enterprise data lake. Style and approach The book takes a pragmatic approach, showing ways to leverage big data technologies and lambda architecture to build an enterprise-level data lake.
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A practitioners guide to choosing relevant Big Data architecture

Author: Bahaaldine Azarmi

Publisher: Apress

ISBN: 1484213262

Category: Computers

Page: 141

View: 1039

This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "Big Data", from the usage of No-SQL databases to the deployment of stream analytics architecture, machine learning, and governance. Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications , which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution. When the data processing is too complex and involves different processing topology like long running jobs, stream processing, multiple data sources correlation, and machine learning, it’s often necessary to delegate the load to Hadoop or Spark and use the No-SQL to serve processed data in real time. This book shows you how to choose a relevant combination of big data technologies available within the Hadoop ecosystem. It focuses on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern is illustrated with practical examples, which use the different open sourceprojects such as Logstash, Spark, Kafka, and so on. Traditional data infrastructures are built for digesting and rendering data synthesis and analytics from large amount of data. This book helps you to understand why you should consider using machine learning algorithms early on in the project, before being overwhelmed by constraints imposed by dealing with the high throughput of Big data. Scalable Big Data Architecture is for developers, data architects, and data scientists looking for a better understanding of how to choose the most relevant pattern for a Big Data project and which tools to integrate into that pattern.
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How Your Company Can Win by Embracing Mobile Technologies

Author: Dirk Nicol

Publisher: Pearson Education

ISBN: 013309491X

Category: Business & Economics

Page: 243

View: 8232

Navigate the Mobile Landscape with Confidence and Create a Mobile Strategy That Wins in the Market Place Mobile Strategy gives IT leaders the ability to transform their business by offering all the guidance they need to navigate this complex landscape, leverage its opportunities, and protect their investments along the way. IBM's Dirk Nicol clearly explains key trends and issues across the entire mobile project lifecycle. He offers insights critical to evaluating mobile technologies, supporting BYOD, and integrating mobile, cloud, social, and big data. Throughout, you'll find proven best practices based on real-world case studies from his extensive experience with IBM's enterprise customers. Coverage includes • Understanding the profound implications and challenges of consumerized IT in the mobile space • Uncovering powerful new opportunities to drive value from mobile technology • Transforming “systems of record” to “systems of engagement” that fully reflect context and intelligence • Identifying proven patterns for delivering common mobile capabilities in operations, commerce, collaboration, and marketing • Managing security threats related to lost/stolen devices, insecure Wi-Fi, and built-in cameras • Choosing mobile data protection, security, and management options: wrappers, containers, virtualization, mobile Software Development Kits (SDKs), virtual private networks (VPNs), Mobile Device Management (MDM), Mobile Application Management (MAM), and anti-malware • Handling the “app store” distribution model and managing updates • Using mobile middleware to support multiple platforms and back-end connectivity with less complexity • Building and integrating high-quality mobile apps—and getting useful customer feedback to improve them • Addressing international considerations and emerging markets • Mastering methodologies for successfully and rapidly executing mobile projects • Converging mobile, cloud, social, and big data into a single high-value IT delivery platform
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Big Data, Data Warehouse and Data Vault

Author: W.H. Inmon,Dan Linstedt

Publisher: Morgan Kaufmann

ISBN: 0128020911

Category: Computers

Page: 378

View: 9484

Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can’t be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You’ll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it Shows how to turn textual information into a form that can be analyzed by standard tools. Explains how Big Data fits within an existing systems environment Presents new opportunities that are afforded by the advent of Big Data Demystifies the murky waters of repetitive and non-repetitive data in Big Data
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An SOA Approach to Managing Core Information

Author: Allen Dreibelbis,Eberhard Hechler,Ivan Milman,Martin Oberhofer,Paul van Run,Dan Wolfson

Publisher: Pearson Education

ISBN: 0132704277

Category: Computers

Page: 656

View: 351

The Only Complete Technical Primer for MDM Planners, Architects, and Implementers Companies moving toward flexible SOA architectures often face difficult information management and integration challenges. The master data they rely on is often stored and managed in ways that are redundant, inconsistent, inaccessible, non-standardized, and poorly governed. Using Master Data Management (MDM), organizations can regain control of their master data, improve corresponding business processes, and maximize its value in SOA environments. Enterprise Master Data Management provides an authoritative, vendor-independent MDM technical reference for practitioners: architects, technical analysts, consultants, solution designers, and senior IT decisionmakers. Written by the IBM® data management innovators who are pioneering MDM, this book systematically introduces MDM’s key concepts and technical themes, explains its business case, and illuminates how it interrelates with and enables SOA. Drawing on their experience with cutting-edge projects, the authors introduce MDM patterns, blueprints, solutions, and best practices published nowhere else—everything you need to establish a consistent, manageable set of master data, and use it for competitive advantage. Coverage includes How MDM and SOA complement each other Using the MDM Reference Architecture to position and design MDM solutions within an enterprise Assessing the value and risks to master data and applying the right security controls Using PIM-MDM and CDI-MDM Solution Blueprints to address industry-specific information management challenges Explaining MDM patterns as enablers to accelerate consistent MDM deployments Incorporating MDM solutions into existing IT landscapes via MDM Integration Blueprints Leveraging master data as an enterprise asset—bringing people, processes, and technology together with MDM and data governance Best practices in MDM deployment, including data warehouse and SAP integration
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Designing the Data Lake and Avoiding the Garbage Dump

Author: Bill Inmon

Publisher: Technics Publications

ISBN: 1634621190

Category: Computers

Page: 166

View: 1974

Organizations invest incredible amounts of time and money obtaining and then storing big data in data stores called data lakes. But how many of these organizations can actually get the data back out in a useable form? Very few can turn the data lake into an information gold mine. Most wind up with garbage dumps. Data Lake Architecture will explain how to build a useful data lake, where data scientists and data analysts can solve business challenges and identify new business opportunities. Learn how to structure data lakes as well as analog, application, and text-based data ponds to provide maximum business value. Understand the role of the raw data pond and when to use an archival data pond. Leverage the four key ingredients for data lake success: metadata, integration mapping, context, and metaprocess. Bill Inmon opened our eyes to the architecture and benefits of a data warehouse, and now he takes us to the next level of data lake architecture.
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An Agile Approach to Leveraging Data and Analytics for Maximum Business Value

Author: Larry Burns

Publisher: Technics Publications

ISBN: 1634621492

Category: Computers

Page: 196

View: 8916

How do we enable our organizations to enjoy the often significant benefits of BI and analytics, while at the same time minimizing the cost and risk of failure? In this book, I am not going to try to be prescriptive; I won’t tell you exactly how to build your BI environment. Instead, I am going to focus on a few core principles that will enable you to navigate the rocky shoals of BI architecture and arrive at a destination best suited for your particular organization. Some of these core principles include: · Have an overarching strategy, plan, and roadmap · Recognize and leverage your existing technology investments · Support both data discovery and data reuse · Keep data in motion, not at rest · Separate information delivery from data storage · Emphasize data transparency over data quality · Take an agile approach to BI development. This book will show you how to successfully navigate both the jungle of BI technology and the minefield of human nature. It will show you how to create a BI architecture and strategy that addresses the needs of all organizational stakeholders. It will show you how to maximize the value of your BI investments. It will show you how to manage the risk of disruptive technology. And it will show you how to use agile methodologies to deliver on the promise of BI and analytics quickly, succinctly, and iteratively. This book is about many things. But principally, it’s about success. The goal of any enterprise initiative is to succeed and to derive benefit—benefit that all stakeholders can share in. I want you to be successful. I want your organization to be successful. This book will show you how. This book is for anyone who is currently or will someday be working on a BI, analytics, or Big Data project, and for organizations that want to get the maximum amount of value from both their data and their BI technology investment. This includes all stakeholders in the BI effort—not just the data people or the IT people, but also the business stakeholders who have the responsibility for the definition and use of data. There are six sections to this book: In Section I, What Kind of Garden Do You Want?, we will examine the benefits and risks of Business Intelligence, making the central point that BI is a business (not IT) process designed to manage data assets in pursuit of enterprise goals. We will show how data, when properly managed and used, can be a key enabler of several types of core business processes. The purpose of this section is to help you define the particular benefit(s) you want from BI. In Section II, Building the Bones, we will talk about how to design and build out the “hardscape” (infrastructure) of your BI environment. This stage of the process involves leveraging existing technology investments and iteratively moving toward your desired target state BI architecture. In Section III, From the Ground Up, we explore the more detailed aspects of implementing your BI operational environment. In Section IV, Weeds, Pests and Critters, we talk about the myriad of things that can go wrong on a BI project, and discuss ways of mitigating these risks. In Section V, The Sustainable Garden, we talk about how to create a BI infrastructure that is easy and inexpensive to maintain. Finally, Section VI presents a case study illustrating the principles of this book, as applied to a fictional manufacturing company (the Blue Moon Guitar Company).
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A Guide to Current Trends and Developments

Author: Alan Treadgold,Academic Director Oxford Institute of Retail Managemen and Deputy Dean Said Business School Jonathan Reynolds

Publisher: Oxford University Press

ISBN: 0198745753

Category:

Page: 250

View: 9794

The retail industry globally is in the early stages of an era of profound, perhaps unprecedented, change. This book is intended to serve as a robust and practical guide to leaders of enterprises tasked with both understanding and delivering success in the new landscape of retailing. The book firstly describes the major directions and drivers of change that define the new global landscape of retailing (Part 1). Accelerating technology change, the rise to prominence globally of internet enabled shoppers and the rapid emergence of entirely new retail enterprises and business models are combining to re-shape the very fundamentals of the retail industry. No longer are shops needed to be in the business of retailing. No longer is choice for the shopper limited to the neighbourhood, town or even country in which they live. No longer is the act of retailing solely the preserve of traditional retail enterprises as internet-enabled businesses, technology, logistics, suppliers and financial services enterprises all seek direct relationships with the shopper. The new landscape of retailing is an unforgiving one. Success can be achieved more quickly than has ever been possible before but failure is equally rapid. The opportunities in the new landscape of retailing are profound, but so too are the challenges. Part 2 of this book discusses the structures, skills and capabilities retail enterprises will need if they are to be successful in this new landscape and the skills and perspectives that will be required of the leaders of retail enterprises. Case studies of innovative and successful enterprises are presented throughout the book to illustrate the themes discussed. Frameworks are presented to provide practical guidance for enterprise leaders to understand and contextualise the nature of change that is re-shaping retail landscapes globally. Clear guidance is given of the capabilities, skills and perspectives that will be needed at both an enterprise and a personal leadership level to deliver success in the new landscape of retailing.
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The Goal-Question-Metric (GQM) Model to Transform Business Data into an Enterprise Asset

Author: Prashanth H Southekal, PhD

Publisher: Technics Publications

ISBN: 1634621867

Category: Computers

Page: 316

View: 1732

Today, digitization is dramatically changing the business landscape, and many progressive organizations have started to treat data as a valuable business asset. While many enterprises are investing in improved data management, only a few have leveraged data to truly impact business performance. To address this problem, Data for Business Performance provides readers with practical guidance and proven techniques to derive value from data in today’s business environment. Specifically, the book has five key elements that make it unique: The book is holistic, as it looks at deriving value for all three key purposes of data: decision making, compliance, and customer service. The book is for practitioners, with practical guidance and proven techniques supported by real world examples. The book is relevant for the current business and IT landscape. The book is novel, with the adoption of the Goal-Question-Metric (GQM) framework as the core mechanism to monetize data in the organization, based on business goals, key questions, and key performance indicators (KPIs). The book is technology-agnostic, as concepts are used for unlocking the value of data without any reference to proprietary technologies. This book is absolutely timely and relevant in today’s data-driven world. Most of the books on data available in the market today focus on data quality, governance, and analytics. This book from Dr. Prashanth Southekal is brilliant as it puts the business stakeholder at the center by addressing the key value propositions of the business user. This book is holistic and I strongly believe it will help to bridge the gaps we have today. Mario Faria Managing Vice President, Gartner, US In today’s era of digital transformation, data and information are more important than ever. But deep understanding of how to manage data and information properly is in short supply. That is what I love about this book by Dr. Southekal. He tangibly closes that gap for the reader. If you are using digital transformation to improve your business performance, this book and its discussion of data’s role in improving business performance is for you. Michael Fulton President, Americas Division, CC and C Solutions, US Packed with insights and leveraging a process oriented approach, this book covers a unique combination of the science, the art and the strategy of unlocking the potential of data for enterprises in a real-life context. The author has managed to provide a clear action plan for creating data analytics and its management a key function in a modern enterprise. Ashish Sonal (Vir Chakra) CEO, Orkash, India This book is one of the most practical sources for how companies can greatly improve their bottom line by improved data management and becoming a data-centric company. It combines leading data management theory with step-by-step implementation and real-life examples, and is a must-read for those wanting to derive more value from their corporate data. Lance Calleberg Application Architect, Husky Energy, Canada Certainly, an engaging read for both information management practitioners and business unit managers alike. The tools, models, and frameworks prescribed are valuable, relevant, and lucidly blend inputs from the real-world to address numerous data management glitches at organizations. Overall, a compelling read with several practical takeaways. Refreshing! Sriram Kannan Digital & Analytics Practice Leader, IBM, India Prashanth has given a very practical guide to implement data culture in an organization. The book Data for Business Performance talks about building the organization of the future and the role of data. Prashanth rightly believes and demonstrates that data is not an asset of the IT team and is an organization-wide asset. He proposes the need for the chief data officer (CDO) as a role that should anchor data and report to the CEO, and manage the stakeholders’ data needs. Harshajith Umapathy Senior Vice President, Hansa Cequity, India Dr. Southekal provides valuable insights on data and information management in mostly short and clearly written sections. Anyone interested in the data-driven company should read this book and learn about the hurdles on the road to be data-driven, and his valuable suggestions on how to overcome them. His wisdom may prevent some of the failures that helped him learn. Erik van der Voorden Domain Architect, Independent Consultant, Netherlands Data can tell us important stories when we process it by proven and structured approaches. Dr. Southekal’s book presents such an approach based on the GQM method for transforming business data into an enterprise asset. This book is a valuable resource for organizations willing to become real data-driven organizations. Ahmet Dikici, PhD Project Manager, Tubitak Bilgem Software Technologies, Turkey
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Author: Krish Krishnan

Publisher: Newnes

ISBN: 0124059201

Category: Computers

Page: 370

View: 428

Data Warehousing in the Age of the Big Data will help you and your organization make the most of unstructured data with your existing data warehouse. As Big Data continues to revolutionize how we use data, it doesn't have to create more confusion. Expert author Krish Krishnan helps you make sense of how Big Data fits into the world of data warehousing in clear and concise detail. The book is presented in three distinct parts. Part 1 discusses Big Data, its technologies and use cases from early adopters. Part 2 addresses data warehousing, its shortcomings, and new architecture options, workloads, and integration techniques for Big Data and the data warehouse. Part 3 deals with data governance, data visualization, information life-cycle management, data scientists, and implementing a Big Data–ready data warehouse. Extensive appendixes include case studies from vendor implementations and a special segment on how we can build a healthcare information factory. Ultimately, this book will help you navigate through the complex layers of Big Data and data warehousing while providing you information on how to effectively think about using all these technologies and the architectures to design the next-generation data warehouse. Learn how to leverage Big Data by effectively integrating it into your data warehouse. Includes real-world examples and use cases that clearly demonstrate Hadoop, NoSQL, HBASE, Hive, and other Big Data technologies Understand how to optimize and tune your current data warehouse infrastructure and integrate newer infrastructure matching data processing workloads and requirements
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Best Practices for Navigating the Future of Enterprise It

Author: Stephen Orban

Publisher: Createspace Independent Publishing Platform

ISBN: 9781981924318

Category:

Page: 334

View: 6587

Cloud computing is the most significant technology development of our lifetimes. It has made countless new businesses possible and presents a massive opportunity for large enterprises to innovate like startups and retire decades of technical debt. But making the most of the cloud requires much more from enterprises than just a technology change. Stephen Orban led Dow Jones's journey toward digital agility as their CIO and now leads AWS's Enterprise Strategy function, where he helps leaders from the largest companies in the world transform their businesses. As he demonstrates in this book, enterprises must re-train their people, evolve their processes, and transform their cultures as they move to the cloud. By bringing together his experiences and those of a number of business leaders, Orban shines a light on what works, what doesn't, and how enterprises can transform themselves using the cloud.
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A Practical Guide for Business and IT Professionals

Author: Steve Hoberman

Publisher: Technics Publications

ISBN: 163462016X

Category: Computers

Page: 244

View: 9846

Data Modeling Made Simple will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices. This book is written in a conversational style that encourages you to read it from start to finish and master these ten objectives: Know when a data model is needed and which type of data model is most effective for each situation Read a data model of any size and complexity with the same confidence as reading a book Build a fully normalized relational data model, as well as an easily navigatable dimensional model Apply techniques to turn a logical data model into an efficient physical design Leverage several templates to make requirements gathering more efficient and accurate Explain all ten categories of the Data Model Scorecard Learn strategies to improve your working relationships with others Appreciate the impact unstructured data has, and will have, on our data modeling deliverables Learn basic UML concepts Put data modeling in context with XML, metadata, and agile development Book Review by Johnny Gay In this book review, I address each section in the book and provide what I found most valuable as a data modeler. I compare, as I go, how the book's structure eases the new data modeler into the subject much like an instructor might ease a beginning swimmer into the pool. This book begins like a Dan Brown novel. It even starts out with the protagonist, our favorite data modeler, lost on a dark road somewhere in France. In this case, what saves him isn't a cipher, but of all things, something that's very much like a data model in the form of a map! The author deems they are both way-finding tools. The chapters in the book are divided into 5 sections. The chapters in each section end with an exercise and a list of the key points covered to reinforce what you've learned. I find myself comparing the teaching structure of the book to the way most of us learn to swim.
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Strategies for Gaining a Competitive Advantage with Data

Author: William McKnight

Publisher: Newnes

ISBN: 0124095267

Category: Computers

Page: 214

View: 2399

Information Management: Gaining a Competitive Advantage with Data is about making smart decisions to make the most of company information. Expert author William McKnight develops the value proposition for information in the enterprise and succinctly outlines the numerous forms of data storage. Information Management will enlighten you, challenge your preconceived notions, and help activate information in the enterprise. Get the big picture on managing data so that your team can make smart decisions by understanding how everything from workload allocation to data stores fits together. The practical, hands-on guidance in this book includes: Part 1: The importance of information management and analytics to business, and how data warehouses are used Part 2: The technologies and data that advance an organization, and extend data warehouses and related functionality Part 3: Big Data and NoSQL, and how technologies like Hadoop enable management of new forms of data Part 4: Pulls it all together, while addressing topics of agile development, modern business intelligence, and organizational change management Read the book cover-to-cover, or keep it within reach for a quick and useful resource. Either way, this book will enable you to master all of the possibilities for data or the broadest view across the enterprise. Balances business and technology, with non-product-specific technical detail Shows how to leverage data to deliver ROI for a business Engaging and approachable, with practical advice on the pros and cons of each domain, so that you learn how information fits together into a complete architecture Provides a path for the data warehouse professional into the new normal of heterogeneity, including NoSQL solutions
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The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

Author: Martin Kleppmann

Publisher: "O'Reilly Media, Inc."

ISBN: 1491903104

Category: Computers

Page: 624

View: 748

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures
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