Data Lineage From A Business Perspective
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| Author | : Irina Steenbeek |
| Publisher | : Independently Published |
| Total Pages | : 242 |
| Release | : 2021-10 |
| Genre | : |
| ISBN | : |
Data lineage has become a daily demand. However, data lineage remains an abstract/ unknown concept for many users. The implementation is complex and resource-consuming. Even if implemented, it is not used as expected. This book uncovers different aspects of data lineage for data management and business professionals. It provides the definition and metamodel of data lineage, demonstrates best practices in data lineage implementation, and discusses the key areas of data lineage usage. Several groups of professionals can use this book in different ways: Data management and business professionals can develop ideas about data lineage and its application areas. Professionals with a technical background may gain a better understanding of business needs and requirements for data lineage. Project management professionals can become familiar with the best practices of data lineage implementation.
| Author | : Irina Steenbeek |
| Publisher | : Independently Published |
| Total Pages | : 216 |
| Release | : 2019-03-09 |
| Genre | : Business & Economics |
| ISBN | : 9781793918994 |
Eight years ago, I joined a new company. My first challenge was to develop an automated management accounting reporting system. A deep analysis of the existing reports showed us the high necessity to implement a singular reporting platform, and we opted to implement a data warehouse. At the time, one of the consultants came to me and said, "I heard that we might need data management. I don't know what it is. Check it out." So I started Googling "Data management..".This book is for professionals who are now in the same position I found myself in eight years ago and for those who want to become a data management pro of a medium sized company.It is a collection of hands-on knowledge, experience and observations on how to implement data management in an effective, feasible and "to-the-point" way.
| Author | : Irina Steenbeek |
| Publisher | : |
| Total Pages | : 24 |
| Release | : 2019-10-21 |
| Genre | : |
| ISBN | : 9781701504745 |
*This book is a brief overview of the model and has only 24 pages.*Almost every data management professional, at some point in their career, has come across the following crucial questions:1. Which industry reference model should I use for the implementation of data managementfunctions?2. What are the key data management capabilities that are feasible and applicable to my company?3. How do I measure the maturity of the data management functions and compare that withthose of my peers in the industry4. What are the critical, logical steps in the implementation of data management?The "Orange" (meta)model of data management provides a collection of techniques and templates for the practical set up of data management through the design and implementation of the data and information value chain, enabled by a set of data management capabilities.This book is a toolkit for advanced data management professionals and consultants thatare involved in the data management function implementation.This book works together with the earlier published "The Data Management Toolkit". The "Orange" model assists in specifying the feasible scope of data management capabilities, that fits company's business goals and resources. "The Data Management Toolkit" is a practical implementation guide of the chosen data management capabilities.
| Author | : Mark Allen |
| Publisher | : Morgan Kaufmann |
| Total Pages | : 244 |
| Release | : 2015-03-21 |
| Genre | : Computers |
| ISBN | : 0128011475 |
Multi-Domain Master Data Management delivers practical guidance and specific instruction to help guide planners and practitioners through the challenges of a multi-domain master data management (MDM) implementation. Authors Mark Allen and Dalton Cervo bring their expertise to you in the only reference you need to help your organization take master data management to the next level by incorporating it across multiple domains. Written in a business friendly style with sufficient program planning guidance, this book covers a comprehensive set of topics and advanced strategies centered on the key MDM disciplines of Data Governance, Data Stewardship, Data Quality Management, Metadata Management, and Data Integration. - Provides a logical order toward planning, implementation, and ongoing management of multi-domain MDM from a program manager and data steward perspective. - Provides detailed guidance, examples and illustrations for MDM practitioners to apply these insights to their strategies, plans, and processes. - Covers advanced MDM strategy and instruction aimed at improving data quality management, lowering data maintenance costs, and reducing corporate risks by applying consistent enterprise-wide practices for the management and control of master data.
| Author | : Zhamak Dehghani |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 387 |
| Release | : 2022-03-08 |
| Genre | : Computers |
| ISBN | : 1492092363 |
Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh.
| Author | : Robert S. Seiner |
| Publisher | : Technics Publications |
| Total Pages | : 147 |
| Release | : 2014-09-01 |
| Genre | : Computers |
| ISBN | : 1634620453 |
Data-governance programs focus on authority and accountability for the management of data as a valued organizational asset. Data Governance should not be about command-and-control, yet at times could become invasive or threatening to the work, people and culture of an organization. Non-Invasive Data Governance™ focuses on formalizing existing accountability for the management of data and improving formal communications, protection, and quality efforts through effective stewarding of data resources. Non-Invasive Data Governance will provide you with a complete set of tools to help you deliver a successful data governance program. Learn how: • Steward responsibilities can be identified and recognized, formalized, and engaged according to their existing responsibility rather than being assigned or handed to people as more work. • Governance of information can be applied to existing policies, standard operating procedures, practices, and methodologies, rather than being introduced or emphasized as new processes or methods. • Governance of information can support all data integration, risk management, business intelligence and master data management activities rather than imposing inconsistent rigor to these initiatives. • A practical and non-threatening approach can be applied to governing information and promoting stewardship of data as a cross-organization asset. • Best practices and key concepts of this non-threatening approach can be communicated effectively to leverage strengths and address opportunities to improve.
| Author | : Sunil Soares |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2012 |
| Genre | : Computers |
| ISBN | : 9781583473771 |
Written by a leading expert in the field, this guide focuses on the convergence of two major trends in information management--big data and information governance--by taking a strategic approach oriented around business cases and industry imperatives. With the advent of new technologies, enterprises are expanding and handling very large volumes of data; this book, nontechnical in nature and geared toward business audiences, encourages the practice of establishing appropriate governance over big data initiatives and addresses how to manage and govern big data, highlighting the relevant processes, procedures, and policies. It teaches readers to understand how big data fits within an overall information governance program; quantify the business value of big data; apply information governance concepts such as stewardship, metadata, and organization structures to big data; appreciate the wide-ranging business benefits for various industries and job functions; sell the value of big data governance to businesses; and establish step-by-step processes to implement big data governance.
| Author | : Mandy Chessell |
| Publisher | : IBM Redbooks |
| Total Pages | : 30 |
| Release | : 2018-02-19 |
| Genre | : Computers |
| ISBN | : 0738456667 |
This IBM RedguideTM publication looks back on the key decisions that made the data lake successful and looks forward to the future. It proposes that the metadata management and governance approaches developed for the data lake can be adopted more broadly to increase the value that an organization gets from its data. Delivering this broader vision, however, requires a new generation of data catalogs and governance tools built on open standards that are adopted by a multi-vendor ecosystem of data platforms and tools. Work is already underway to define and deliver this capability, and there are multiple ways to engage. This guide covers the reasons why this new capability is critical for modern businesses and how you can get value from it.
| Author | : Irina Steenbeek |
| Publisher | : Createspace Independent Publishing Platform |
| Total Pages | : 32 |
| Release | : 2018-03-16 |
| Genre | : |
| ISBN | : 9781984149930 |
A lot of companies realize that data is an invaluable asset and has to be managed accordingly. They would also like to get value from data. Everyone wants to be 'data-driven' these days. What lies beneath this idea, is the wish to make the decision-making process easier and more effective. It means delivering the required data of acceptable quality to the relevant decision makers when and where they need it. In short: a lot of companies have the necessity to manage their data properly. The main question is: how do you put this in practice? Knowing the potential of your data, and managing it correctly is the key to an effective and successful business. As a result of well-implemented data management, you will be able to reduce risks and costs, increase efficiency, ensure business continuity and successful growth. In this book, we invite you for a five-course dinner. During each course we will explain the steps of our 5-step programme which guarantees successful implementation of data management.
| Author | : Pradeep Pasupuleti |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 164 |
| Release | : 2015-11-26 |
| Genre | : Computers |
| ISBN | : 1785881663 |
Explore architectural approaches to building Data Lakes that ingest, index, manage, and analyze massive amounts of data using Big Data technologies About This Book Comprehend the intricacies of architecting a Data Lake and build a data strategy around your current data architecture Efficiently manage vast amounts of data and deliver it to multiple applications and systems with a high degree of performance and scalability Packed with industry best practices and use-case scenarios to get you up-and-running Who This Book Is For This book is for architects and senior managers who are responsible for building a strategy around their current data architecture, helping them identify the need for a Data Lake implementation in an enterprise context. The reader will need a good knowledge of master data management and information lifecycle management, and experience of Big Data technologies. What You Will Learn Identify the need for a Data Lake in your enterprise context and learn to architect a Data Lake Learn to build various tiers of a Data Lake, such as data intake, management, consumption, and governance, with a focus on practical implementation scenarios Find out the key considerations to be taken into account while building each tier of the Data Lake Understand Hadoop-oriented data transfer mechanism to ingest data in batch, micro-batch, and real-time modes Explore various data integration needs and learn how to perform data enrichment and data transformations using Big Data technologies Enable data discovery on the Data Lake to allow users to discover the data Discover how data is packaged and provisioned for consumption Comprehend the importance of including data governance disciplines while building a Data Lake In Detail A Data Lake is a highly scalable platform for storing huge volumes of multistructured data from disparate sources with centralized data management services. This book explores the potential of Data Lakes and explores architectural approaches to building data lakes that ingest, index, manage, and analyze massive amounts of data using batch and real-time processing frameworks. It guides you on how to go about building a Data Lake that is managed by Hadoop and accessed as required by other Big Data applications. This book will guide readers (using best practices) in developing Data Lake's capabilities. It will focus on architect data governance, security, data quality, data lineage tracking, metadata management, and semantic data tagging. By the end of this book, you will have a good understanding of building a Data Lake for Big Data. Style and approach Data Lake Development with Big Data provides architectural approaches to building a Data Lake. It follows a use case-based approach where practical implementation scenarios of each key component are explained. It also helps you understand how these use cases are implemented in a Data Lake. The chapters are organized in a way that mimics the sequential data flow evidenced in a Data Lake.
