Managing Data As A Product

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Managing Data As A Product

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Author : Andrea Gioia
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-11-29

Managing Data As A Product written by Andrea Gioia and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-29 with Computers categories.

Learn everything you need to know to manage data as a product and shift toward a more modular and decentralized socio-technical data architecture to deliver business value in an incremental, measurable, and sustainable way Key Features Leverage data-as-product to unlock the modular platform potential and fix flaws in traditional monolithic architectures Learn how to identify, implement, and operate data products throughout their life cycle Design and execute a forward-thinking strategy to turn your data products into organizational assets Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionTraditional monolithic data platforms struggle with scalability and burden central data teams with excessive cognitive load, leading to challenges in managing technological debt. As maintenance costs escalate, these platforms lose their ability to provide sustained value over time. With two decades of hands-on experience implementing data solutions and his pioneering work in the Open Data Mesh Initiative, Andrea Gioia brings practical insights and proven strategies for transforming how organizations manage their data assets. Managing Data as a Product introduces a modular and distributed approach to data platform development, centered on the concept of data products. In this book, you’ll explore the rationale behind this shift, understand the core features and structure of data products, and learn how to identify, develop, and operate them in a production environment. The book guides you through designing and implementing an incremental, value-driven strategy for adopting data product-centered architectures, including strategies for securing buy-in from stakeholders. Additionally, it explores data modeling in distributed environments, emphasizing its crucial role in fully leveraging modern generative AI solutions. By the end of this book, you’ll have gained a comprehensive understanding of product-centric data architecture and the essential steps needed to adopt this modern approach to data management.What you will learn Overcome the challenges in scaling monolithic data platforms, including cognitive load, tech debt, and maintenance costs Discover the benefits of adopting a data-as-a-product approach for scalability and sustainability Navigate the complete data product lifecycle, from inception to decommissioning Automate data product lifecycle management using a self-serve platform Implement an incremental, value-driven strategy for transitioning to data-product-centric architectures Optimize data modeling in distributed environments to enhance GenAI-based use cases Who this book is for If you’re an experienced data engineer, data leader, architect, or practitioner committed to reimagining your data architecture and designing one that enables your organization to get the most value from your data in a sustainable and scalable way, this book is for you. Whether you’re a staff engineer, product manager, or a software engineering leader or executive, you’ll find this book useful. Familiarity with basic data engineering principles and practices is assumed.

Product Information Management

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Author : Jorij Abraham
language : en
Publisher: Springer
Release Date : 2014-05-05

Product Information Management written by Jorij Abraham and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-05 with Business & Economics categories.

Product Information Management is the latest topic that companies across the world are deliberating upon. As companies sell online, they are confronted with the fact that not all information necessary to sell their products is available. Where marketing, sales and finance have been core processes of the corporate world for a long time, PIM is a new business process with its own unique implementation and management challenges. The book describes the core PIM processes; their strategic, tactical and operational benefits and implementation challenges. The book has been written for managers, business users as well as students, and illustrates the different concepts with practical cases from companies like Coca Cola, Nikon and Thomas Cook.

Product Lifecycle Management For A Global Market

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Author : Shuichi Fukuda
language : en
Publisher: Springer
Release Date : 2014-12-17

Product Lifecycle Management For A Global Market written by Shuichi Fukuda and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-17 with Business & Economics categories.

This book constitutes the refereed post-proceedings of the 11th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2014, held in Yokohama, Japan, in July 2014. The 51 full papers presented were carefully reviewed and selected from 77 submissions. They are organized in the following topical sections: BIM operations, maintenance, and renovation; BIM concepts and lifecycle management; design and education; naval engineering and shipbuilding; aeronautical and automotive engineering; industry and consumer products; interoperability, integration, configuration, systems engineering; change management and maturity; knowledge engineering; knowledge management; service and manufacturing; and new PLM.

Data Management At Scale

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Author : Piethein Strengholt
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-04-10

Data Management At Scale written by Piethein Strengholt and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-10 with Computers categories.

As data management continues to evolve rapidly, managing all of your data in a central place, such as a data warehouse, is no longer scalable. Today's world is about quickly turning data into value. This requires a paradigm shift in the way we federate responsibilities, manage data, and make it available to others. With this practical book, you'll learn how to design a next-gen data architecture that takes into account the scale you need for your organization. Executives, architects and engineers, analytics teams, and compliance and governance staff will learn how to build a next-gen data landscape. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including regulatory requirements, privacy concerns, and new developments such as data mesh and data fabric Go deep into building a modern data architecture, including cloud data landing zones, domain-driven design, data product design, and more Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata

Data Quality Management In The Data Age

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Author : Haiyan Yu
language : en
Publisher: Springer Nature
Release Date : 2024-10-29

Data Quality Management In The Data Age written by Haiyan Yu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-29 with Computers categories.

This book addresses data quality management for data markets, including foundational quality issues in modern data science. By clarifying the concept of data quality, its impact on real-world applications, and the challenges stemming from poor data quality, it will equip data scientists and engineers with advanced skills in data quality management, with a particular focus on applications within data markets. This will help them create an environment that encourages potential data sellers with high-quality data to join the market, ultimately leading to an improvement in overall data quality. High-quality data, as a novel factor of production, has assumed a pivotal role in driving digital economic development. The acquisition of such data is particularly important for contemporary decision-making models. Data markets facilitate the procurement of high-quality data and thereby enhance the data supply. Consequently, potential data sellers with high-quality data are incentivized to enter the market, an aspect that is particularly relevant in data-scarce domains such as personalized medicine and services. Data scientists have a pivotal role to play in both the intellectual vitality and the practical utility of high-quality data. Moreover, data quality control presents opportunities for data scientists to engage with less structured or ambiguous problems. The book will foster fruitful discussions on the contributions that various scientists and engineers can make to data quality and the further evolution of data markets.

Managing Data Integrity For Finance

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Author : Jane Sarah Lat
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-01-31

Managing Data Integrity For Finance written by Jane Sarah Lat and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-31 with Computers categories.

Level up your career by learning best practices for managing the data quality and integrity of your financial data Key Features Accelerate data integrity management using artificial intelligence-powered solutions Learn how business intelligence tools, ledger databases, and database locks solve data integrity issues Find out how to detect fraudulent transactions affecting financial report integrity Book DescriptionData integrity management plays a critical role in the success and effectiveness of organizations trying to use financial and operational data to make business decisions. Unfortunately, there is a big gap between the analysis and management of finance data along with the proper implementation of complex data systems across various organizations. The first part of this book covers the important concepts for data quality and data integrity relevant to finance, data, and tech professionals. The second part then focuses on having you use several data tools and platforms to manage and resolve data integrity issues on financial data. The last part of this the book covers intermediate and advanced solutions, including managed cloud-based ledger databases, database locks, and artificial intelligence, to manage the integrity of financial data in systems and databases. After finishing this hands-on book, you will be able to solve various data integrity issues experienced by organizations globally.What you will learn Develop a customized financial data quality scorecard Utilize business intelligence tools to detect, manage, and resolve data integrity issues Find out how to use managed cloud-based ledger databases for financial data integrity Apply database locking techniques to prevent transaction integrity issues involving finance data Discover the methods to detect fraudulent transactions affecting financial report integrity Use artificial intelligence-powered solutions to resolve various data integrity issues and challenges Who this book is for This book is for financial analysts, technical leaders, and data professionals interested in learning practical strategies for managing data integrity and data quality using relevant frameworks and tools. A basic understanding of finance concepts, accounting, and data analysis is expected. Knowledge of finance management is not a prerequisite, but it’ll help you grasp the more advanced topics covered in this book.

Advances In Apparel Production

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Author : Catherine Fairhurst
language : en
Publisher: Elsevier
Release Date : 2008-05-28

Advances In Apparel Production written by Catherine Fairhurst and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-05-28 with Technology & Engineering categories.

Apparel production is a complex process often involving an international supply chain which must respond rapidly to the changing needs and tastes of consumers. This important book discusses the technological improvements which are transforming the speed, flexibility and productivity of the industry.The first part of the book reviews advances in apparel design. There are chapters on modelling fabric and garment drape, computer-aided colour matching, yarn design and pattern making. Other chapters discuss key issues in apparel sizing and fit, and the role of 3-D body scanning in improving garment fit and design. The second part of the book surveys advances in production, beginning with product development before looking at advances in knitting, sewing, printing, finishing and fabric inspection.With its distinguished editor and international team of contributors, Advances in apparel production is a standard work for those researching and working in this important industry. - Discusses the technological improvements transforming the speed, flexibility and productivity of the industry - Examines computer aided colour matching, garment drape and yarn design - Explores key issues in apparel sizing and fit, the role of three-dimensional body scanning in improving garment fit and design

Managing Data Science

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Author : Kirill Dubovikov
language : en
Publisher:
Release Date : 2019-11-12

Managing Data Science written by Kirill Dubovikov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-12 with categories.

Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key Features Learn the basics of data science and explore its possibilities and limitations Manage data science projects and assemble teams effectively even in the most challenging situations Understand management principles and approaches for data science projects to streamline the innovation process Book Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learn Understand the underlying problems of building a strong data science pipeline Explore the different tools for building and deploying data science solutions Hire, grow, and sustain a data science team Manage data science projects through all stages, from prototype to production Learn how to use ModelOps to improve your data science pipelines Get up to speed with the model testing techniques used in both development and production stages Who this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.

Managing Data Science

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Author : Kirill Dubovikov
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-11-12

Managing Data Science written by Kirill Dubovikov and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-12 with Computers categories.

Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key FeaturesLearn the basics of data science and explore its possibilities and limitationsManage data science projects and assemble teams effectively even in the most challenging situationsUnderstand management principles and approaches for data science projects to streamline the innovation processBook Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learnUnderstand the underlying problems of building a strong data science pipelineExplore the different tools for building and deploying data science solutionsHire, grow, and sustain a data science teamManage data science projects through all stages, from prototype to productionLearn how to use ModelOps to improve your data science pipelinesGet up to speed with the model testing techniques used in both development and production stagesWho this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.

Enterprise Master Data Management

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Author : Allen Dreibelbis
language : en
Publisher: Pearson Education
Release Date : 2008-06-05

Enterprise Master Data Management written by Allen Dreibelbis and has been published by Pearson Education this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-06-05 with Business & Economics categories.

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