Master Data Management Whitepaper.Indd
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Data Center (CDP-DC) Reference Architecture
Cloudera Data Platform - Data Center (CDP-DC) Reference Architecture Important Notice © 2010-2020 Cloudera, Inc. All rights reserved. Cloudera, the Cloudera logo, and any other product or service names or slogans contained in this document, except as otherwise disclaimed, are trademarks of Cloudera and its suppliers or licensors, and may not be copied, imitated or used, in whole or in part, without the prior written permission of Cloudera or the applicable trademark holder. Hadoop and the Hadoop elephant logo are trademarks of the Apache Software Foundation. All other trademarks, registered trademarks, product names and company names or logos mentioned in this document are the property of their respective owners to any products, services, processes or other information, by trade name, trademark, manufacturer, supplier or otherwise does not constitute or imply endorsement, sponsorship or recommendation thereof by us. Complying with all applicable copyright laws is the responsibility of the user. Without limiting the rights under copyright, no part of this document may be reproduced, stored in or introduced into a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording, or otherwise), or for any purpose, without the express written permission of Cloudera. Cloudera may have patents, patent applications, trademarks, copyrights, or other intellectual property rights covering subject matter in this document. Except as expressly provided in any written license agreement from Cloudera, the furnishing of this document does not give you any license to these patents, trademarks copyrights, or other intellectual property. The information in this document is subject to change without notice. Cloudera shall not be liable for any damages resulting from technical errors or omissions which may be present in this document, or from use of this document. -
Data Warehouse Fundamentals for Storage Professionals – What You Need to Know EMC Proven Professional Knowledge Sharing 2011
Data Warehouse Fundamentals for Storage Professionals – What You Need To Know EMC Proven Professional Knowledge Sharing 2011 Bruce Yellin Advisory Technology Consultant EMC Corporation [email protected] Table of Contents Introduction ................................................................................................................................ 3 Data Warehouse Background .................................................................................................... 4 What Is a Data Warehouse? ................................................................................................... 4 Data Mart Defined .................................................................................................................. 8 Schemas and Data Models ..................................................................................................... 9 Data Warehouse Design – Top Down or Bottom Up? ............................................................10 Extract, Transformation and Loading (ETL) ...........................................................................11 Why You Build a Data Warehouse: Business Intelligence .....................................................13 Technology to the Rescue?.......................................................................................................19 RASP - Reliability, Availability, Scalability and Performance ..................................................20 Data Warehouse Backups .....................................................................................................26 -
A Guide to Data Governance Building a Roadmap for Trusted Data a Guide to Data Governance Contents
A Guide to Data Governance Building a roadmap for trusted data A Guide to Data Governance Contents What is Data Governance? ...............................................................................................3 Why Do We Need It? ............................................................................................................................................................................. 4 The Need to Create Trusted Data ..................................................................................................................................................... 4 The Need to Protect Data .................................................................................................................................................................... 5 Requirements For Governing Data In A Modern Enterprise ........................................7 Common Business Vocabulary ........................................................................................................................................................... 7 Governing Data Across A Distributed Data Landscape............................................................................................................ 7 Data Governance Classification ......................................................................................................................................................... 8 Data Governance Roles and Responsibilities .............................................................................................................................. -
Data Stewardship Committee: Minutes of February 23, 2015
Data Stewardship Committee: Minutes of February 23, 2015 In attendance: Craig Abbey (OIA), Gary Pacer (EAS), Brian O’Connor (CAS), Tom Wendt (VPRE), David Love (SEAS), Leah Feroleto (SW), Troy Joseph (GEMS), Chris Connor (GEMS/UG Admissions), Greg Olsen (VPEM), John Gottardy (Financial Aid), Sue Krzystofiak (HR), Beth Corry (Financial Services), Shirley Walker (Student Accounts), Michael Koziej (Campus Living), Kelly Hayes-McAlonie (Capital Planning), Tom Okon (Business Reporting and Services), Mark Molnar (OIA), Laurie Barnum (Resource Planning) Peter Elkin (Biomedical Informatics), Rachel Link (OIA). Meeting called to order at 4:00 p.m. by Gary Pacer. Gary thanked all in attendance for making the time to participate and asked attendees to introduce themselves and the areas represented. Gary gave a brief overview of the Data Stewardship Committee, which is a subsidiary of the Data Governance Council. Craig Abbey and Gary Pacer are co-chairing the DSC, and the initial invitation sent to the DSC members included the charge from the Provost for both the DSC and DGC. Thirteen data domains are represented on the committee, providing a university-wide perspective of how data governance is managed across the institution, how data are defined for particular projects, and identifying common vernacular to solve or address problems. The DSC membership is comprised of Data Stewards, Ex Officio members, and Ad Lucem (Latin for “to the light”) members, who serve to help lead the data stewardship process. Gary next reminded those in attendance of the work that lies ahead for the committee. The initial focus is to prepare the report recommending permanent data management organization and structure. -
Value and Implications of Master Data Management (MDM) and Metadata Management
Value and Implications of Master Data Management (MDM) and Metadata Management Prepared for: Dimitris A Geragas GARTNER CONSULTING Project Number: 330022715 CONFIDENTIAL AND PROPRIETARY This presentation, including any supporting materials, is owned by Gartner, Inc. and/or its affiliates and is for the sole use of the intended Gartner audience or other intended recipients. This presentation may contain information that is confidential, proprietary or otherwise legally protected, and it may not be further copied, distributed or publicly displayed without the express written permission of Gartner, Inc. or its affiliates. © 2015 Gartner, Inc. and/or its affiliates. All rights reserved. Enterprise Information Management Background CONFIDENTIAL AND PROPRIETARY 330022715 | © 2015 Gartner, Inc. and/or its affiliates. All rights reserved. 1 Enterprise Information Management is Never Information Enterprise Information Management (EIM) is about . Providing business value . Managing information (EIM) to provide business value . Setting up a business and IT program to manage information EIM is an integrative discipline for structuring, describing and governing information assets across organizational and technological boundaries. CONFIDENTIAL AND PROPRIETARY 330022715 | © 2015 Gartner, Inc. and/or its affiliates. All rights reserved. 2 Enterprise Information Management and the Information-Related Disciplines Enterprise Information Management (EIM) is about how information gets from a multitude of sources to a multitude of consumers in a relevant, -
Data Governance with Oracle
Data Governance with Oracle Defining and Implementing a Pragmatic Data Governance Process with Oracle Metadata Management and Oracle Data Quality Solutions ORACLE WHITE P A P E R | SEPTEMBER 2015 Disclaimer The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle. DATA GOVERNANCE WITH ORACLE Table of Contents Disclaimer 1 Introduction 1 First Define the Business Problem 2 Identify Executive Sponsor 3 Manage Glossary of Business Terms 4 Identify Critical Data Elements 4 Classify Data from an Information Security Perspective 5 Manage Business Rules 6 Manage Allowable Values for Business Terms 7 Support for Data Lineage and Impact Analysis 8 Manage Data Stewardship Workflows 10 Govern Big Data 11 Manage Data Quality Rules 12 Execute Data Quality Rules 12 View Data Quality Dashboard 16 Data Quality Remediation 16 Data Privacy and Security 17 Ingredients for Data Governance Success 17 Governance with Any Enterprise System 19 Align with Other Oracle Solutions 20 About the Author 22 DATA GOVERNANCE WITH ORACLE . DATA GOVERNANCE WITH ORACLE Introduction Data governance is the formulation of policy to optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions. Data governance programs have traditionally been focused on people and process. In this whitepaper, we will discuss how key data governance capabilities are enabled by Oracle Enterprise Metadata Manager (OEMM) and Oracle Enterprise Data Quality (EDQ). -
Data Warehouse Applications in Modern Day Business
California State University, San Bernardino CSUSB ScholarWorks Theses Digitization Project John M. Pfau Library 2002 Data warehouse applications in modern day business Carla Mounir Issa Follow this and additional works at: https://scholarworks.lib.csusb.edu/etd-project Part of the Business Intelligence Commons, and the Databases and Information Systems Commons Recommended Citation Issa, Carla Mounir, "Data warehouse applications in modern day business" (2002). Theses Digitization Project. 2148. https://scholarworks.lib.csusb.edu/etd-project/2148 This Project is brought to you for free and open access by the John M. Pfau Library at CSUSB ScholarWorks. It has been accepted for inclusion in Theses Digitization Project by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [email protected]. DATA WAREHOUSE APPLICATIONS IN MODERN DAY BUSINESS A Project Presented to the Faculty of California State University, San Bernardino In Partial Fulfillment of the Requirements for the Degree Master of Business Administration by Carla Mounir Issa June 2002 DATA WAREHOUSE APPLICATIONS IN MODERN DAY BUSINESS A Project Presented to the Faculty of California State University, San Bernardino by Carla Mounir Issa June 2002 Approved by: Date Dr. Walter T. Stewart ABSTRACT Data warehousing is not a new concept in the business world. However, the constant changing application of data warehousing is what is being innovated constantly. It is these applications that are enabling managers to make better decisions through strategic planning, and allowing organizations to achieve a competitive advantage. The technology of implementing the data warehouse will help with the decision making process, analysis design, and will be more cost effective in the future. -
Master Data Management Services (MDMS) System
ADAMS ML19121A465 U.S. Nuclear Regulatory Commission Privacy Impact Assessment Designed to collect the information necessary to make relevant determinations regarding the applicability of the Privacy Act, the Paperwork Reduction Act information collection requirements, and records management requirements. Master Data Management Services (MDMS) System Date: April 16, 2019 A. GENERAL SYSTEM INFORMATION 1. Provide a detailed description of the system: The Nuclear Regulatory Commission (NRC) has established the Master Data Management Services (MDMS) system to address both short and long-term enterprise data management needs. The MDMS is a major agencywide initiative to support the processes and decisions of NRC through: • Improving data quality • Improving re-use and exchange of information • Reducing or eliminating the storage of duplicate information • Providing an enterprise-wide foundation for information sharing • Establishing a data governance structure The MDMS provides an overall vision for, and leadership focused on, an enterprise solution that establishes authoritative data owners, delivers quality data in an efficient manner to the systems that require it, and effectively meets business needs. The MDMS is also focused on data architecture and includes projects that will identify and define data elements across the agency. The MDMS system is a web-based application accessible to representatives from every NRC office—that provides standardized and validated dockets and data records that support dockets, including licenses, contact and billing information, to all downstream NRC systems that require that data. The NRC collects digital identification data for individuals to support the agency core business operations, issue credentials, and administer access to agency‘s physical and logical resources. To support this need, the MDMS system also serves as a centralized repository for the accessibility of organization and personnel data. -
What to Look for When Selecting a Master Data Management Solution What to Look for When Selecting a Master Data Management Solution
What to Look for When Selecting a Master Data Management Solution What to Look for When Selecting a Master Data Management Solution Table of Contents Business Drivers of MDM .......................................................................................................... 3 Next-Generation MDM .............................................................................................................. 5 Keys to a Successful MDM Deployment .............................................................................. 6 Choosing the Right Solution for MDM ................................................................................ 7 Talend’s MDM Solution .............................................................................................................. 7 Conclusion ..................................................................................................................................... 8 Master data management (MDM) exists to enable the business success of an orga- nization. With the right MDM solution, organizations can successfully manage and leverage their most valuable shared information assets. Because of the strategic importance of MDM, organizations can’t afford to make any mistakes when choosing their MDM solution. This article will discuss what to look for in an MDM solution and why the right choice will help to drive strategic business initiatives, from cloud computing to big data to next-generation business agility. 2 It is critical for CIOs and business decision-makers to understand the value -
Lecture Notes on Data Mining& Data Warehousing
LECTURE NOTES ON DATA MINING& DATA WAREHOUSING COURSE CODE:BCS-403 DEPT OF CSE & IT VSSUT, Burla SYLLABUS: Module – I Data Mining overview, Data Warehouse and OLAP Technology,Data Warehouse Architecture, Stepsfor the Design and Construction of Data Warehouses, A Three-Tier Data WarehouseArchitecture,OLAP,OLAP queries, metadata repository,Data Preprocessing – Data Integration and Transformation, Data Reduction,Data Mining Primitives:What Defines a Data Mining Task? Task-Relevant Data, The Kind of Knowledge to be Mined,KDD Module – II Mining Association Rules in Large Databases, Association Rule Mining, Market BasketAnalysis: Mining A Road Map, The Apriori Algorithm: Finding Frequent Itemsets Using Candidate Generation,Generating Association Rules from Frequent Itemsets, Improving the Efficiently of Apriori,Mining Frequent Itemsets without Candidate Generation, Multilevel Association Rules, Approaches toMining Multilevel Association Rules, Mining Multidimensional Association Rules for Relational Database and Data Warehouses,Multidimensional Association Rules, Mining Quantitative Association Rules, MiningDistance-Based Association Rules, From Association Mining to Correlation Analysis Module – III What is Classification? What Is Prediction? Issues RegardingClassification and Prediction, Classification by Decision Tree Induction, Bayesian Classification, Bayes Theorem, Naïve Bayesian Classification, Classification by Backpropagation, A Multilayer Feed-Forward Neural Network, Defining aNetwork Topology, Classification Based of Concepts from -
Centralized Or Federated Data Management Models, IT Professionals' Preferences
Paper ID #8737 CENTRALIZED OR FEDERATED DATA MANAGEMENT MODELS, IT PROFESSIONALS’ PREFERENCES Dr. Gholam Ali Shaykhian, NASA Ali Shaykhian has received a Master of Science (M.S.) degree in Computer Systems from University of Central Florida and a second M.S. degree in Operations Research from the same university and has earned a Ph.D. in Operations Research from Florida Institute of Technology. His research interests include knowledge management, data mining, object-oriented methodologies, design patterns, software safety, genetic and optimization algorithms and data mining. Dr. Shaykhian is a professional member of the American Society for Engineering Education (ASEE). Dr. Mohd A. Khairi, Najran University B.Sc of computer science from Khartoum university. Earned my masters from ottawa university in system science. My doctoral degree in information system from University of Phoenix( my dissertation was in Master Data Management). I worked in IT industry over 20 years, 10 of them with Microsoft in different groups and for the last 4 years was with business intelligence group. My focus was in Master Data Management. For the last 2 years I teach at universify of Najran in the college of Computer Science and Information System . My research interest in Master Data Management. c American Society for Engineering Education, 2014 CENTRALIZED OR FEDERATED DATA MANAGEMENT MODELS, IT PROFESSIONALS’ PREFERENCES Gholam Ali Shaykhian, Ph.D. Mohd Abdelgadir Khairi, Ph.D. Abstract The purpose of this paper is to evaluate IT professionals’ preferences and experiences with the suitable data management models (Centralized Data Model or Federated Data Model) selection. The goal is to determine the best architectural model for managing enterprise data; and help organizations to select an architectural model. -
Database Vs. Data Warehouse: a Comparative Review
Insights Database vs. Data Warehouse: A Comparative Review By Drew Cardon Professional Services, SVP Health Catalyst A question I often hear out in the field is: I already have a database, so why do I need a data warehouse for healthcare analytics? What is the difference between a database vs. a data warehouse? These questions are fair ones. For years, I’ve worked with databases in healthcare and in other industries, so I’m very familiar with the technical ins and outs of this topic. In this post, I’ll do my best to introduce these technical concepts in a way that everyone can understand. But, before we discuss the difference, could I ask one big favor? This will only take 10 seconds. Could you click below and take a quick poll? I’d like to find out if your organization has a data warehouse, data base(s), or if you don’t know? This would really help me better understand how prevalent data warehouses really are. Click to take our 10 second database vs data warehouse poll Before diving in to the topic, I want to quickly highlight the importance of analytics in healthcare. If you don’t understand the importance of analytics, discussing the distinction between a database and a data warehouse won’t be relevant to you. Here it is in a nutshell. The future of healthcare depends on our ability to use the massive amounts of data now available to drive better quality at a lower cost. If you can’t perform analytics to make sense of your data, you’ll have trouble improving quality and costs, and you won’t succeed in the new healthcare environment.