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| data management use cases: Enterprise Master Data Management Allen Dreibelbis, Eberhard Hechler, Ivan Milman, Martin Oberhofer, Paul van Run, Dan Wolfson, 2008-06-05 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 |
| data management use cases: Master Data Management David Loshin, 2010-07-28 The key to a successful MDM initiative isn't technology or methods, it's people: the stakeholders in the organization and their complex ownership of the data that the initiative will affect.Master Data Management equips you with a deeply practical, business-focused way of thinking about MDM—an understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: you'll master all the details involved in planning and executing an MDM project that leads to measurable improvements in business productivity and effectiveness. - Presents a comprehensive roadmap that you can adapt to any MDM project - Emphasizes the critical goal of maintaining and improving data quality - Provides guidelines for determining which data to master. - Examines special issues relating to master data metadata - Considers a range of MDM architectural styles - Covers the synchronization of master data across the application infrastructure |
| data management use cases: Data Management Technologies and Applications Slimane Hammoudi, Christoph Quix, Jorge Bernardino, 2021-07-22 This book constitutes the thoroughly refereed proceedings of the 9th International Conference on Data Management Technologies and Applications, DATA 2020, which was supposed to take place in Paris, France, in July 2020. Due to the Covid-19 pandemic the event was held virtually. The 14 revised full papers were carefully reviewed and selected from 70 submissions. The papers deal with the following topics: datamining; decision support systems; data analytics; data and information quality; digital rights management; big data; knowledge management; ontology engineering; digital libraries; mobile databases; object-oriented database systems; data integrity. |
| data management use cases: Design Patterns for Cloud Native Applications Kasun Indrasiri, Sriskandarajah Suhothayan, 2021-05-17 With the immense cost savings and scalability the cloud provides, the rationale for building cloud native applications is no longer in question. The real issue is how. With this practical guide, developers will learn about the most commonly used design patterns for building cloud native applications using APIs, data, events, and streams in both greenfield and brownfield development. You'll learn how to incrementally design, develop, and deploy large and effective cloud native applications that you can manage and maintain at scale with minimal cost, time, and effort. Authors Kasun Indrasiri and Sriskandarajah Suhothayan highlight use cases that effectively demonstrate the challenges you might encounter at each step. Learn the fundamentals of cloud native applications Explore key cloud native communication, connectivity, and composition patterns Learn decentralized data management techniques Use event-driven architecture to build distributed and scalable cloud native applications Explore the most commonly used patterns for API management and consumption Examine some of the tools and technologies you'll need for building cloud native systems |
| data management use cases: Data Management at Scale Piethein Strengholt, 2020-07-29 As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata |
| data management use cases: Performance Dashboards Wayne W. Eckerson, 2005-10-27 Tips, techniques, and trends on how to use dashboard technology to optimize business performance Business performance management is a hot new management discipline that delivers tremendous value when supported by information technology. Through case studies and industry research, this book shows how leading companies are using performance dashboards to execute strategy, optimize business processes, and improve performance. Wayne W. Eckerson (Hingham, MA) is the Director of Research for The Data Warehousing Institute (TDWI), the leading association of business intelligence and data warehousing professionals worldwide that provide high-quality, in-depth education, training, and research. He is a columnist for SearchCIO.com, DM Review, Application Development Trends, the Business Intelligence Journal, and TDWI Case Studies & Solution. |
| data management use cases: Data Management: a gentle introduction Bas van Gils, 2020-03-03 The overall objective of this book is to show that data management is an exciting and valuable capability that is worth time and effort. More specifically it aims to achieve the following goals: 1. To give a “gentle” introduction to the field of DM by explaining and illustrating its core concepts, based on a mix of theory, practical frameworks such as TOGAF, ArchiMate, and DMBOK, as well as results from real-world assignments. 2. To offer guidance on how to build an effective DM capability in an organization.This is illustrated by various use cases, linked to the previously mentioned theoretical exploration as well as the stories of practitioners in the field. The primary target groups are: busy professionals who “are actively involved with managing data”. The book is also aimed at (Bachelor’s/ Master’s) students with an interest in data management. The book is industry-agnostic and should be applicable in different industries such as government, finance, telecommunications etc. Typical roles for which this book is intended: data governance office/ council, data owners, data stewards, people involved with data governance (data governance board), enterprise architects, data architects, process managers, business analysts and IT analysts. The book is divided into three main parts: theory, practice, and closing remarks. Furthermore, the chapters are as short and to the point as possible and also make a clear distinction between the main text and the examples. If the reader is already familiar with the topic of a chapter, he/she can easily skip it and move on to the next. |
| data management use cases: Data Governance: The Definitive Guide Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy-Grant, Jessi Ashdown, 2021-03-08 As your company moves data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure you meet compliance. Data governance incorporates the ways that people, processes, and technology work together to support business efficiency. With this practical guide, chief information, data, and security officers will learn how to effectively implement and scale data governance throughout their organizations. You'll explore how to create a strategy and tooling to support the democratization of data and governance principles. Through good data governance, you can inspire customer trust, enable your organization to extract more value from data, and generate more-competitive offerings and improvements in customer experience. This book shows you how. Enable auditable legal and regulatory compliance with defined and agreed-upon data policies Employ better risk management Establish control and maintain visibility into your company's data assets, providing a competitive advantage Drive top-line revenue and cost savings when developing new products and services Implement your organization's people, processes, and tools to operationalize data trustworthiness. |
| data management use cases: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| data management use cases: Data Management: a gentle introduction Bas van Gils, 2020-03-03 The overall objective of this book is to show that data management is an exciting and valuable capability that is worth time and effort. More specifically it aims to achieve the following goals: 1. To give a “gentle” introduction to the field of DM by explaining and illustrating its core concepts, based on a mix of theory, practical frameworks such as TOGAF, ArchiMate, and DMBOK, as well as results from real-world assignments. 2. To offer guidance on how to build an effective DM capability in an organization.This is illustrated by various use cases, linked to the previously mentioned theoretical exploration as well as the stories of practitioners in the field. The primary target groups are: busy professionals who “are actively involved with managing data”. The book is also aimed at (Bachelor’s/ Master’s) students with an interest in data management. The book is industry-agnostic and should be applicable in different industries such as government, finance, telecommunications etc. Typical roles for which this book is intended: data governance office/ council, data owners, data stewards, people involved with data governance (data governance board), enterprise architects, data architects, process managers, business analysts and IT analysts. The book is divided into three main parts: theory, practice, and closing remarks. Furthermore, the chapters are as short and to the point as possible and also make a clear distinction between the main text and the examples. If the reader is already familiar with the topic of a chapter, he/she can easily skip it and move on to the next. |
| data management use cases: Data Driven Thomas C. Redman, 2008-09-22 Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the Data Doc, shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professionally. Whether you're a top executive, an aspiring leader, or a product-line manager, this eye-opening book provides the tools and thinking you need to do that. |
| data management use cases: Smarter Modeling of IBM InfoSphere Master Data Management Solutions Jan-Bernd Bracht, Joerg Rehr, Markus Siebert, Rouven Thimm, IBM Redbooks, 2012-08-09 This IBM® Redbooks® publication presents a development approach for master data management projects, and in particular, those projects based on IBM InfoSphere® MDM Server. The target audience for this book includes Enterprise Architects, Information, Integration and Solution Architects and Designers, Developers, and Product Managers. Master data management combines a set of processes and tools that defines and manages the non-transactional data entities of an organization. Master data management can provide processes for collecting, consolidating, persisting, and distributing this data throughout an organization. IBM InfoSphere Master Data Management Server creates trusted views of master data that can improve applications and business processes. You can use it to gain control over business information by managing and maintaining a complete and accurate view of master data. You also can use InfoSphere MDM Server to extract maximum value from master data by centralizing multiple data domains. InfoSphere MDM Server provides a comprehensive set of prebuilt business services that support a full range of master data management functionality. |
| data management use cases: Data Management at Scale Piethein Strengholt, 2023-04-10 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 management use cases: Executing Data Quality Projects Danette McGilvray, 2021-05-27 Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today's data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization's standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. - Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach - Contains real examples from around the world, gleaned from the author's consulting practice and from those who implemented based on her training courses and the earlier edition of the book - Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices - A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online |
| data management use cases: Big Data Management, Technologies, and Applications Hu, Wen-Chen, 2013-10-31 This book discusses the exponential growth of information size and the innovative methods for data capture, storage, sharing, and analysis for big data--Provided by publisher. |
| data management use cases: Data Mesh Zhamak Dehghani, 2022-03-08 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. |
| data management use cases: Data Management Solutions Using SAS Hash Table Operations Paul Dorfman, Don Henderson, 2018-07-09 Hash tables can do a lot more than you might think! Data Management Solutions Using SAS Hash Table Operations: A Business Intelligence Case Study concentrates on solving your challenging data management and analysis problems via the power of the SAS hash object, whose environment and tools make it possible to create complete dynamic solutions. To this end, this book provides an in-depth overview of the hash table as an in-memory database with the CRUD (Create, Retrieve, Update, Delete) cycle rendered by the hash object tools. By using this concept and focusing on real-world problems exemplified by sports data sets and statistics, this book seeks to help you take advantage of the hash object productively, in particular, but not limited to, the following tasks: select proper hash tools to perform hash table operations use proper hash table operations to support specific data management tasks use the dynamic, run-time nature of hash object programming understand the algorithmic principles behind hash table data look-up, retrieval, and aggregation learn how to perform data aggregation, for which the hash object is exceptionally well suited manage the hash table memory footprint, especially when processing big data use hash object techniques for other data processing tasks, such as filtering, combining, splitting, sorting, and unduplicating. Using this book, you will be able to answer your toughest questions quickly and in the most efficient way possible! |
| data management use cases: The Practitioner's Guide to Data Quality Improvement David Loshin, 2010-11-22 The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. - Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. - Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. - Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. |
| data management use cases: Data Governance John Ladley, 2019-11-08 Managing data continues to grow as a necessity for modern organizations. There are seemingly infinite opportunities for organic growth, reduction of costs, and creation of new products and services. It has become apparent that none of these opportunities can happen smoothly without data governance. The cost of exponential data growth and privacy / security concerns are becoming burdensome. Organizations will encounter unexpected consequences in new sources of risk. The solution to these challenges is also data governance; ensuring balance between risk and opportunity. Data Governance, Second Edition, is for any executive, manager or data professional who needs to understand or implement a data governance program. It is required to ensure consistent, accurate and reliable data across their organization. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. This valuable resource provides comprehensive guidance to beginning professionals, managers or analysts looking to improve their processes, and advanced students in Data Management and related courses. With the provided framework and case studies all professionals in the data governance field will gain key insights into launching successful and money-saving data governance program. - Incorporates industry changes, lessons learned and new approaches - Explores various ways in which data analysts and managers can ensure consistent, accurate and reliable data across their organizations - Includes new case studies which detail real-world situations - Explores all of the capabilities an organization must adopt to become data driven - Provides guidance on various approaches to data governance, to determine whether an organization should be low profile, central controlled, agile, or traditional - Provides guidance on using technology and separating vendor hype from sincere delivery of necessary capabilities - Offers readers insights into how their organizations can improve the value of their data, through data quality, data strategy and data literacy - Provides up to 75% brand-new content compared to the first edition |
| data management use cases: A Primer in Financial Data Management Martijn Groot, 2017-05-10 A Primer in Financial Data Management describes concepts and methods, considering financial data management, not as a technological challenge, but as a key asset that underpins effective business management. This broad survey of data management in financial services discusses the data and process needs from the business user, client and regulatory perspectives. Its non-technical descriptions and insights can be used by readers with diverse interests across the financial services industry. The need has never been greater for skills, systems, and methodologies to manage information in financial markets. The volume of data, the diversity of sources, and the power of the tools to process it massively increased. Demands from business, customers, and regulators on transparency, safety, and above all, timely availability of high quality information for decision-making and reporting have grown in tandem, making this book a must read for those working in, or interested in, financial management. - Focuses on ways information management can fuel financial institutions' processes, including regulatory reporting, trade lifecycle management, and customer interaction - Covers recent regulatory and technological developments and their implications for optimal financial information management - Views data management from a supply chain perspective and discusses challenges and opportunities, including big data technologies and regulatory scrutiny |
| data management use cases: Database Management using AI: A Comprehensive Guide A Purushotham Reddy, 2024-10-20 Database Management Using AI: A Comprehensive Guide is a professional yet accessible exploration of how artificial intelligence (AI) is reshaping the world of database management. Designed for database administrators, data scientists, and tech enthusiasts, this book walks readers through the transformative impact of AI on modern data systems. The guide begins with the fundamentals of database management, covering key concepts such as data models, SQL, and the principles of database design. From there, it delves into the powerful role AI plays in optimizing database performance, enhancing security, and automating complex tasks like data retrieval, query optimization, and schema design. The book doesn't stop at theory. It brings AI to life with practical case studies showing how AI-driven database systems are being used in industries such as e-commerce, healthcare, finance, and logistics. These real-world examples demonstrate AI's role in improving efficiency, reducing errors, and driving intelligent decision-making. Key topics covered include: Introduction to Database Systems: Fundamentals of database management, from relational databases to modern NoSQL systems. AI Integration: How AI enhances database performance, automates routine tasks, and strengthens security. Real-World Applications: Case studies from diverse sectors like healthcare, finance, and retail, showcasing the practical impact of AI in database management. Predictive Analytics and Data Mining: How AI tools leverage data to make accurate predictions and uncover trends. Future Trends: Explore cutting-edge innovations like autonomous databases and cloud-based AI solutions that are shaping the future of data management. With its clear explanations and actionable insights, Database Management Using AI equips readers with the knowledge to navigate the fast-evolving landscape of AI-powered databases, making it a must-have resource for those looking to stay ahead in the digital age. |
| data management use cases: Big Data Management and Processing Kuan-Ching Li, Hai Jiang, Albert Y. Zomaya, 2017-05-19 From the Foreword: Big Data Management and Processing is [a] state-of-the-art book that deals with a wide range of topical themes in the field of Big Data. The book, which probes many issues related to this exciting and rapidly growing field, covers processing, management, analytics, and applications... [It] is a very valuable addition to the literature. It will serve as a source of up-to-date research in this continuously developing area. The book also provides an opportunity for researchers to explore the use of advanced computing technologies and their impact on enhancing our capabilities to conduct more sophisticated studies. ---Sartaj Sahni, University of Florida, USA Big Data Management and Processing covers the latest Big Data research results in processing, analytics, management and applications. Both fundamental insights and representative applications are provided. This book is a timely and valuable resource for students, researchers and seasoned practitioners in Big Data fields. --Hai Jin, Huazhong University of Science and Technology, China Big Data Management and Processing explores a range of big data related issues and their impact on the design of new computing systems. The twenty-one chapters were carefully selected and feature contributions from several outstanding researchers. The book endeavors to strike a balance between theoretical and practical coverage of innovative problem solving techniques for a range of platforms. It serves as a repository of paradigms, technologies, and applications that target different facets of big data computing systems. The first part of the book explores energy and resource management issues, as well as legal compliance and quality management for Big Data. It covers In-Memory computing and In-Memory data grids, as well as co-scheduling for high performance computing applications. The second part of the book includes comprehensive coverage of Hadoop and Spark, along with security, privacy, and trust challenges and solutions. The latter part of the book covers mining and clustering in Big Data, and includes applications in genomics, hospital big data processing, and vehicular cloud computing. The book also analyzes funding for Big Data projects. |
| data management use cases: The Self-Service Data Roadmap Sandeep Uttamchandani, 2020-09-10 Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can’t scale data science teams fast enough to keep up with the growing amounts of data to transform. What’s the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work. Build a self-service portal to support data discovery, quality, lineage, and governance Select the best approach for each self-service capability using open source cloud technologies Tailor self-service for the people, processes, and technology maturity of your data platform Implement capabilities to democratize data and reduce time to insight Scale your self-service portal to support a large number of users within your organization |
| data management use cases: NoSQL Distilled Pramod J. Sadalage, Martin Fowler, 2013 'NoSQL Distilled' is designed to provide you with enough background on how NoSQL databases work, so that you can choose the right data store without having to trawl the whole web to do it. It won't answer your questions definitively, but it should narrow down the range of options you have to consider. |
| data management use cases: Enterprise Data Governance Pierre Bonnet, 2013-03-04 In an increasingly digital economy, mastering the quality of data is an increasingly vital yet still, in most organizations, a considerable task. The necessity of better governance and reinforcement of international rules and regulatory or oversight structures (Sarbanes Oxley, Basel II, Solvency II, IAS-IFRS, etc.) imposes on enterprises the need for greater transparency and better traceability of their data. All the stakeholders in a company have a role to play and great benefit to derive from the overall goals here, but will invariably turn towards their IT department in search of the answers. However, the majority of IT systems that have been developed within businesses are overly complex, badly adapted, and in many cases obsolete; these systems have often become a source of data or process fragility for the business. It is in this context that the management of ‘reference and master data’ or Master Data Management (MDM) and semantic modeling can intervene in order to straighten out the management of data in a forward-looking and sustainable manner. This book shows how company executives and IT managers can take these new challenges, as well as the advantages of using reference and master data management, into account in answering questions such as: Which data governance functions are available? How can IT be better aligned with business regulations? What is the return on investment? How can we assess intangible IT assets and data? What are the principles of semantic modeling? What is the MDM technical architecture? In these ways they will be better able to deliver on their responsibilities to their organizations, and position them for growth and robust data management and integrity in the future. |
| data management use cases: The DAMA Dictionary of Data Management Dama International, 2011 A glossary of over 2,000 terms which provides a common data management vocabulary for IT and Business professionals, and is a companion to the DAMA Data Management Body of Knowledge (DAMA-DMBOK). Topics include: Analytics & Data Mining Architecture Artificial Intelligence Business Analysis DAMA & Professional Development Databases & Database Design Database Administration Data Governance & Stewardship Data Management Data Modeling Data Movement & Integration Data Quality Management Data Security Management Data Warehousing & Business Intelligence Document, Record & Content Management Finance & Accounting Geospatial Data Knowledge Management Marketing & Customer Relationship Management Meta-Data Management Multi-dimensional & OLAP Normalization Object-Orientation Parallel Database Processing Planning Process Management Project Management Reference & Master Data Management Semantic Modeling Software Development Standards Organizations Structured Query Language (SQL) XML Development |
| data management use cases: Using IBM Spectrum Copy Data Management with IBM FlashSystem A9000 or A9000R and SAP HANA Axel Westphal, Bert Dufrasne, Markus Oscheka, IBM Redbooks, 2017-08-29 Data is the currency of the new economy, and organizations are increasingly tasked with finding better ways to protect, recover, access, share, and use it. IBM SpectrumTM Copy Data Management is aimed at using existing data in a manner that is efficient, automated, scalable. It helps you manage all of those snapshot and IBM FlashCopy® images made to support DevOps, data protection, disaster recovery, and Hybrid Cloud computing environments. This IBM® RedpaperTM publication specifically addresses IBM Spectrum Copy Data Management in combination with IBM FlashSystem® A9000 or A9000R when used for Automated Disaster Recovery of SAP HANA. |
| data management use cases: Crowdsourced Data Management Adam Marcus, Aditya Parameswaran, 2015-11-18 Crowdsourced Data Management: Industry and Academic Perspectives aims to narrow the gap between academics and practitioners in this burgeoning field. It simultaneously introduces academics to real problems that practitioners encounter every day, and provides a survey of the state of the art for practitioners to incorporate into their designs. |
| data management use cases: Modernizing Your IT Infrastructure with IBM b-type Gen 6 Storage Networking and IBM Spectrum Storage Products Deana Coble, Simone Bosisio, Lee Cockrell, Steve Guendert, Abilio Oliveira, Rodrigo Jungi Suzuki, IBM Redbooks, 2018-02-02 This IBM® Redbooks® publication describes the challenge that most data centers face when updating and modernizing their IT infrastructure. New business demands are driving new applications, joining, and creating in the digital world. A rich, meaningful digital experience is the key to effective engagement in today's integrated digital world. Companies are able to customize digital experiences for their employees with personalized, targeted content for fully connecting with customers, co-workers, and business partners in the most powerful and productive ways. To achieve this, a robust infrastructure is required. Speed of access to data is one of the most important factors. The development of the flash storage devices helped with the insatiable desire for data access speed, but even that is not enough for the most demanding uses. The needs of SAN switches, servers, and software defined infrastructure (SDI) technologies are all requiring more; therefore, the bigger picture needs to be wholly analyzed to build a balanced ecosystem. This publication can help you with planning for growth in your IT infrastructure. This publication explores the concept of modernization and considers important aspects of IT, such as SAN switches, storage systems, and software defined storage. |
| data management use cases: Enterprise Blockchain: Applications and Use Cases Peter Jones, 2024-10-13 Enterprise Blockchain: Applications and Use Cases is an essential guide for business leaders, IT professionals, and anyone interested in implementing blockchain technology in enterprise environments. This comprehensive book demystifies blockchain technology, detailing its fundamentals, security measures, and encryption techniques. Each chapter delves into a different aspect of enterprise application, from enhancing supply chain management to revolutionizing financial and banking operations. Readers will gain a thorough understanding of private versus public blockchains, explore real-world case studies, and discover strategic considerations for integrating blockchain with existing IT infrastructure. The book also addresses crucial legal and regulatory considerations, ensuring businesses can navigate the complexities of blockchain adoption with foresight and confidence. Whether you're looking to optimize business processes, increase transparency, or enhance security within your organization, Enterprise Blockchain: Applications and Use Cases provides the knowledge and insights needed to leverage blockchain technology effectively. Embrace the future of digital transformation in business with this authoritative and practical resource. |
| data management use cases: Principles of Database Management Wilfried Lemahieu, Seppe vanden Broucke, Bart Baesens, 2018-07-12 Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science. |
| data management use cases: Blockchain and Distributed Ledger Technology Use Cases Horst Treiblmaier, Trevor Clohessy, 2020-06-05 Blockchain and other trustless systems have gone from being relatively obscure technologies, which were only known to a small community of computer scientists and cryptologists, to mainstream phenomena that are now considered powerful game changers for many industries. This book explores and assesses real-world use cases and case studies on blockchain and related technologies. The studies describe the respective applications and address how these technologies have been deployed, the rationale behind their application, and finally, their outcomes. The book shares a wealth of experiences and lessons learned regarding financial markets, energy, SCM, healthcare, law and compliance. Given its scope, it is chiefly intended for academics and practitioners who want to learn more about blockchain applications. |
| data management use cases: Advances in Human Factors and Ergonomics in Healthcare and Medical Devices Jay Kalra, Nancy J. Lightner, Redha Taiar, 2021-07-07 This book is concerned with human factors and ergonomics research and developments in the design and use of systems and devices for effective and safe healthcare delivery. It reports on approaches for improving healthcare devices so that they better fit to people’s, including special population’s needs. It also covers assistive devices aimed at reducing occupational risks of health professionals as well as innovative strategies for error reduction, and more effective training and education methods for healthcare workers and professionals. Equal emphasis is given to digital technologies and to physical, cognitive and organizational aspects, which are considered in an integrated manner, so as to facilitate a systemic approach for improving the quality and safety of healthcare service. The book also includes a special section dedicated to innovative strategies for assisting caregivers’, patients’, and people’s needs during pandemic. Based on papers presented at the AHFE 2021 Conference on Human Factors and Ergonomics in Healthcare and Medical Devices, held virtually on 25–29 July, 2021, from USA, the book offers a timely reference guide to both researchers and healthcare professionals involved in the design of medical systems and managing healthcare settings, as well as to healthcare counselors and global health organizations. |
| data management use cases: Information Technology in Organisations and Societies Zach W. Y. Lee, Tommy K. H. Chan, Christy M. K. Cheung, 2021-07-12 Information Technology in Organisations and Societies: Multidisciplinary Perspectives from AI to Technostress consolidates studies on key issues and phenomena concerning the positive and negative aspects of IT use as well as prescribing future research avenues in related research. |
| data management use cases: From Active Data Management to Event-Based Systems and More Kai Sachs, Ilia Petrov, Pablo Guerrero, 2010-11-18 Data management has evolved over the years from being strictly associated with database systems, through active databases, to become a topic that has grown beyond the scope of a single field encompassing a large range of subjects, such as distributed systems, event-driven systems, and peer-to-peer and streaming systems. The present collection of works, which sheds light on various facets of data management, is dedicated to Prof. Alejandro Buchmann on the occasion of his 60th birthday. His scientific path looks back on more than thirty years of successful academic life and high-impact research. With this book we celebrate Prof. Buchmann's vision and achievements. |
| data management use cases: Business Process Management Forum Mathias Weske, Marco Montali, Ingo Weber, Jan vom Brocke, 2018-08-31 This book constitutes the proceedings of the BPM Forum held during the 16th International Conference on Business Process Management, BPM 2018, which took place in Sydney, Australia, in September 2018. The BPM Forum hosts innovative research which has a high potential of stimulating discussions. The papers selected for the forum are expected to showcase fresh ideas from exciting and emerging topics in BPM, even if they are not yet as mature as the regular papers at the conference. The 14 papers presented in this volume were carefully reviewed and selected from a total of 113 submissions. They were organized according to the tracks of the conference: foundations; engineering; management. |
| data management use cases: Building the Data Lakehouse Bill Inmon, Ranjeet Srivastava, Mary Levins, 2021-10 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. |
| data management use cases: Global Perspectives on Design Science Research Robert Winter, J. Leon Zhao, Stephan Aier, 2010-06 This book constitutes the refereed proceedings of the 5th International Conference on Global Perspectives on Design Science Research, DERIST 2010, held in St. Gallen, Switzerland, in June 2010. The 35 revised full papers presented together with 10 revised short papers were carefully reviewed and selected from 80 submissions. The papers are organized in topical sections on organising design research, reflecting design science research, design research techniques, design and context, design and organisation, design and information, design research exemplars, design and behaviour, designing collaboration, as well as design and requirements engineering. |
| data management use cases: The Definitive Guide to Data Integration Pierre-Yves BONNEFOY, Emeric CHAIZE, Raphaël MANSUY, Mehdi TAZI, 2024-03-29 Learn the essentials of data integration with this comprehensive guide, covering everything from sources to solutions, and discover the key to making the most of your data stack Key Features Learn how to leverage modern data stack tools and technologies for effective data integration Design and implement data integration solutions with practical advice and best practices Focus on modern technologies such as cloud-based architectures, real-time data processing, and open-source tools and technologies Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe Definitive Guide to Data Integration is an indispensable resource for navigating the complexities of modern data integration. Focusing on the latest tools, techniques, and best practices, this guide helps you master data integration and unleash the full potential of your data. This comprehensive guide begins by examining the challenges and key concepts of data integration, such as managing huge volumes of data and dealing with the different data types. You’ll gain a deep understanding of the modern data stack and its architecture, as well as the pivotal role of open-source technologies in shaping the data landscape. Delving into the layers of the modern data stack, you’ll cover data sources, types, storage, integration techniques, transformation, and processing. The book also offers insights into data exposition and APIs, ingestion and storage strategies, data preparation and analysis, workflow management, monitoring, data quality, and governance. Packed with practical use cases, real-world examples, and a glimpse into the future of data integration, The Definitive Guide to Data Integration is an essential resource for data eclectics. By the end of this book, you’ll have the gained the knowledge and skills needed to optimize your data usage and excel in the ever-evolving world of data.What you will learn Discover the evolving architecture and technologies shaping data integration Process large data volumes efficiently with data warehousing Tackle the complexities of integrating large datasets from diverse sources Harness the power of data warehousing for efficient data storage and processing Design and optimize effective data integration solutions Explore data governance principles and compliance requirements Who this book is for This book is perfect for data engineers, data architects, data analysts, and IT professionals looking to gain a comprehensive understanding of data integration in the modern era. Whether you’re a beginner or an experienced professional enhancing your knowledge of the modern data stack, this definitive guide will help you navigate the data integration landscape. |
| data management use cases: Expanding Perspectives on Open Science: Communities, Cultures and Diversity in Concepts and Practices L. Chan, F. Loizides, 2017-06-20 Twenty-one years ago, the term ‘electronic publishing’ promised all manner of potential that the Web and network technologies could bring to scholarly communication, scientific research and technical innovation. Over the last two decades, tremendous developments have indeed taken place across all of these domains. One of the most important of these has been Open Science; perhaps the most widely discussed topic in research communications today. This book presents the proceedings of Elpub 2017, the 21st edition of the International Conference on Electronic Publishing, held in Limassol, Cyprus, in June 2017. Continuing the tradition of bringing together academics, publishers, lecturers, librarians, developers, entrepreneurs, users and all other stakeholders interested in the issues surrounding electronic publishing, this edition of the conference focuses on Open Science, and the 27 research and practitioner papers and 1 poster included here reflect the results and ideas of researchers and practitioners with diverse backgrounds from all around the world with regard to this important subject. Intended to generate discussion and debate on the potential and limitations of openness, the book addresses the current challenges and opportunities in the ecosystem of Open Science, and explores how to move forward in developing an inclusive system that will work for a much broader range of participants. It will be of interest to all those concerned with electronic publishing, and Open Science in particular. |
Data management and use: case studies of technologies and …
These case studies were prepared in support of the Data Management and Use: Governance in the 21st Century project, carried out by the British Academy and the Royal Society.
Data Lifecycle Management Use Case - Utility - Data …
• A Data Strategy that utilizes Use Case management to prioritize critical organizational data, and manages the data lifecycle from Suppliers through Consumers, is a more strategic way to …
Success Accelerators Customer Guide for Top IDMC Use …
•Data Governance Use Case Discovery •Data Democratization for Self Service Analytics •Getting Started & Deploy CDGC, CDQ and CDMP 02 DESIGN •Recommended Roles for Data …
State of Unstructured Data Management Report - Komprise
• The leading new approach for unstructured data management as identified in our survey is the ability to initiate and execute data workflows (43%). • The leading new use case for …
The Power of Data-Driven Asset Management I Accenture
firm’s data foundation is critical to facilitating data-powered business use cases and realizing tangible business value. Asset managers often struggle to take a firmwide view of data, so …
Geospatial Data AS a Service: Use Cases - FGDC
To illustrate these points, we examined four case studies that provide more in-depth use cases from across Federal and local agencies and the private sector. These case studies showcase …
Data Management Use Cases - software.xsede.org
Data Management Use Cases Version 2.5 July 31, 2019 These use cases describe how researchers manage research data. Use cases DM-01 through DM-04 and DM-11 describe …
DATA MANAGEMENT USE CASES IN FINANCIAL …
This whitepaper details a series of use cases and lessons learned around key imperatives related to the European Union’s BCBS 239 Principles for Effective Risk Data Aggregation and Risk …
Data Management Use Cases (2024)
Data Management Use Cases: Data Management: a gentle introduction Bas van Gils,2020-03-03 The overall objective of this book is to show that data management is an exciting and valuable …
USE CASEOVERVIEW - International Data Spaces
Use cases representing the cross-company business processes modified with the INTERNA-TIONAL DATA SPACES. The purpose is the identification, analysis and evaluation of require …
Next Generation Data Management: Case Studies for …
The following clinical data management use case highlights are described as evidence of the technology’s ability to address many clinical data management challenges. Use case 1 – …
Top Five High-Impact Use Cases for Big Data Analytics
Oct 3, 2020 · deployments, Datameer identified the top five use cases for big data that deliver the highest value. This eBook outlines those use cases and includes real-world customer …
Data management and use: governance in the 21st century
In this report, we consider data governance to mean everything designed to inform the extent of confidence in data management, data use and the technologies derived from it. We cannot …
architecture use cases AWS Prescriptive Guidance
In a data-centric archicture, data is a core IT asset, and you design your IT systems and processes to optimize your data. This guide offers best practices for designing a modern data …
WithUseCasesinEdge-CloudandBlockchainSystems
Overview of Distributed Data Management: Participants will be provided with a quick overview of distributed data management, focusing on critical areas such as ACID properties,
7 essential data use cases for startups - d1.awsstatic.com
understand how you can use data and AI to drive results in the targeted area. These seven solution areas offer prime opportunities to use your data to transform functions and capabilities …
The Top 5 Use Cases of Graph Databases
significant business value from your connected data? Here are the top five use cases of graph database technologies: TABLE OF CONTENTS Introduction 1 Fraud Detection 2 Real-Time …
Top Big Data Analytics Use Cases - Oracle
To get started on your big data journey, check out our top twenty-two big data use cases. Each use case ofers a real-world example of how companies are taking advantage of data insights …
Using Machine Learning In Data Quality Management - Infosys
• Prioritize data sources to meet consumption requirements • Standardize the source-to-target data acquisition process by de˜ning a common data model • Build a global data warehouse for …
What is Data Management and Why is it Important?
The primary data warehouse use cases are BI querying and enterprise reporting, which enable business analysts and executives to analyze sales, inventory management and other key …
Data management and use: case studies of technologies …
These case studies were prepared in support of the Data Management and Use: Governance in the 21st Century project, carried out by the British Academy and the Royal Society.
Data Lifecycle Management Use Case - Utility - Data …
• A Data Strategy that utilizes Use Case management to prioritize critical organizational data, and manages the data lifecycle from Suppliers through Consumers, is a more strategic way to …
Success Accelerators Customer Guide for Top IDMC Use …
•Data Governance Use Case Discovery •Data Democratization for Self Service Analytics •Getting Started & Deploy CDGC, CDQ and CDMP 02 DESIGN •Recommended Roles for Data …
State of Unstructured Data Management Report - Komprise
• The leading new approach for unstructured data management as identified in our survey is the ability to initiate and execute data workflows (43%). • The leading new use case for …
The Power of Data-Driven Asset Management I Accenture
firm’s data foundation is critical to facilitating data-powered business use cases and realizing tangible business value. Asset managers often struggle to take a firmwide view of data, so …
Geospatial Data AS a Service: Use Cases - FGDC
To illustrate these points, we examined four case studies that provide more in-depth use cases from across Federal and local agencies and the private sector. These case studies showcase …
Data Management Use Cases - software.xsede.org
Data Management Use Cases Version 2.5 July 31, 2019 These use cases describe how researchers manage research data. Use cases DM-01 through DM-04 and DM-11 describe …
DATA MANAGEMENT USE CASES IN FINANCIAL REGULATION
This whitepaper details a series of use cases and lessons learned around key imperatives related to the European Union’s BCBS 239 Principles for Effective Risk Data Aggregation and Risk …
Data Management Use Cases (2024)
Data Management Use Cases: Data Management: a gentle introduction Bas van Gils,2020-03-03 The overall objective of this book is to show that data management is an exciting and valuable …
USE CASEOVERVIEW - International Data Spaces
Use cases representing the cross-company business processes modified with the INTERNA-TIONAL DATA SPACES. The purpose is the identification, analysis and evaluation of require …
Next Generation Data Management: Case Studies for …
The following clinical data management use case highlights are described as evidence of the technology’s ability to address many clinical data management challenges. Use case 1 – …
Top Five High-Impact Use Cases for Big Data Analytics
Oct 3, 2020 · deployments, Datameer identified the top five use cases for big data that deliver the highest value. This eBook outlines those use cases and includes real-world customer …
Data management and use: governance in the 21st century
In this report, we consider data governance to mean everything designed to inform the extent of confidence in data management, data use and the technologies derived from it. We cannot …
architecture use cases AWS Prescriptive Guidance
In a data-centric archicture, data is a core IT asset, and you design your IT systems and processes to optimize your data. This guide offers best practices for designing a modern data …
WithUseCasesinEdge-CloudandBlockchainSystems
Overview of Distributed Data Management: Participants will be provided with a quick overview of distributed data management, focusing on critical areas such as ACID properties,
7 essential data use cases for startups - d1.awsstatic.com
understand how you can use data and AI to drive results in the targeted area. These seven solution areas offer prime opportunities to use your data to transform functions and capabilities …
The Top 5 Use Cases of Graph Databases
significant business value from your connected data? Here are the top five use cases of graph database technologies: TABLE OF CONTENTS Introduction 1 Fraud Detection 2 Real-Time …
Top Big Data Analytics Use Cases - Oracle
To get started on your big data journey, check out our top twenty-two big data use cases. Each use case ofers a real-world example of how companies are taking advantage of data insights …
Using Machine Learning In Data Quality Management - Infosys
• Prioritize data sources to meet consumption requirements • Standardize the source-to-target data acquisition process by de˜ning a common data model • Build a global data warehouse for …
What is Data Management and Why is it Important?
The primary data warehouse use cases are BI querying and enterprise reporting, which enable business analysts and executives to analyze sales, inventory management and other key …
