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| collibra reference data management: Data Governance and Data Management Rupa Mahanti, 2021-09-08 This book delves into the concept of data as a critical enterprise asset needed for informed decision making, compliance, regulatory reporting and insights into trends, behaviors, performance and patterns. With good data being key to staying ahead in a competitive market, enterprises capture and store exponential volumes of data. Considering the business impact of data, there needs to be adequate management around it to derive the best value. Data governance is one of the core data management related functions. However, it is often overlooked, misunderstood or confused with other terminologies and data management functions. Given the pervasiveness of data and the importance of data, this book provides comprehensive understanding of the business drivers for data governance and benefits of data governance, the interactions of data governance function with other data management functions and various components and aspects of data governance that can be facilitated by technology and tools, the distinction between data management tools and data governance tools, the readiness checks to perform before exploring the market to purchase a data governance tool, the different aspects that must be considered when comparing and selecting the appropriate data governance technologies and tools from large number of options available in the marketplace and the different market players that provide tools for supporting data governance. This book combines the data and data governance knowledge that the author has gained over years of working in different industrial and research programs and projects associated with data, processes and technologies with unique perspectives gained through interviews with thought leaders and data experts. This book is highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge and get guidance on implementing data governance in their own data initiatives. |
| collibra reference data management: Cloud Data Architectures Demystified Ashok Boddeda, 2023-09-27 Learn using Cloud data technologies for improving data analytics and decision-making capabilities for your organization KEY FEATURES ● Get familiar with the fundamentals of data architecture and Cloud computing. ● Design and deploy enterprise data architectures on the Cloud. ● Learn how to leverage AI/ML to gain insights from data. DESCRIPTION Cloud data architectures are a valuable tool for organizations that want to use data to make better decisions. By understanding the different components of Cloud data architectures and the benefits they offer, organizations can select the right architecture for their needs. This book is a holistic guide for using Cloud data technologies to ingest, transform, and analyze data. It covers the entire data lifecycle, from collecting data to transforming it into actionable insights. The readers will get a comprehensive overview of Cloud data technologies and AI/ML algorithms. The readers will learn how to use these technologies and algorithms to improve decision-making, optimize operations, and identify new opportunities. By the end of the book, you will have a comprehensive understanding of loud data architectures and the confidence to implement effective solutions that drive business success. WHAT YOU WILL LEARN ● Learn the fundamental principles of data architecture. ● Understand the working of different cloud ecosystems such as AWS, Azure & GCP. ● Explore different Snowflake data services. ● Learn how to implement data governance policies and procedures. ● Use artificial intelligence (AI) and machine learning (ML) to gain insights from data. WHO THIS BOOK IS FOR This book is for executives, IT professionals, and data enthusiasts who want to learn more about Cloud data architectures. It does not require any prior experience, but a basic understanding of data concepts and technology landscapes will be helpful. TABLE OF CONTENTS 1. Data Architectures and Patterns 2. Enterprise Data Architectures 3. Cloud Fundamentals 4. Azure Data Eco-system 5. AWS Data Services 6. Google Data Services 7. Snowflake Data Eco-system 8. Data Governance 9. Data Intelligence: AI-ML Modeling and Services |
| collibra reference data management: Data Governance Dimitrios Sargiotis, |
| collibra reference data management: Next-Generation Big Data Butch Quinto, 2018-06-12 Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies. Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data warehousing, data warehouse optimization, real-time and batch data ingestion and processing, real-time data visualization, big data governance, data wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big data case studies from Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard. What You’ll Learn Install Apache Kudu, Impala, and Spark to modernize enterprise data warehouse and business intelligence environments, complete with real-world, easy-to-follow examples, and practical advice Integrate HBase, Solr, Oracle, SQL Server, MySQL, Flume, Kafka, HDFS, and Amazon S3 with Apache Kudu, Impala, and Spark Use StreamSets, Talend, Pentaho, and CDAP for real-time and batch data ingestion and processing Utilize Trifacta, Alteryx, and Datameer for data wrangling and interactive data processing Turbocharge Spark with Alluxio, a distributed in-memory storage platform Deploy big data in the cloud using Cloudera Director Perform real-time data visualization and time series analysis using Zoomdata, Apache Kudu, Impala, and Spark Understand enterprise big data topics such as big data governance, metadata management, data lineage, impact analysis, and policy enforcement, and how to use Cloudera Navigator to perform common data governance tasks Implement big data use cases such as big data warehousing, data warehouse optimization, Internet of Things, real-time data ingestion and analytics, complex event processing, and scalable predictive modeling Study real-world big data case studies from innovative companies, including Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard Who This Book Is For BI and big data warehouse professionals interested in gaining practical and real-world insight into next-generation big data processing and analytics using Apache Kudu, Impala, and Spark; and those who want to learn more about other advanced enterprise topics |
| collibra reference data management: Managing Reference Data in Enterprise Databases Malcolm Chisholm, 2001 This is a great book! I have to admit I wasn't enthusiastic about the idea of a book with such a narrow topic initially, but, frankly, it's the first professional book I've read page to page in one sitting in a long time. It should be of interest to DBAs, data architects and modelers, programmers who have to write database programs, and yes, even managers. This book is a winner. - Karen Watterson, Editor SQL Server Professional Malcolm Chisholm has produced a very readable book. It is well-written and with excellent examples. It will, I am sure, become the Reference Book on Reference Data. - Clive Finkelstein, Father of Information Engineering, Managing Director, Information Engineering Services Pty Ltd Reference data plays a key role in your business databases and must be free from defects of any kind. So why is it so hard to find information on this critical topic? Recognizing the dangers of taking reference data for granted, Managing Reference Data in Enterprise Databases gives you precisely what you've been seeking: A complete guide to the implementation and management of reference data of all kinds. This book begins with a thorough definition of reference data, then proceeds with a detailed examination of all reference data issues, fully describing uses, common difficulties, and practical solutions. Whether you're a database manager, architect, administrator, programmer, or analyst, be sure to keep this easy-to-use reference close at hand. Features Solves special challenges associated with maintaining reference data. Addresses a wide range of reference data issues, including acronyms, redundancy, mapping, life cycles, multiple languages, and querying. Describes how reference data interacts with other system components, what problems can arise, and how to mitigate these problems. Offers examples of standard reference data types and matrices for evaluating management methods. Provides a number of standard reference data tables and more specialized material to help you deal with reference data, via a companion Web site |
| collibra reference data management: Business Metadata: Capturing Enterprise Knowledge W.H. Inmon, Bonnie O'Neil, Lowell Fryman, 2010-07-28 Business Metadata: Capturing Enterprise Knowledge is the first book that helps businesses capture corporate (human) knowledge and unstructured data, and offer solutions for codifying it for use in IT and management. Written by Bill Inmon, one of the fathers of the data warehouse and well-known author, the book is filled with war stories, examples, and cases from current projects. It includes a complete metadata acquisition methodology and project plan to guide readers every step of the way, and sample unstructured metadata for use in self-testing and developing skills. This book is recommended for IT professionals, including those in consulting, working on systems that will deliver better knowledge management capability. This includes people in these positions: data architects, data analysts, SOA architects, metadata analysts, repository (metadata data warehouse) managers as well as vendors that have a metadata component as part of their systems or tools. - First book that helps businesses capture corporate (human) knowledge and unstructured data, and offer solutions for codifying it for use in IT and management - Written by Bill Inmon, one of the fathers of the data warehouse and well-known author, and filled with war stories, examples, and cases from current projects - Very practical, includes a complete metadata acquisition methodology and project plan to guide readers every step of the way - Includes sample unstructured metadata for use in self-testing and developing skills |
| collibra reference data management: Mastering Data Warehousing Cybellium Ltd, Architect, Build, and Optimize Your Data Warehouse Are you ready to revolutionize the way your organization stores and accesses data? Mastering Data Warehousing is your definitive guide to architecting, building, and optimizing data warehouses that facilitate efficient data storage and retrieval. Whether you're a data architect designing robust warehouse structures or a business leader aiming to glean insights from your data, this book equips you with the knowledge and strategies to master the art of data warehousing. Key Features: 1. Architecting Data Warehouses: Immerse yourself in the world of data warehousing, understanding its significance, challenges, and opportunities. Build a strong foundation that empowers you to design data warehouses that cater to your organization's needs. 2. Data Warehouse Models: Master various data warehouse models. Learn about star schema, snowflake schema, and other dimensional modeling techniques for organizing data for efficient querying and analysis. 3. Data ETL (Extract, Transform, Load): Uncover the power of ETL processes in data warehousing. Explore techniques for extracting data from diverse sources, transforming it for analysis, and loading it into your warehouse. 4. Data Quality and Governance: Delve into data quality and governance within data warehousing. Learn how to ensure data accuracy, consistency, and compliance within your warehouse. 5. Optimizing Query Performance: Master techniques for optimizing query performance. Learn about indexing, partitioning, and materialized views to enhance query speed and responsiveness. 6. Scalability and High Availability: Explore strategies for scaling and ensuring high availability of your data warehouse. Learn how to handle growing data volumes and ensure uninterrupted access to critical information. 7. Cloud Data Warehousing: Discover the world of cloud data warehousing. Learn about designing and migrating data warehouses to cloud platforms, enabling scalability and cost-efficiency. 8. Data Warehousing Tools and Platforms: Uncover a range of tools and platforms for data warehousing. Explore traditional solutions as well as modern technologies like columnar databases and data lakes. 9. Real-Time Data Warehousing: Dive into real-time data warehousing techniques. Learn how to capture and process streaming data for instant insights and decision-making. 10. Real-World Applications: Gain insights into real-world use cases of data warehousing across industries. From business intelligence to customer analytics, discover how organizations leverage data warehouses for strategic advantage. Who This Book Is For: Mastering Data Warehousing is an essential resource for data architects, analysts, and business professionals aiming to excel in designing and managing data warehouses. Whether you're enhancing your technical skills or transforming data into actionable insights, this book will guide you through the intricacies and empower you to harness the full potential of data warehousing. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
| collibra reference data management: Legal Data for Banking Akber Datoo, 2019-06-17 A practical, informative guide to banks’ major weakness Legal Data for Banking defines the legal data domain in the context of financial institutions, and describes how banks can leverage these assets to optimise business lines and effectively manage risk. Legal data is at the heart of post-2009 regulatory reform, and practitioners need to deepen their grasp of legal data management in order to remain compliant with new rules focusing on transparency in trade and risk reporting. This book provides essential information for IT, project management and data governance leaders, with detailed discussion of current and best practices. Many banks are experiencing recurrent pain points related to legal data management issues, so clear explanations of the required processes, systems and strategic governance provide immediately-relevant relief. The recent financial crisis following the collapse of major banks had roots in poor risk data management, and the regulators’ unawareness of accumulated systemic risk stemming from contractual obligations between firms. To avoid repeating history, today’s banks must be proactive in legal data management; this book provides the critical knowledge practitioners need to put the necessary systems and practices in place. Learn how current legal data management practices are hurting banks Understand the systems, structures and strategies required to manage risk and optimise business lines Delve into the regulations surrounding risk aggregation, netting, collateral enforceability and more Gain practical insight on legal data technology, systems and migration The legal contracts between firms contain significant obligations that underpin the financial markets; failing to recognise these terms as valuable data assets means increased risk exposure and untapped business lines. Legal Data for Banking provides critical information for the banking industry, with actionable guidance for implementation. |
| collibra reference data management: Creating and Sustaining an Information Governance Program Helge, Kris, Rookey, Caitlin A., 2024-04-26 We live in an era defined by data proliferation and digital transformation, and the effective management of information has become a concern for organizations across the globe. Creating and Sustaining an Information Governance Program is a comprehensive academic guide that delves into the intricate realm of Information Governance (IG), focusing on the key components and strategies essential for establishing and perpetuating a robust IG program. This book elucidates the intricacies of establishing and nurturing an information governance program, and it equips readers with the knowledge and tools to navigate the challenges and opportunities inherent in this endeavor. It delves into the cultural shifts, communication strategies, and training methods necessary for success. It emphasizes the vital importance of collaboration across organizational silos, the cultivation of administrative support, securing appropriate funding, and educating stakeholders on the purpose and benefits of an IG program. This book is ideal for individuals across academia, corporate sectors, government agencies, and for-profit and not-for-profit organizations. Its insights are universally applicable, spanning industries such as law firms, general corporate environments, government entities, educational institutions, and businesses of all sizes. Creating and Sustaining an Information Governance Program guides organizations of all stripes toward effective information governance, compliance, and risk mitigation in a data-centric world. |
| collibra reference data management: Modern Enterprise Business Intelligence and Data Management Alan Simon, 2014-08-28 Nearly every large corporation and governmental agency is taking a fresh look at their current enterprise-scale business intelligence (BI) and data warehousing implementations at the dawn of the Big Data Era...and most see a critical need to revitalize their current capabilities. Whether they find the frustrating and business-impeding continuation of a long-standing silos of data problem, or an over-reliance on static production reports at the expense of predictive analytics and other true business intelligence capabilities, or a lack of progress in achieving the long-sought-after enterprise-wide single version of the truth – or all of the above – IT Directors, strategists, and architects find that they need to go back to the drawing board and produce a brand new BI/data warehousing roadmap to help move their enterprises from their current state to one where the promises of emerging technologies and a generation's worth of best practices can finally deliver high-impact, architecturally evolvable enterprise-scale business intelligence and data warehousing. Author Alan Simon, whose BI and data warehousing experience dates back to the late 1970s and who has personally delivered or led more than thirty enterprise-wide BI/data warehousing roadmap engagements since the mid-1990s, details a comprehensive step-by-step approach to building a best practices-driven, multi-year roadmap in the quest for architecturally evolvable BI and data warehousing at the enterprise scale. Simon addresses the triad of technology, work processes, and organizational/human factors considerations in a manner that blends the visionary and the pragmatic. - Takes a fresh look at true enterprise-scale BI/DW in the Dawn of the Big Data Era - Details a checklist-based approach to surveying one's current state and identifying which components are enterprise-ready and which ones are impeding the key objectives of enterprise-scale BI/DW - Provides an approach for how to analyze and test-bed emerging technologies and architectures and then figure out how to include the relevant ones in the roadmaps that will be developed - Presents a tried-and-true methodology for building a phased, incremental, and iterative enterprise BI/DW roadmap that is closely aligned with an organization's business imperatives, organizational culture, and other considerations |
| collibra reference data management: Rewired Eric Lamarre, Kate Smaje, Rodney Zemmel, 2023-06-13 In Rewired, the world's most influential management consulting firm, McKinsey & Company, delivers a road-tested, how-to manual their own consultants use to help companies build the capabilities to outcompete in the age of digital and AI. Many companies are stuck with digital transformations that are not moving the needle. There are no quick fixes but there is a playbook. The answer is in rewiring your business so hundreds, thousands, of teams can harness technology to continuously create great customer experiences, lower unit costs, and generate value. It's the capabilities of the organization that win the race. McKinsey Digital's top leaders Eric Lamarre, Kate Smaje and Rodney W. Zemmel provide proven how-to details on what it takes in six comprehensive sections – creating the transformation roadmap, building a talent bench, adopting a new operating model, producing a distributed technology environment so teams can innovate, embedding data everywhere, and unlocking user adoption and enterprise scaling. Tested, iterated, reworked, and tested again over the years, McKinsey's digital and AI transformation playbook is captured in the pages of Rewired. It contains diagnostic assessments, operating model designs, technology and data architecture diagrams, how-to checklists, best practices and detailed implementation methods, all exemplified with demonstrated case studies and illustrated with 100+ exhibits. Rewired is for leaders who are ready to roll up their sleeves and do the hard work needed to rewire their company for long-term success. |
| collibra reference data management: 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. |
| collibra reference data management: 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 |
| collibra reference data management: Non-Invasive Data Governance Robert S. Seiner, 2014-09-01 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. |
| collibra reference data management: Multi-Domain Master Data Management Mark Allen, Dalton Cervo, 2015-03-21 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. |
| collibra reference data management: Information Technology: New Generations Shahram Latifi, 2016-03-28 This book collects articles presented at the 13th International Conference on Information Technology- New Generations, April, 2016, in Las Vegas, NV USA. It includes over 100 chapters on critical areas of IT including Web Technology, Communications, Security, and Data Mining. |
| collibra reference data management: 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 |
| collibra reference data management: Data Stewardship David Plotkin, 2013-09-16 Data stewards in business and IT are the backbone of a successful data governance implementation because they do the work to make a company's data trusted, dependable, and high quality. Data Stewardship explains everything you need to know to successfully implement the stewardship portion of data governance, including how to organize, train, and work with data stewards, get high-quality business definitions and other metadata, and perform the day-to-day tasks using a minimum of the steward's time and effort. David Plotkin has loaded this book with practical advice on stewardship so you can get right to work, have early successes, and measure and communicate those successes, gaining more support for this critical effort. - Provides clear and concise practical advice on implementing and running data stewardship, including guidelines on how to organize based on company structure, business functions, and data ownership - Shows how to gain support for your stewardship effort, maintain that support over the long-term, and measure the success of the data stewardship effort and report back to management - Includes detailed lists of responsibilities for each type of data steward and strategies to help the Data Governance Program Office work effectively with the data stewards |
| collibra reference data management: On the Move to Meaningful Internet Systems: OTM 2019 Workshops Christophe Debruyne, Hervé Panetto, Wided Guédria, Peter Bollen, Ioana Ciuciu, George Karabatis, Robert Meersman, 2020-02-12 This volume constitutes the refereed proceedings of the Confederated International International Workshop on Enterprise Integration, Interoperability and Networking (EI2N ), Fact Based Modeling ( FBM), Industry Case Studies Program ( ICSP ), International Workshop on Methods, Evaluation, Tools and Applications for the Creation and Consumption of Structured Data for the e-Society (Meta4eS) and, 1st International Workshop on Security via Information Analytics and Applications (SIAnA 2019) held as part of OTM 2018 in October 2019 in Rhodes, Greece. As the three main conferences and the associated workshops all share the distributed aspects of modern computing systems, they experience the application pull created by the Internet and by the so-called Semantic Web, in particular developments of Big Data, increased importance of security issues, and the globalization of mobile-based technologies. |
| collibra reference data management: 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 |
| collibra reference data management: Self-Service Analytics Simplified Arshad Khan, 2019-07-10 Self-Service Analytics Simplified: How to Plan and Implement will introduce you to self-service analytics (SSA), which aims to make business users less dependent on IT for their reporting and analytics needs. This book, which teaches how to plan and implement an SSA project, will appeal to a broad range of users including senior executives, business and IT managers, project managers, data analysts, business analysts, developers, casual users, as well as IT professionals. The topics covered in Self-Service Analytics Simplified: How to Plan and Implement include an introduction to self-service analytics, relationship with BI, benefits for different types of users, readiness assessment, planning, data-related topics including metadata and data pipelining, architecture, tools, requirements, implementation, data governance, security, training, data and user onboarding, and barriers to adoption, as well as challenges, best practices, lessons, and tips. |
| collibra reference data management: Practical DataOps Harvinder Atwal, 2019-12-09 Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles. This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will LearnDevelop a data strategy for your organization to help it reach its long-term goals Recognize and eliminate barriers to delivering data to users at scale Work on the right things for the right stakeholders through agile collaboration Create trust in data via rigorous testing and effective data management Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes Create cross-functional self-organizing teams focused on goals not reporting lines Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products Who This Book Is For Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production. |
| collibra reference data management: Multidimensional Databases and Data Warehousing Christian S. Jensen, Torben Bach Pedersen, Christian Thomsen, 2010 The present book's subject is multidimensional data models and data modeling concepts as they are applied in real data warehouses. The book aims to present the most important concepts within this subject in a precise and understandable manner. The book's coverage of fundamental concepts includes data cubes and their elements, such as dimensions, facts, and measures and their representation in a relational setting; it includes architecture-related concepts; and it includes the querying of multidimensional databases. The book also covers advanced multidimensional concepts that are considered to be particularly important. This coverage includes advanced dimension-related concepts such as slowly changing dimensions, degenerate and junk dimensions, outriggers, parent-child hierarchies, and unbalanced, non-covering, and non-strict hierarchies. The book offers a principled overview of key implementation techniques that are particularly important to multidimensional databases, including materialized views, bitmap indices, join indices, and star join processing. The book ends with a chapter that presents the literature on which the book is based and offers further readings for those readers who wish to engage in more in-depth study of specific aspects of the book's subject. Table of Contents: Introduction / Fundamental Concepts / Advanced Concepts / Implementation Issues / Further Readings |
| collibra reference data management: What's Your Digital Business Model? Peter Weill, Stephanie Woerner, 2018-04-17 Digital transformation is not about technology--it's about change. In the rapidly changing digital economy, you can't succeed by merely tweaking management practices that led to past success. And yet, while many leaders and managers recognize the threat from digital--and the potential opportunity--they lack a common language and compelling framework to help them assess it and guide them in responding. They don't know how to think about their digital business model. In this concise, practical book, MIT digital research leaders Peter Weill and Stephanie Woerner provide a powerful yet straightforward framework that has been field-tested globally with dozens of senior management teams. Based on years of study at the MIT Center for Information Systems Research (CISR), the authors find that digitization is moving companies' business models on two dimensions: from value chains to digital ecosystems, and from a fuzzy understanding of the needs of end customers to a sharper one. Looking at these dimensions in combination results in four distinct business models, each with different capabilities. The book then sets out six driving questions, in separate chapters, that help managers and executives clarify where they are currently in an increasingly digital business landscape and highlight what's needed to move toward a higher-value digital business model. Filled with straightforward self-assessments, motivating examples, and sharp financial analyses of where profits are made, this smart book will help you tackle the threats, leverage the opportunities, and create winning digital strategies. |
| collibra reference data management: Data Strategy Sid Adelman, Larissa Terpeluk Moss, Majid Abai, 2005 Without a data strategy, the people within an organization have no guidelines for making decisions that are absolutely crucial to the success of the IT organization and to the entire organization. The absence of a strategy gives a blank check to those who want to pursue their own agendas, including those who want to try new database management systems, new technologies (often unproven), and new tools. This type of environment provides no hope for success. Data Strategy should result in the development of systems with less risk, higher quality systems, and reusability of assets. This is key to keeping cost and maintenance down, thus running lean and mean. Data Strategy provides a CIO with a rationale to counter arguments for immature technology and data strategies that are inconsistent with existing strategies. This book uses case studies and best practices to give the reader the tools they need to create the best strategy for the organization. |
| collibra reference data management: Hands-On Big Data Modeling James Lee, Tao Wei, Suresh Kumar Mukhiya, 2018-11-30 Solve all big data problems by learning how to create efficient data models Key FeaturesCreate effective models that get the most out of big dataApply your knowledge to datasets from Twitter and weather data to learn big dataTackle different data modeling challenges with expert techniques presented in this bookBook Description Modeling and managing data is a central focus of all big data projects. In fact, a database is considered to be effective only if you have a logical and sophisticated data model. This book will help you develop practical skills in modeling your own big data projects and improve the performance of analytical queries for your specific business requirements. To start with, you’ll get a quick introduction to big data and understand the different data modeling and data management platforms for big data. Then you’ll work with structured and semi-structured data with the help of real-life examples. Once you’ve got to grips with the basics, you’ll use the SQL Developer Data Modeler to create your own data models containing different file types such as CSV, XML, and JSON. You’ll also learn to create graph data models and explore data modeling with streaming data using real-world datasets. By the end of this book, you’ll be able to design and develop efficient data models for varying data sizes easily and efficiently. What you will learnGet insights into big data and discover various data modelsExplore conceptual, logical, and big data modelsUnderstand how to model data containing different file typesRun through data modeling with examples of Twitter, Bitcoin, IMDB and weather data modelingCreate data models such as Graph Data and Vector SpaceModel structured and unstructured data using Python and RWho this book is for This book is great for programmers, geologists, biologists, and every professional who deals with spatial data. If you want to learn how to handle GIS, GPS, and remote sensing data, then this book is for you. Basic knowledge of R and QGIS would be helpful. |
| collibra reference data management: Designing Cloud Data Platforms Danil Zburivsky, Lynda Partner, 2021-04-20 Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is an hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you''ll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You''ll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyse it. about the technology Access to affordable, dependable, serverless cloud services has revolutionized the way organizations can approach data management, and companies both big and small are raring to migrate to the cloud. But without a properly designed data platform, data in the cloud can remain just as siloed and inaccessible as it is today for most organizations. Designing Cloud Data Platforms lays out the principles of a well-designed platform that uses the scalable resources of the public cloud to manage all of an organization''s data, and present it as useful business insights. about the book In Designing Cloud Data Platforms, you''ll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform. Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs. Starting with foundational principles, you''ll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors. As you progress past the basics you''ll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more. what''s inside The tools of different public cloud for implementing data platforms Best practices for managing structured and unstructured data sets Machine learning tools that can be used on top of the cloud Cost optimization techniques about the reader For data professionals familiar with the basics of cloud computing and distributed data processing systems like Hadoop and Spark. about the authors Danil Zburivsky has over 10 years experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years. |
| collibra reference data management: 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. |
| collibra reference data management: 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. |
| collibra reference data management: Controlling Privacy and the Use of Data Assets - Volume 2 Ulf Mattsson, 2023-08-24 The book will review how new and old privacy-preserving techniques can provide practical protection for data in transit, use, and rest. We will position techniques like Data Integrity and Ledger and will provide practical lessons in Data Integrity, Trust, and data’s business utility. Based on a good understanding of new and old technologies, emerging trends, and a broad experience from many projects in this domain, this book will provide a unique context about the WHY (requirements and drivers), WHAT (what to do), and HOW (how to implement), as well as reviewing the current state and major forces representing challenges or driving change, what you should be trying to achieve and how you can do it, including discussions of different options. We will also discuss WHERE (in systems) and WHEN (roadmap). Unlike other general or academic texts, this book is being written to offer practical general advice, outline actionable strategies, and include templates for immediate use. It contains diagrams needed to describe the topics and Use Cases and presents current real-world issues and technological mitigation strategies. The inclusion of the risks to both owners and custodians provides a strong case for why people should care. This book reflects the perspective of a Chief Technology Officer (CTO) and Chief Security Strategist (CSS). The Author has worked in and with startups and some of the largest organizations in the world, and this book is intended for board members, senior decision-makers, and global government policy officials—CISOs, CSOs, CPOs, CTOs, auditors, consultants, investors, and other people interested in data privacy and security. The Author also embeds a business perspective, answering the question of why this an important topic for the board, audit committee, and senior management regarding achieving business objectives, strategies, and goals and applying the risk appetite and tolerance. The focus is on Technical Visionary Leaders, including CTO, Chief Data Officer, Chief Privacy Officer, EVP/SVP/VP of Technology, Analytics, Data Architect, Chief Information Officer, EVP/SVP/VP of I.T., Chief Information Security Officer (CISO), Chief Risk Officer, Chief Compliance Officer, Chief Security Officer (CSO), EVP/SVP/VP of Security, Risk Compliance, and Governance. It can also be interesting reading for privacy regulators, especially those in developed nations with specialist privacy oversight agencies (government departments) across their jurisdictions (e.g., federal and state levels). |
| collibra reference data management: The Data and Analytics Playbook Lowell Fryman, Gregory Lampshire, Dan Meers, 2016-08-12 The Data and Analytics Playbook: Proven Methods for Governed Data and Analytic Quality explores the way in which data continues to dominate budgets, along with the varying efforts made across a variety of business enablement projects, including applications, web and mobile computing, big data analytics, and traditional data integration. The book teaches readers how to use proven methods and accelerators to break through data obstacles to provide faster, higher quality delivery of mission critical programs. Drawing upon years of practical experience, and using numerous examples and an easy to understand playbook, Lowell Fryman, Gregory Lampshire, and Dan Meers discuss a simple, proven approach to the execution of multiple data oriented activities. In addition, they present a clear set of methods to provide reliable governance, controls, risk, and exposure management for enterprise data and the programs that rely upon it. In addition, they discuss a cost-effective approach to providing sustainable governance and quality outcomes that enhance project delivery, while also ensuring ongoing controls. Example activities, templates, outputs, resources, and roles are explored, along with different organizational models in common use today and the ways they can be mapped to leverage playbook data governance throughout the organization. - Provides a mature and proven playbook approach (methodology) to enabling data governance that supports agile implementation - Features specific examples of current industry challenges in enterprise risk management, including anti-money laundering and fraud prevention - Describes business benefit measures and funding approaches using exposure based cost models that augment risk models for cost avoidance analysis and accelerated delivery approaches using data integration sprints for application, integration, and information delivery success |
| collibra reference data management: Data Governance Evren Eryurek, Uri Gilad, Jessi Ashdown, Valliappa Lakshmanan, Anita Kibunguchy, 2021-04-13 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 |
| collibra reference data management: Trino: The Definitive Guide Matt Fuller, Manfred Moser, Martin Traverso, 2021-04-14 Perform fast interactive analytics against different data sources using the Trino high-performance distributed SQL query engine. With this practical guide, you'll learn how to conduct analytics on data where it lives, whether it's Hive, Cassandra, a relational database, or a proprietary data store. Analysts, software engineers, and production engineers will learn how to manage, use, and even develop with Trino. Initially developed by Facebook, open source Trino is now used by Netflix, Airbnb, LinkedIn, Twitter, Uber, and many other companies. Matt Fuller, Manfred Moser, and Martin Traverso show you how a single Trino query can combine data from multiple sources to allow for analytics across your entire organization. Get started: Explore Trino's use cases and learn about tools that will help you connect to Trino and query data Go deeper: Learn Trino's internal workings, including how to connect to and query data sources with support for SQL statements, operators, functions, and more Put Trino in production: Secure Trino, monitor workloads, tune queries, and connect more applications; learn how other organizations apply Trino |
| collibra reference data management: Artificial Intelligence Applications and Innovations. AIAI 2024 IFIP WG 12.5 International Workshops Ilias Maglogiannis, |
| collibra reference data management: Information Governance Robert F. Smallwood, 2014-04-21 Proven and emerging strategies for addressing document and records management risk within the framework of information governance principles and best practices Information Governance (IG) is a rapidly emerging super discipline and is now being applied to electronic document and records management, email, social media, cloud computing, mobile computing, and, in fact, the management and output of information organization-wide. IG leverages information technologies to enforce policies, procedures and controls to manage information risk in compliance with legal and litigation demands, external regulatory requirements, and internal governance objectives. Information Governance: Concepts, Strategies, and Best Practices reveals how, and why, to utilize IG and leverage information technologies to control, monitor, and enforce information access and security policies. Written by one of the most recognized and published experts on information governance, including specialization in e-document security and electronic records management Provides big picture guidance on the imperative for information governance and best practice guidance on electronic document and records management Crucial advice and insights for compliance and risk managers, operations managers, corporate counsel, corporate records managers, legal administrators, information technology managers, archivists, knowledge managers, and information governance professionals IG sets the policies that control and manage the use of organizational information, including social media, mobile computing, cloud computing, email, instant messaging, and the use of e-documents and records. This extends to e-discovery planning and preparation. Information Governance: Concepts, Strategies, and Best Practices provides step-by-step guidance for developing information governance strategies and practices to manage risk in the use of electronic business documents and records. |
| collibra reference data management: Innovative Mobile and Internet Services in Ubiquitous Computing Leonard Barolli, Fatos Xhafa, Omar K. Hussain, 2019-06-18 This book highlights the latest research findings, methods and techniques, as well as challenges and solutions related to Ubiquitous and Pervasive Computing (UPC). In this regard, it employs both theoretical and practical perspectives, and places special emphasis on innovative, mobile and internet services. With the proliferation of wireless technologies and electronic devices, there is a rapidly growing interest in Ubiquitous and Pervasive Computing (UPC). UPC makes it possible to create a human-oriented computing environment in which computer chips are embedded in everyday objects and interact with the physical world. Through UPC, people can remain online even while underway, thus enjoying nearly permanent access to their preferred services. Though it has a great potential to revolutionize our lives, UPC also poses a number of new research challenges. |
| collibra reference data management: Handbook on Business Process Management 2 Jan vom Brocke, Michael Rosemann, 2014-09-30 Business Process Management (BPM) has become one of the most widely used approaches for the design of modern organizational and information systems. The conscious treatment of business processes as significant corporate assets has facilitated substantial improvements in organizational performance but is also used to ensure the conformance of corporate activities. This Handbook presents in two volumes the contemporary body of knowledge as articulated by the world's leading BPM thought leaders. This second volume focuses on the managerial and organizational challenges of BPM such as strategic and cultural alignment, governance and the education of BPM stakeholders. As such, this book provides concepts and methodologies for the integration of BPM. Each chapter has been contributed by leading international experts. Selected case studies complement their views and lead to a summary of BPM expertise that is unique in its coverage of the most critical success factors of BPM. The second edition of this handbook has been significantly revised and extended. Each chapter has been updated to reflect the most current developments. This includes in particular new technologies such as in-memory data and process management, social media and networks. A further focus of this revised and extended edition is on the actual deployment of the proposed theoretical concepts. This volume includes a number of entire new chapters from some of the world's leading experts in the domain of BPM. The practice of Business Process Management has progressed significantly since Michael Hammer and I wrote the Reengineering book. This handbook presents the most complete description of the competencies required for BPM and exhaustively describes what we have learned about process management in the last 20 years. Jim Champy (Co-Author of the Best-Seller B́usiness Process Reengineering ́by Michael Hammer and Jim Champy) |
| collibra reference data management: Data Governance Tools Sunil Soares, 2015-02 Data governance programs often start off using programs such as Microsoft Excel and Microsoft SharePoint to document and share data governance artifacts. But these tools often lack critical functionality. Meanwhile, vendors have matured their data governance offerings to the extent that today's organizations need to consider tools as a critical component of their data governance programs. In this book, data governance expert Sunil Soares reviews the Enterprise Data Management (EDM) reference architecture and discusses key data governance tasks that can be automated by tools for business glossa. |
| collibra reference data management: Corporate Innovation in the Fifth Era Matthew C. Le Merle, Alison Davis, 2017-05-19 The companies who have been most able to tap into new innovations have become the most highly valued companies in the world. To do so, they have created a new approach to corporate innovation. In this book, Silicon Valley insiders share the lessons they have learned from two decades of interaction with today's most valuable companies. |
| collibra reference data management: 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 |
Collibra Reference Data Management - Saturn
Reference Data Management - Simple Steps to Win, Insights and Opportunities for Maxing Out Success Gerard Blokdijk,2015-12-09 Starting out with Reference Data Management means …
DHS Collibra
Synaptein Solutions proposes a comprehensive three-year plan for DHS’s Collibra implementation, drawing on our experience as a Collibra partner and insights from the DHS …
Integration of Microsoft Azure Data Catalog and Collibra
Integration of Microsoft Azure Data Catalog and Collibra Information Asset has developed a solution to transfer metadata between Azure Data Catalog and Collibra Data Governance …
DATA GOVERNANCE CENTER - Collibra
This module gives an overview of approaches and best practices to organize data governance in terms of communities , domains , and roles . We setup your DG
Collibra Data Intelligence Platform for Energy and Utilities
Energy and utilities providers globally rely on Collibra Data Intelligence Platform to gain in-depth visibility into their data ecosystem across on-premises, hybrid and multi-cloud environments, …
Collibra Data Governance product slides - DATAVERSITY
The Forrester WaveTM is a graphical representation of Forrester’s call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does …
Data Governance Center - bloorresearch.com
Collibra specialises in data governance software and support-ing data stewardship for structured data. As such it is the only vendor that specifically focuses on this market to the exclusion of …
REALIZE THE FULL POTENTIAL OF YOUR DATA WITH DATA …
Data governance tools today focus on ten key pillars to ensure effective management and utilization of data: Data Quality Management maintains accuracy and reliability; Metadata …
Unlock the Full Potential of Collibra
By prioritizing Collibra user roles heavily involved in data management, you enhance adoption and maximize return on investment — key factors leadership considers during contract renewals.
Collibra Reference Data Management (book) - archive.ncarb.org
reference data Addresses a wide range of reference data issues including acronyms redundancy mapping life cycles multiple languages and querying Describes how reference data interacts …
Collibra Reference Data Management (book) - archive.ncarb.org
experiences from previous Reference Data Management changes This is where this book is your guide and roadmap You will be able to relate to the experiences laid out in its resources …
Data Cataloging with Collibra: Enhancing Data Discovery and …
Collibra, a well-known data governance and cataloging platform, has played a critical role in enabling comprehensive solutions for metadata management, data lineage tracking, data …
Engage Focus Nine Best Practices for Driving [Enterprise] …
Using Collibra will increase trust in data. Data in Collibra will be identified and developed by Subject Matter Experts that are already trusted by data users. That data will be vetted and …
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Collibra is a data catalog platform and tool that helps organizations better understand and manage their data assets. Collibra helps create an inventory of data assets, capture …
Make the case for a predictive and self-service data quality tool
Collibra’s new-generation data quality tool has had a positive impact on productivity through self-service and embedding quality rules more broadly. Without designated resources and a …
Collibra Data Governance Training - multisoftsystems.com
Collibra Data Governance training by Multisoft Systems is designed to empower professionals with the skills and knowledge required to effectively manage and govern organizational data. …
Collibra Data Intelligence Platform
easy to find, understand and access, so you can do more with your data. With a best-in-class catalog, flexible governance, automated lineage, continuous quality and observability, and built …
Creating Synergy Between Data Governance and Data Quality …
Collibra DGC provides a broad data governance framework in which you can establish data owners and stewards, policies and rules, business terms, and view key dashboards on the …
Collibra Reference Data Management (PDF)
data via a companion Web site Reference Data Management - Simple Steps to Win, Insights and Opportunities for Maxing Out Success Gerard Blokdijk,2015-12-09 Starting out with Reference …
Experian Data Quality management for Collibra - Collibra …
With Experian’s Data Quality Management platform, Aperture Data Studio, data owners, consumers and processors can ensure that their critical data elements governed through …
Collibra Reference Data Management - Saturn
Reference Data Management - Simple Steps to Win, Insights and Opportunities for Maxing Out Success Gerard Blokdijk,2015-12-09 Starting out with Reference Data Management means …
DHS Collibra
Synaptein Solutions proposes a comprehensive three-year plan for DHS’s Collibra implementation, drawing on our experience as a Collibra partner and insights from the DHS …
Integration of Microsoft Azure Data Catalog and Collibra
Integration of Microsoft Azure Data Catalog and Collibra Information Asset has developed a solution to transfer metadata between Azure Data Catalog and Collibra Data Governance …
DATA GOVERNANCE CENTER - Collibra
This module gives an overview of approaches and best practices to organize data governance in terms of communities , domains , and roles . We setup your DG
Collibra Data Intelligence Platform for Energy and Utilities
Energy and utilities providers globally rely on Collibra Data Intelligence Platform to gain in-depth visibility into their data ecosystem across on-premises, hybrid and multi-cloud environments, …
Collibra Data Governance product slides - DATAVERSITY
The Forrester WaveTM is a graphical representation of Forrester’s call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does …
Data Governance Center - bloorresearch.com
Collibra specialises in data governance software and support-ing data stewardship for structured data. As such it is the only vendor that specifically focuses on this market to the exclusion of …
REALIZE THE FULL POTENTIAL OF YOUR DATA WITH DATA …
Data governance tools today focus on ten key pillars to ensure effective management and utilization of data: Data Quality Management maintains accuracy and reliability; Metadata …
Unlock the Full Potential of Collibra
By prioritizing Collibra user roles heavily involved in data management, you enhance adoption and maximize return on investment — key factors leadership considers during contract renewals.
Collibra Reference Data Management (book)
reference data Addresses a wide range of reference data issues including acronyms redundancy mapping life cycles multiple languages and querying Describes how reference data interacts …
Collibra Reference Data Management (book)
experiences from previous Reference Data Management changes This is where this book is your guide and roadmap You will be able to relate to the experiences laid out in its resources …
Data Cataloging with Collibra: Enhancing Data Discovery and …
Collibra, a well-known data governance and cataloging platform, has played a critical role in enabling comprehensive solutions for metadata management, data lineage tracking, data …
Engage Focus Nine Best Practices for Driving [Enterprise] …
Using Collibra will increase trust in data. Data in Collibra will be identified and developed by Subject Matter Experts that are already trusted by data users. That data will be vetted and …
How Datagaps’ Integration with Collibra Enhanced Data …
Collibra is a data catalog platform and tool that helps organizations better understand and manage their data assets. Collibra helps create an inventory of data assets, capture …
Make the case for a predictive and self-service data quality tool
Collibra’s new-generation data quality tool has had a positive impact on productivity through self-service and embedding quality rules more broadly. Without designated resources and a …
Collibra Data Governance Training - multisoftsystems.com
Collibra Data Governance training by Multisoft Systems is designed to empower professionals with the skills and knowledge required to effectively manage and govern organizational data. …
Collibra Data Intelligence Platform
easy to find, understand and access, so you can do more with your data. With a best-in-class catalog, flexible governance, automated lineage, continuous quality and observability, and …
Creating Synergy Between Data Governance and Data Quality …
Collibra DGC provides a broad data governance framework in which you can establish data owners and stewards, policies and rules, business terms, and view key dashboards on the …
Collibra Reference Data Management (PDF)
data via a companion Web site Reference Data Management - Simple Steps to Win, Insights and Opportunities for Maxing Out Success Gerard Blokdijk,2015-12-09 Starting out with Reference …
Experian Data Quality management for Collibra - Collibra …
With Experian’s Data Quality Management platform, Aperture Data Studio, data owners, consumers and processors can ensure that their critical data elements governed through …
