DOCSLIB.ORG

The Latest in the Database – Metadata Relationship Donna Burbank Global Data Strategy Ltd

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Latest in the Database – Metadata Relationship Donna Burbank Global Data Strategy Ltd The Latest in the Database – Metadata Relationship Donna Burbank Global Data Strategy Ltd. Database Now! Online Conference July 19, 2017 Donna Burbank of business drivers with data-centric latest BI and Analytics software in the technology. In past roles, she has served in market. key brand strategy and product management roles at CA Technologies and She has worked with dozens of Fortune Embarcadero Technologies for several of 500 companies worldwide in the Americas, the leading data management products in Europe, Asia, and Africa and speaks the market. regularly at industry conferences. She has co-authored two books: Data Modeling for As an active contributor to the data the Business and Data Modeling Made Donna is a recognised industry expert in management community, she is a long Simple with ERwin Data Modeler and is a information management with over 20 time DAMA International member, Past regular contributor to industry years of experience in data strategy, President and Advisor to the DAMA Rocky publications. She can be reached at information management, data modeling, Mountain chapter, and was recently [email protected] metadata management, and enterprise awarded the Excellence in Data Donna is based in Boulder, Colorado, USA. architecture. Her background is multi- Management Award from DAMA faceted across consulting, product International in 2016. She was on the development, product management, brand review committee for the Object strategy, marketing, and business Management Group’s (OMG) Information leadership. Management Metamodel (IMM) and the Business Process Modeling Notation She is currently the Managing Director at (BPMN). Donna is also an analyst at the Global Data Strategy, Ltd., an international Boulder BI Train Trust (BBBT) where she information management consulting provides advices and gains insight on the company that specializes in the alignment Follow on Twitter @donnaburbank Global Data Strategy, Ltd. 2017 Today’s hashtag: #DBNow 2 Agenda What we’ll cover today • Emerging Trends in Metadata Management • The Business Value of Metadata Management • Metadata as Part of Wider Enterprise Data Management • Metadata Isn’t Just for Relational Databases Anymore • Technical Innovation & Best Practices in Managing Metadata Global Data Strategy, Ltd. 2017 3 Metadata is Hotter than ever A Growing Trend In a recent DATAVERSITY survey, over 80% of respondents stated that: Metadata is as important, if not more important, than in the past. Global Data Strategy, Ltd. 2017 4 What is Metadata? Metadata is Data In Context Global Data Strategy, Ltd. 2017 5 Metadata is the “Who, What, Where, Why, When & How” of Data Who What Where Why When How Who created this What is the business Where is this data Why are we storing When was this data How is this data data? definition of this data stored? this data? created? formatted? element? (character, numeric, etc.) Who is the Steward of What are the business Where did this data What is its usage & When was this data How many databases this data? rules for this data? come from? purpose? last updated? or data sources store this data? Who is using this What is the security Where is this data What are the business How long should it be data? level or privacy level used & shared? drivers for using this stored? of this data? data? Who “owns” this What is the Where is the backup When does it need to data? abbreviation or for this data? be purged/deleted? acronym for this data element? Who is regulating or What are the technical Are there regional auditing this data? naming standards for privacy or security database policies that regulate implementation? this data? Global Data Strategy, Ltd. 2017 6 Metadata is Needed by Business Stakeholders Making business decisions on accurate and well-understood data 80% of users of metadata are from the business, according to the recent DATAVERSITY survey. Business users often “get” metadata more than IT does! Global Data Strategy, Ltd. 2017 7 Poor Metadata Management Can be Expensive On average organizations waste 56% of UK marketing organizations 15-18% of their budgets dealing say managing data quality is a with data problems. “significant challenge” . Source: Experian Source: UK Marketing Today The US economy loses $3.1 trillion a year due to poor data quality . Correcting poor data quality is a Source: Artemis Ventures In the US, 6.9 billion pieces of mail Data Scientist’s least favorite task, are undeliverable annually because consuming on average 80% of their of address issues . working day Source: Forbes 2016 Source: US Postal Service Global Data Strategy, Ltd. 2017 8 A Very Expensive Example - NASA • On September 23, 1999 NASA lost the $125 million Mars Climate Orbiter spacecraft after a 286-day journey to Mars. • Missing Metadata was the culprit • Thruster data was sent in English units of pound-seconds (lbf s) instead of Metric units of newton- seconds (N s) • This metadata inconsistency caused thrusters to fire incorrectly, sending the craft off course – 60 miles in all (96.56 km). • In addition to the cost of the orbiter were: • Brand and Reputational Damage • Lost Opportunities for research on the Martian atmosphere & climate Global Data Strategy, Ltd. 2017 9 Human Metadata Avoid the dreaded “I just know” • Much business metadata and the history of the business exists in employee’s heads. • It is important to capture this metadata in an electronic format for sharing with others. • Avoid the dreaded “I just know” Part Number is what used to be called Component Number before the Business Glossary acquisition. Metadata Repository Data Models Etc. Global Data Strategy, Ltd. 2017 10 Metadata is Part of a Larger Enterprise Landscape A Successful Data Strategy Requires Many Inter-related Disciplines “Top-Down” alignment with business priorities Managing the people, process, policies & culture around data Leveraging & managing data for strategic advantage Coordinating & integrating disparate data sources “Bottom-Up” management & inventory of data sources Copyright Global Data Strategy, Ltd. 2017 Global Data Strategy, Ltd. 2017 11 Metadata Use Cases – Now & In the Future • Business Use Cases for Metadata area Evolving, according to the DATAVERSITY Emerging Trends survey. • The “Top 5” are changing – Less BI/DW and Software Dev & More Big Data & Data Science • Data Governance, Master Data Management, and Data Quality remain constants Now Future Global Data Strategy, Ltd. 2017 12 Types of Metadata – Now & In the Future • The types of metadata sources being managed are also evolving. • Business Glossaries & Data Warehouses remain constants • Data Quality & Big Data Platform sources are growing Now Future Global Data Strategy, Ltd. 2017 13 Metadata isn’t just for Relational Databases anymore… • There are many Sources and Types of Metadata Relational databases Application Code Data Models Data Transformation / ETL Tools Text Documents Spreadsheets XML Data Quality Tools Open Data Business Process Models Internet of Things (IoT) Business Intelligence (BI) Tools Photos / Images ERP, CRM, and Packed Applications Social Media Big Data platforms COBOL Copybooks Etc.… there are many more Graph Databases Global Data Strategy, Ltd. 2017 14 Relational Database Metadata • The technical structure of a relational database is defined by DDL (data definition language). It describes the structure / schema for how data is stored in a database. • A Glossary or Data Dictionary generally stores the business metadata. Data Technical Metadata Business Metadata CREATE TABLE EMPLOYEE ( employee_id INTEGER NOT NULL, department_id INTEGER NOT NULL, employee_fname VARCHAR(50) NULL, Term Definition employee_lname VARCHAR(50) NULL, An employee is an individual who currently employee_ssn CHAR(9) NULL); Employee works for the organization or who has been recently employed within the past 6 months. CREATE TABLE CUSTOMER ( customer_id INTEGER NOT NULL, A customer is a person or organization who customer_name VARCHAR(50) NULL, has purchased from the organization within Customer customer_address VARCHAR(150) NULL, the past 2 years and has an active loyalty card customer_city VARCHAR(50) NULL, customer_state CHAR(2) NULL, or maintenance contract. customer_zip CHAR(9) NULL); Glossary or Data Dictionary DDL John Smith Global Data Strategy, Ltd. 2017 15 Data Models are a Good Source of Metadata • Data Models are another good source of both business & technical metadata for relational databases. • They store structural metadata as well as business rules & definitions. Technical Metadata Business Metadata Customer Customer_ID CHAR(18) NOT NULL First Name CHAR(18) NOT NULL Last Name CHAR(18) NOT NULL City CHAR(18) NULL Date Purchased CHAR(18) NULL Global Data Strategy, Ltd. 2017 16 ERP, CRM and Packaged Application Metadata • Packaged applications such as CRM and ERP systems (e.g. Salesforce, PeopleSoft, etc.) are typically based on a relational database system. • Therefore, there is important metadata about both the physical table structures as well as the business names & definitions. Technical Metadata Business Metadata Global Data Strategy, Ltd. 2017 17 NoSQL – Key Value Databases • NoSQL Databases are often optimal solutions for flexibility & performance in certain scenarios. • One common NoSQL database is a key-value pair database (e.g. Redis, Oracle NoSQL, etc.) • They can support extremely high volumes of records & state changes per second through distributed processing and distributed storage. • Use cases include: Managing user sessions
Load more

Recommended publications
  • Metadata and GIS
    Metadata and GIS ® An ESRI White Paper • October 2002 ESRI 380 New York St., Redlands, CA 92373-8100, USA • TEL 909-793-2853 • FAX 909-793-5953 • E-MAIL [email protected] • WEB www.esri.com Copyright © 2002 ESRI All rights reserved. Printed in the United States of America. The information contained in this document is the exclusive property of ESRI. This work is protected under United States copyright law and other international copyright treaties and conventions. No part of this work may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying and recording, or by any information storage or retrieval system, except as expressly permitted in writing by ESRI. All requests should be sent to Attention: Contracts Manager, ESRI, 380 New York Street, Redlands, CA 92373-8100, USA. The information contained in this document is subject to change without notice. U.S. GOVERNMENT RESTRICTED/LIMITED RIGHTS Any software, documentation, and/or data delivered hereunder is subject to the terms of the License Agreement. In no event shall the U.S. Government acquire greater than RESTRICTED/LIMITED RIGHTS. At a minimum, use, duplication, or disclosure by the U.S. Government is subject to restrictions as set forth in FAR §52.227-14 Alternates I, II, and III (JUN 1987); FAR §52.227-19 (JUN 1987) and/or FAR §12.211/12.212 (Commercial Technical Data/Computer Software); and DFARS §252.227-7015 (NOV 1995) (Technical Data) and/or DFARS §227.7202 (Computer Software), as applicable. Contractor/Manufacturer is ESRI, 380 New York Street, Redlands, CA 92373- 8100, USA.
    [Show full text]
  • Creating Permissionless Blockchains of Metadata Records Dejah Rubel
    Articles No Need to Ask: Creating Permissionless Blockchains of Metadata Records Dejah Rubel ABSTRACT This article will describe how permissionless metadata blockchains could be created to overcome two significant limitations in current cataloging practices: centralization and a lack of traceability. The process would start by creating public and private keys, which could be managed using digital wallet software. After creating a genesis block, nodes would submit either a new record or modifications to a single record for validation. Validation would rely on a Federated Byzantine Agreement consensus algorithm because it offers the most flexibility for institutions to select authoritative peers. Only the top tier nodes would be required to store a copy of the entire blockchain thereby allowing other institutions to decide whether they prefer to use the abridged version or the full version. INTRODUCTION Several libraries and library vendors are investigating how blockchain could improve activities such as scholarly publishing, content dissemination, and copyright enforcement. A few organizations, such as Katalysis, are creating prototypes or alpha versions of blockchain platforms and products.1 Although there has been some discussion about using blockchains for metadata creation and management, only one company appears to be designing such a product. Therefore, this article will describe how permissionless blockchains of metadata records could be created, managed, and stored to overcome current challenges with metadata creation and management. LIMITATIONS OF CURRENT PRACTICES Metadata standards, processes, and systems are changing to meet twenty-first century information needs and expectations. There are two significant limitations, however, to our current metadata creation and modification practices that have not been addressed: centralization and traceability.
    [Show full text]
  • SQL Server Column Store Indexes Per-Åke Larson, Cipri Clinciu, Eric N
    SQL Server Column Store Indexes Per-Åke Larson, Cipri Clinciu, Eric N. Hanson, Artem Oks, Susan L. Price, Srikumar Rangarajan, Aleksandras Surna, Qingqing Zhou Microsoft {palarson, ciprianc, ehans, artemoks, susanpr, srikumar, asurna, qizhou}@microsoft.com ABSTRACT SQL Server column store indexes are “pure” column stores, not a The SQL Server 11 release (code named “Denali”) introduces a hybrid, because they store all data for different columns on new data warehouse query acceleration feature based on a new separate pages. This improves I/O scan performance and makes index type called a column store index. The new index type more efficient use of memory. SQL Server is the first major combined with new query operators processing batches of rows database product to support a pure column store index. Others greatly improves data warehouse query performance: in some have claimed that it is impossible to fully incorporate pure column cases by hundreds of times and routinely a tenfold speedup for a store technology into an established database product with a broad broad range of decision support queries. Column store indexes are market. We’re happy to prove them wrong! fully integrated with the rest of the system, including query To improve performance of typical data warehousing queries, all a processing and optimization. This paper gives an overview of the user needs to do is build a column store index on the fact tables in design and implementation of column store indexes including the data warehouse. It may also be beneficial to build column enhancements to query processing and query optimization to take store indexes on extremely large dimension tables (say more than full advantage of the new indexes.
    [Show full text]
  • GAVS' Blockchain-Based
    Brochure GAVS’ Blockchain-based Master Data Management Framework For Enterprises Enterprise-wide Data Stewardship, Shared Ownership Of Data Master Data Challenges With enterprises moving full throttle towards digital transformation and the consequent proliferation of data, the magnitude of Master Data Management (MDM) challenges are on a steep rise. Implementing a sustainable MDM strategy has now gained critical importance. 01 www.gavstech.com Master Data Management Challenges Centralized Authority Data Management Centralized management of Master Data is complex, Data Reconciliation expensive and prone to security compromise & Mergers & acquisitions involve complex reconciliation accidental loss. and appropriate definition for Data Stewardship & Governance. Data Security Data Movement A single malicious attack to a centralized infrastructure Sharing Master Data across enterprise boundaries, with can do a lot of damage. subsidiaries or BUs across geographies, or multi-master updates is highly resource intensive. Data may also be compromised by breaches through the backend, bypassing business logic-based front-end Transfer of Ownership controls. Transfer of Master Data ownership to customers or to other BUs comes with a compliance risk due to the lack of cryptography-based authentication. Data Lineage Audit trails may be incomplete, unavailable or corrupt leading to lack of data lineage & traceability, which in-turn will affect downstream systems. Blockchain-based Master Data Management to the Rescue! A Blockchain is a kind of distributed ledger consisting of cryptographically signed, irrevocable transactional records based on shared consensus among all participants in the network. Decentralized Trust & Transparent Secured Resilient Innovative application of Blockchain is key to transformation in several industry verticals 02 www.gavstech.com Decentralization Data Transparency/Traceability The cornerstone of the Blockchain technology is Since Blockchains are based on smart contracts and decentralization.
    [Show full text]
  • When Relational-Based Applications Go to Nosql Databases: a Survey
    information Article When Relational-Based Applications Go to NoSQL Databases: A Survey Geomar A. Schreiner 1,* , Denio Duarte 2 and Ronaldo dos Santos Mello 1 1 Departamento de Informática e Estatística, Federal University of Santa Catarina, 88040-900 Florianópolis - SC, Brazil 2 Campus Chapecó, Federal University of Fronteira Sul, 89815-899 Chapecó - SC, Brazil * Correspondence: [email protected] Received: 22 May 2019; Accepted: 12 July 2019; Published: 16 July 2019 Abstract: Several data-centric applications today produce and manipulate a large volume of data, the so-called Big Data. Traditional databases, in particular, relational databases, are not suitable for Big Data management. As a consequence, some approaches that allow the definition and manipulation of large relational data sets stored in NoSQL databases through an SQL interface have been proposed, focusing on scalability and availability. This paper presents a comparative analysis of these approaches based on an architectural classification that organizes them according to their system architectures. Our motivation is that wrapping is a relevant strategy for relational-based applications that intend to move relational data to NoSQL databases (usually maintained in the cloud). We also claim that this research area has some open issues, given that most approaches deal with only a subset of SQL operations or give support to specific target NoSQL databases. Our intention with this survey is, therefore, to contribute to the state-of-art in this research area and also provide a basis for choosing or even designing a relational-to-NoSQL data wrapping solution. Keywords: big data; data interoperability; NoSQL databases; relational-to-NoSQL mapping 1.
    [Show full text]
  • Studying Social Tagging and Folksonomy: a Review and Framework
    Studying Social Tagging and Folksonomy: A Review and Framework Item Type Journal Article (On-line/Unpaginated) Authors Trant, Jennifer Citation Studying Social Tagging and Folksonomy: A Review and Framework 2009-01, 10(1) Journal of Digital Information Journal Journal of Digital Information Download date 02/10/2021 03:25:18 Link to Item http://hdl.handle.net/10150/105375 Trant, Jennifer (2009) Studying Social Tagging and Folksonomy: A Review and Framework. Journal of Digital Information 10(1). Studying Social Tagging and Folksonomy: A Review and Framework J. Trant, University of Toronto / Archives & Museum Informatics 158 Lee Ave, Toronto, ON Canada M4E 2P3 jtrant [at] archimuse.com Abstract This paper reviews research into social tagging and folksonomy (as reflected in about 180 sources published through December 2007). Methods of researching the contribution of social tagging and folksonomy are described, and outstanding research questions are presented. This is a new area of research, where theoretical perspectives and relevant research methods are only now being defined. This paper provides a framework for the study of folksonomy, tagging and social tagging systems. Three broad approaches are identified, focusing first, on the folksonomy itself (and the role of tags in indexing and retrieval); secondly, on tagging (and the behaviour of users); and thirdly, on the nature of social tagging systems (as socio-technical frameworks). Keywords: Social tagging, folksonomy, tagging, literature review, research review 1. Introduction User-generated keywords – tags – have been suggested as a lightweight way of enhancing descriptions of on-line information resources, and improving their access through broader indexing. “Social Tagging” refers to the practice of publicly labeling or categorizing resources in a shared, on-line environment.
    [Show full text]
  • Data Dictionary a Data Dictionary Is a File That Helps to Define The
    Cleveland | v. 216.369.2220 • Columbus | v. 614.291.8456 Data Dictionary A data dictionary is a file that helps to define the organization of a particular database. The data dictionary acts as a description of the data objects or items in a model and is used for the benefit of the programmer or other people who may need to access it. A data dictionary does not contain the actual data from the database; it contains only information for how to describe/manage the data; this is called metadata*. Building a data dictionary provides the ability to know the kind of field, where it is located in a database, what it means, etc. It typically consists of a table with multiple columns that describe relationships as well as labels for data. A data dictionary often contains the following information about fields: • Default values • Constraint information • Definitions (example: functions, sequence, etc.) • The amount of space allocated for the object/field • Auditing information What is the data dictionary used for? 1. It can also be used as a read-only reference in order to obtain information about the database 2. A data dictionary can be of use when developing programs that use a data model 3. The data dictionary acts as a way to describe data in “real-world” terms Why is a data dictionary needed? One of the main reasons a data dictionary is necessary is to provide better accuracy, organization, and reliability in regards to data management and user/administrator understanding and training. Benefits of using a data dictionary: 1.
    [Show full text]
  • Blockchain Database for a Cyber Security Learning System
    Session ETD 475 Blockchain Database for a Cyber Security Learning System Sophia Armstrong Department of Computer Science, College of Engineering and Technology East Carolina University Te-Shun Chou Department of Technology Systems, College of Engineering and Technology East Carolina University John Jones College of Engineering and Technology East Carolina University Abstract Our cyber security learning system involves an interactive environment for students to practice executing different attack and defense techniques relating to cyber security concepts. We intend to use a blockchain database to secure data from this learning system. The data being secured are students’ scores accumulated by successful attacks or defends from the other students’ implementations. As more professionals are departing from traditional relational databases, the enthusiasm around distributed ledger databases is growing, specifically blockchain. With many available platforms applying blockchain structures, it is important to understand how this emerging technology is being used, with the goal of utilizing this technology for our learning system. In order to successfully secure the data and ensure it is tamper resistant, an investigation of blockchain technology use cases must be conducted. In addition, this paper defined the primary characteristics of the emerging distributed ledgers or blockchain technology, to ensure we effectively harness this technology to secure our data. Moreover, we explored using a blockchain database for our data. 1. Introduction New buzz words are constantly surfacing in the ever evolving field of computer science, so it is critical to distinguish the difference between temporary fads and new evolutionary technology. Blockchain is one of the newest and most developmental technologies currently drawing interest.
    [Show full text]
  • An Introduction to Cloud Databases a Guide for Administrators
    Compliments of An Introduction to Cloud Databases A Guide for Administrators Wendy Neu, Vlad Vlasceanu, Andy Oram & Sam Alapati REPORT Break free from old guard databases AWS provides the broadest selection of purpose-built databases allowing you to save, grow, and innovate faster Enterprise scale at 3-5x the performance 14+ database engines 1/10th the cost of vs popular alternatives - more than any other commercial databases provider Learn more: aws.amazon.com/databases An Introduction to Cloud Databases A Guide for Administrators Wendy Neu, Vlad Vlasceanu, Andy Oram, and Sam Alapati Beijing Boston Farnham Sebastopol Tokyo An Introduction to Cloud Databases by Wendy A. Neu, Vlad Vlasceanu, Andy Oram, and Sam Alapati Copyright © 2019 O’Reilly Media Inc. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more infor‐ mation, contact our corporate/institutional sales department: 800-998-9938 or [email protected]. Development Editor: Jeff Bleiel Interior Designer: David Futato Acquisitions Editor: Jonathan Hassell Cover Designer: Karen Montgomery Production Editor: Katherine Tozer Illustrator: Rebecca Demarest Copyeditor: Octal Publishing, LLC September 2019: First Edition Revision History for the First Edition 2019-08-19: First Release The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. An Introduction to Cloud Databases, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the authors, and do not represent the publisher’s views.
    [Show full text]
  • Metadata Creation Practices in Digital Repositories and Collections
    Metadata Creation Practices in Digital Repositories and Collections: Schemata, Jung-ran Park and Selection Criteria, and Interoperability Yuji Tosaka This study explores the current state of metadata-creation development of such a mediation mechanism calls for practices across digital repositories and collections by an empirical assessment of various issues surrounding metadata-creation practices. using data collected from a nationwide survey of mostly The critical issues concerning metadata practices cataloging and metadata professionals. Results show across distributed digital collections have been rela- that MARC, AACR2, and LCSH are the most widely tively unexplored. While examining learning objects and used metadata schema, content standard, and subject- e-prints communities of practice, Barton, Currier, and Hey point out the lack of formal investigation of the metadata- controlled vocabulary, respectively. Dublin Core (DC) is creation process.2 As will be discussed in the following the second most widely used metadata schema, followed section, some researchers have begun to assess the current by EAD, MODS, VRA, and TEI. Qualified DC’s wider state of descriptive practices, metadata schemata, and use vis-à-vis Unqualified DC (40.6 percent versus 25.4 content standards. However, the literature has not yet developed to a point where it affords a comprehensive percent) is noteworthy. The leading criteria in selecting picture. Given the propagation of metadata projects, it is metadata and controlled-vocabulary schemata are collec- important to continue to track changes in metadata-cre- tion-specific considerations, such as the types of resources, ation practices while they are still in constant flux. Such efforts are essential for adding new perspectives to digital nature of the collection, and needs of primary users and library research and practices in an environment where communities.
    [Show full text]
  • 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).
    [Show full text]
  • Geotagging Photos to Share Field Trips with the World During the Past Few
    Geotagging photos to share field trips with the world During the past few years, numerous new online tools for collaboration and community building have emerged, providing web-users with a tremendous capability to connect with and share a variety of resources. Coupled with this new technology is the ability to ‘geo-tag’ photos, i.e. give a digital photo a unique spatial location anywhere on the surface of the earth. More precisely geo-tagging is the process of adding geo-spatial identification or ‘metadata’ to various media such as websites, RSS feeds, or images. This data usually consists of latitude and longitude coordinates, though it can also include altitude and place names as well. Therefore adding geo-tags to photographs means adding details as to where as well as when they were taken. Geo-tagging didn’t really used to be an easy thing to do, but now even adding GPS data to Google Earth is fairly straightforward. The basics Creating geo-tagged images is quite straightforward and there are various types of software or websites that will help you ‘tag’ the photos (this is discussed later in the article). In essence, all you need to do is select a photo or group of photos, choose the "Place on map" command (or similar). Most programs will then prompt for an address or postcode. Alternatively a GPS device can be used to store ‘way points’ which represent coordinates of where images were taken. Some of the newest phones (Nokia N96 and i- Phone for instance) have automatic geo-tagging capabilities. These devices automatically add latitude and longitude metadata to the existing EXIF file which is already holds information about the picture such as camera, date, aperture settings etc.
    [Show full text]