Data Quality Accelerator Guide

Total Page:16

File Type:pdf, Size:1020Kb

Data Quality Accelerator Guide Informatica® 10.2 HotFix 1 Data Quality Accelerator Guide Informatica Data Quality Accelerator Guide 10.2 HotFix 1 April 2019 © Copyright Informatica LLC 2009, 2019 This software and documentation are provided only under a separate license agreement containing restrictions on use and disclosure. No part of this document may be reproduced or transmitted in any form, by any means (electronic, photocopying, recording or otherwise) without prior consent of Informatica LLC. U.S. GOVERNMENT RIGHTS Programs, software, databases, and related documentation and technical data delivered to U.S. Government customers are "commercial computer software" or "commercial technical data" pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, the use, duplication, disclosure, modification, and adaptation is subject to the restrictions and license terms set forth in the applicable Government contract, and, to the extent applicable by the terms of the Government contract, the additional rights set forth in FAR 52.227-19, Commercial Computer Software License. Informatica, PowerCenter, and the Informatica logo are trademarks or registered trademarks of Informatica LLC in the United States and many jurisdictions throughout the world. A current list of Informatica trademarks is available on the web at https://www.informatica.com/trademarks.html. Other company and product names may be trade names or trademarks of their respective owners. Portions of this software and/or documentation are subject to copyright held by third parties. Required third party notices are included with the product. The information in this documentation is subject to change without notice. If you find any problems in this documentation, report them to us at [email protected]. Informatica products are warranted according to the terms and conditions of the agreements under which they are provided. INFORMATICA PROVIDES THE INFORMATION IN THIS DOCUMENT "AS IS" WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTY OR CONDITION OF NON-INFRINGEMENT. Publication Date: 2019-04-17 Table of Contents Preface ...................................................................... 7 Informatica Resources................................................... 7 Informatica Network................................................. 7 Informatica Knowledge Base............................................ 7 Informatica Documentation............................................. 7 Informatica Product Availability Matrices.................................... 8 Informatica Velocity.................................................. 8 Informatica Marketplace............................................... 8 Informatica Global Customer Support...................................... 8 Chapter 1: Introduction to Accelerators....................................... 9 Accelerators Overview................................................... 9 Accelerator Structure.................................................... 9 General Accelerator Structure........................................... 10 Data Domain Accelerator Structure....................................... 10 Accelerator Installation.................................................. 11 Rules and Guidelines for Accelerator Installation.............................. 12 Importing Rule and Mapping Objects...................................... 13 Importing Data Domains and Data Domain Groups............................. 13 Accelerator Components................................................. 14 Content Sets...................................................... 16 Data Domains..................................................... 16 Demonstration Mappings............................................. 17 Reference Tables................................................... 17 Rule Specifications................................................. 17 Rules.......................................................... 17 Tags and Rules....................................................... 18 Accelerator Use in PowerCenter............................................ 18 Chapter 2: Core Accelerator................................................. 19 Core Accelerator Overview................................................ 19 Core Address Data Cleansing Rules.......................................... 19 Core Contact Data Cleansing Rules.......................................... 21 Core Corporate Data Cleansing Rules......................................... 22 Core General Data Cleansing Rules.......................................... 22 Core Matching and Deduplication Rules....................................... 28 Core Product Data Cleansing Rules.......................................... 28 Core Demonstration Mappings............................................. 29 Table of Contents 3 Chapter 3: Data Domains Accelerator........................................ 30 Data Domains Accelerator Overview.......................................... 30 Data Domains in the Data Domains Accelerator.................................. 31 Column Name Rules in the Data Domains Accelerator.............................. 43 Data Rules in the Data Domains Accelerator.................................... 47 Chapter 4: Australia/New Zealand Accelerator............................... 54 Australia/New Zealand Accelerator Overview.................................... 54 Australia/New Zealand Address Data Cleansing Rules.............................. 55 Australia/New Zealand Composite Rules....................................... 56 Australia/New Zealand Contact Data Cleansing Rules.............................. 58 Australia/New Zealand Corporate Data Cleansing Rules............................. 60 Australia/New Zealand General Data Cleansing Rules.............................. 61 Australia/New Zealand Matching and Deduplication Rules........................... 62 Australia/New Zealand Demonstration Mappings................................. 64 Chapter 5: BCBS 239/CCAR Accelerator..................................... 66 BCBS 239/CCAR Accelerator Overview........................................ 66 BCBS 239/CCAR Rule Specifications......................................... 66 Dependent Rules................................................... 72 BCBS 239/CCAR Demonstration Mappings..................................... 73 Chapter 6: Brazil Accelerator................................................ 74 Brazil Accelerator Overview............................................... 74 Brazil Address Data Cleansing Rules......................................... 74 Brazil Composite Rules.................................................. 75 Brazil Contact Data Cleansing Rules.......................................... 76 Brazil Corporate Data Cleansing Rules........................................ 78 Brazil General Data Cleansing Rules.......................................... 78 Brazil Matching and Deduplication Rules....................................... 79 Brazil Demonstration Mappings............................................ 80 Chapter 7: Financial Services Accelerator................................... 82 Financial Services Accelerator Overview....................................... 82 Financial Services Contact Data Cleansing Rules................................. 82 Financial Services Financial Data Cleansing Rules................................. 83 Financial Services General Data Cleansing Rules.................................. 85 Financial Services Matching and Deduplication Rules.............................. 86 Chapter 8: France Accelerator............................................... 88 France Accelerator Overview.............................................. 88 France Address Data Cleansing Rules......................................... 88 4 Table of Contents France Composite Rules................................................. 89 France Contact Data Cleansing Rules......................................... 90 France Corporate Data Cleansing Rules....................................... 92 France General Data Cleansing Rules......................................... 93 France Matching and Deduplication Rules...................................... 93 France Demonstration Mappings............................................ 95 Chapter 9: Germany Accelerator............................................. 96 Germany Accelerator Overview............................................. 96 Germany Address Data Cleansing Rules....................................... 96 Germany Composite Rules................................................ 97 Germany Contact Data Cleansing Rules....................................... 98 Germany Corporate Data Cleansing Rules..................................... 100 Germany General Data Cleansing Rules....................................... 100 Germany Matching and Deduplication Rules.................................... 101 Germany Demonstration Mappings......................................... 103 Chapter 10: India Accelerator.............................................. 105 India Accelerator Overview............................................... 105 India Address Data Cleansing Rules......................................... 105 India Contact Data Cleansing Rules......................................... 106 India Corporate Data Cleansing Rules........................................ 107 India Financial Data Cleansing
Recommended publications
  • Criminal Background Check Procedures
    Shaping the future of international education New Edition Criminal Background Check Procedures CIS in collaboration with other agencies has formed an International Task Force on Child Protection chaired by CIS Executive Director, Jane Larsson, in order to apply our collective resources, expertise, and partnerships to help international school communities address child protection challenges. Member Organisations of the Task Force: • Council of International Schools • Council of British International Schools • Academy of International School Heads • U.S. Department of State, Office of Overseas Schools • Association for the Advancement of International Education • International Schools Services • ECIS CIS is the leader in requiring police background check documentation for Educator and Leadership Candidates as part of the overall effort to ensure effective screening. Please obtain a current police background check from your current country of employment/residence as well as appropriate documentation from any previous country/countries in which you have worked. It is ultimately a school’s responsibility to ensure that they have appropriate police background documentation for their Educators and CIS is committed to supporting them in this endeavour. It is important to demonstrate a willingness and effort to meet the requirement and obtain all of the paperwork that is realistically possible. This document is the result of extensive research into governmental, law enforcement and embassy websites. We have tried to ensure where possible that the information has been obtained from official channels and to provide links to these sources. CIS requests your help in maintaining an accurate and useful resource; if you find any information to be incorrect or out of date, please contact us at: [email protected].
    [Show full text]
  • Police Background Check Procedures
    Police Background Check Procedures CIS in collaboration with other agencies has formed an International Task Force on Child Protection in order to apply our collective resources, expertise, and partnerships to help international school communities address child protection challenges. Members of the Task Force: • Colin Bell, Executive Director, Council of British International Schools • Bambi Betts, Executive Director, Academy of International School Heads • Christine Brown, Regional Education Officer for Europe, U.S. Department of State, Office of Overseas Schools • Linda Duevel, President-elect, Association for the Advancement of International Education • Roger Hove, President, International Schools Services • Jane Larsson, Executive Director, Council of International Schools (Chair) • Kevin Ruth, Executive Director, ECIS CIS is taking the leading step of requiring police background check documentation for Educator Candidates as part of the overall effort to ensure effective screening. Please obtain a current police background check from your current country of employment/residence as well as appropriate documentation from any previous country/countries in which you have worked. It is ultimately a school’s responsibility to ensure that they have appropriate police background documentation for their Educators and CIS is committed to supporting them in this endeavour. It is important to demonstrate a willingness and effort to meet the requirement and obtain all of the paperwork that is realistically possible. CIS is continually updating and adding information to this document regarding police background check application procedures worldwide. Document compiled by: CIS Educational Recruitment Services Disclaimer: This information has been collated from governmental websites and law enforcement agencies on an 'as-is', 'as available' basis. While every attempt has been made to ensure the information is accurate and up to date, CIS makes no warranties or representations of any kind that it is error-free.
    [Show full text]
  • Django-Localflavor Documentation
    django-localflavor Documentation Release 1.0 Django Software Foundation and individual contributors July 29, 2013 CONTENTS 1 Installation 3 2 Internationalization 5 3 Adding flavors 7 4 Releases 9 5 How to migrate 11 6 Backwards compatibility 13 7 Indices and tables 15 7.1 Argentina (ar).............................................. 15 7.2 Austria (at)............................................... 15 7.3 Australia (au).............................................. 16 7.4 Belgium (be)............................................... 17 7.5 Brazil (br)................................................ 18 7.6 Canada (ca)............................................... 18 7.7 Switzerland (ch)............................................. 19 7.8 Chile (cl)................................................ 20 7.9 China (cn)................................................ 21 7.10 Columbia (co).............................................. 22 7.11 Czech Republic (cz)........................................... 22 7.12 Germany (de).............................................. 23 7.13 Ecuador (ec)............................................... 23 7.14 Spain (es)................................................ 24 7.15 Finnland (fi).............................................. 25 7.16 France (fr)................................................ 25 7.17 Great Britain (gb)............................................ 26 7.18 Hong Kong (hk)............................................. 27 7.19 Croatia (hr)............................................... 27 7.20
    [Show full text]
  • Django-Localflavor Documentation
    django-localflavor Documentation Release 3.1 Django Software Foundation and individual contributors Aug 26, 2021 Contents 1 Installation 3 2 Internationalization 5 3 Adding flavors 7 4 Releases 9 4.1 Deprecation Policy............................................9 5 Backwards compatibility 11 6 Indices and tables 13 6.1 Authors.................................................. 13 6.2 Changelog................................................ 16 6.3 Argentina (ar).............................................. 27 6.4 Austria (at)............................................... 28 6.5 Australia (au).............................................. 29 6.6 Belgium (be)............................................... 31 6.7 Bulgaria (bg)............................................... 32 6.8 Brazil (br)................................................ 33 6.9 Canada (ca)............................................... 35 6.10 Switzerland (ch)............................................. 36 6.11 Chile (cl)................................................ 37 6.12 China (cn)................................................ 37 6.13 Colombia (co).............................................. 38 6.14 Czech Republic (cz)........................................... 38 6.15 Germany (de).............................................. 39 6.16 Denmark (dk).............................................. 40 6.17 Ecuador (ec)............................................... 41 6.18 Estonia (ee)............................................... 41 6.19 Egypt (eg)...............................................
    [Show full text]
  • Forcepoint DLP Predefined Policies and Classifiers
    Forcepoint DLP Predefined Policies and Classifiers Predefined Policies and Classifiers | Forcepoint DLP | 8.7.x For your convenience, Forcepoint DLP includes hundreds of predefined policies and content classifiers. ● Predefined policies help administrators quickly and easily define what type of content is considered a security breach at their organization. While choosing a policy or policy category, some items are set “off” by default. They can be activated individually in the Forcepoint Security Manager. ■ Data Loss Prevention policies, page 2 ■ Discovery policies, page 115 ● Predefined classifiers can be used to detect events and threats involving secured data. This article provides a list of all the predefined content classifiers that Forcepoint DLP provides for detecting events and threats involving secured data. This includes: ■ File-type classifiers ■ Script classifiers ■ Dictionaries ■ Pattern classifiers The predefined policies and classifiers are constantly being updated and improved. See Updating Predefined Policies and Classifiers for instructions on keeping policies and classifiers current. © 2019 Forcepoint LLC Data Loss Prevention policies Predefined Policies and Classifiers | Forcepoint DLP | 8.7.x Use the predefined data loss prevention policies to detect sensitive content, compliance violations, and data theft. For acceptable use policies, see: ● Acceptable Use, page 3 The content protection policies fall into several categories: ● Company Confidential and Intellectual Property (IP), page 4 ● Credit Cards, page 9 ● Financial
    [Show full text]
  • Predefined Policies and Classifiers
    Predefined Policies and Classifiers Predefined Policies and Classifiers | Data Security Solutions | Version 7.8.x For your convenience, Websense Data Security includes hundreds of predefined policies and content classifiers. Predefined Policies enable you to quickly and easily define what type of content is considered a security breach on your network. Predefined Classifiers can be used to detect events and threats involving secured data. Predefined Policies Predefined Policies and Classifiers | Data Security Solutions | Version 7.8.x Predefined policies enable you to quickly and easily define what type of content is considered a security breach on your network. Many of the Websense Data Security policies are Natural Language Processing (NLP) policies which enable more powerful search and analysis techniques. While choosing a policy or policy category, some items are set “off” by default, and can be activated individually by checking them according to the specific needs of the organization or business. The predefined policies included in Websense Data Security are constantly being updated and improved. See Updating Predefined Policies and Classifiers for instructions on keeping your policies current. Data Loss Prevention policies, page 1 Discovery policies, page 79 Data Loss Prevention policies Predefined Policies and Classifiers | Data Security Solutions | Version 7.8.x The predefined data loss prevention policies are based on detection of sensitive content, compliance violations, and data theft. Data Security - Predefined Policies
    [Show full text]
  • Forcepoint DLP Predefined Policies and Classifiers
    Forcepoint DLP Predefined Policies and Classifiers Predefined Policies and Classifiers | Forcepoint DLP | v8.4.x For your convenience, Forcepoint DLP includes hundreds of predefined policies and content classifiers. ● Predefined policies help administrators quickly and easily define what type of content is considered a security breach at their organization. While choosing a policy or policy category, some items are set “off” by default. They can be activated individually in the Forcepoint Security Manager. ■ Data Loss Prevention policies, page 2 ■ Discovery policies, page 90 ● Predefined classifiers can be used to detect events and threats involving secured data. This article provides a list of all the predefined content classifiers that Forcepoint DLP provides for detecting events and threats involving secured data. This includes: ■ File-type classifiers ■ Script classifiers ■ Dictionaries ■ Pattern classifiers The predefined policies and classifiers are constantly being updated and improved. See Updating Predefined Policies and Classifiers for instructions on keeping policies and classifiers current. © 2017 Forcepoint LLC Data Loss Prevention policies Predefined Policies and Classifiers | Forcepoint DLP | v8.4.x Use the predefined data loss prevention policies to detect sensitive content, compliance violations, and data theft. For acceptable use policies, see: ● Acceptable Use, page 3 The content protection policies fall into several categories: ● Company Confidential and Intellectual Property (IP), page 4 ● Credit Cards, page 9 ● Financial
    [Show full text]
  • Accelerator Guide
    Informatica® Data Quality 10.1.1 HotFix 1 Accelerator Guide Informatica Data Quality Accelerator Guide 10.1.1 HotFix 1 November 2017 © Copyright Informatica LLC 2009, 2018 This software and documentation are provided only under a separate license agreement containing restrictions on use and disclosure. No part of this document may be reproduced or transmitted in any form, by any means (electronic, photocopying, recording or otherwise) without prior consent of Informatica LLC. U.S. GOVERNMENT RIGHTS Programs, software, databases, and related documentation and technical data delivered to U.S. Government customers are "commercial computer software" or "commercial technical data" pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, the use, duplication, disclosure, modification, and adaptation is subject to the restrictions and license terms set forth in the applicable Government contract, and, to the extent applicable by the terms of the Government contract, the additional rights set forth in FAR 52.227-19, Commercial Computer Software License. Informatica, the Informatica logo, and PowerCenter are trademarks or registered trademarks of Informatica LLC in the United States and many jurisdictions throughout the world. A current list of Informatica trademarks is available on the web at https://www.informatica.com/trademarks.html. Other company and product names may be trade names or trademarks of their respective owners. Portions of this software and/or documentation are subject to copyright held by third parties. Required third party notices are included with the product. See patents at https://www.informatica.com/legal/patents.html. DISCLAIMER: Informatica LLC provides this documentation "as is" without warranty of any kind, either express or implied, including, but not limited to, the implied warranties of noninfringement, merchantability, or use for a particular purpose.
    [Show full text]
  • Privacy Risks from Public Data Sources
    Privacy Risks from Public Data Sources Zacharias Tzermias Panagiotis Papadopoulos Sotiris Ioannidis Vassilis Prevelakis FORTH-ICS, FORTH-ICS, FORTH-ICS, IDA, TUBS, Greece Greece Greece Germany 1. Introduction Pavlos’ story is very commonplace and highly predictable: his phone rung early one morning and the person who called identified herself as the representative of a well known national bank. She then asked Pavlos to confirm some personal information such as his tax registration number, address, identification card number, and so on. When Pavlos confirmed that all the provided information was correct, she told him that for identification purposes she needed his credit card number, expiration date and a three digit number printed on the back. He later told the police that since the person on the phone knew so much about him, he assumed that she was who she claimed to be. In fact she was a fraudster trying to extract information from Pavlos which was used to purchase airline tickets using Pavlos’ credit card. But how did the person on the phone know so many personal details about Pavlos? In most likelihood, she got it from the Greek government. This is so, because, the Greek government, like many other governments and private concerns, has been amassing personal data in electronic form since the beginning of the computer era. In the past two decades, the prevalence of the Internet and the technological advances in storage technologies have enabled an unprecedented volume of personal data to be collected. While this data was stored in magnetic tapes sitting in silent basements, or even in large mainframes disconnected from the rest of the world, the risk for large scale loss of personal information was kept to a minimum.
    [Show full text]
  • Predefined Policies and Classifiers I Contents
    Contents Predefined Policies . 1 Data Loss Prevention policies . 1 Acceptable Use . 2 Content Protection. 3 Company Confidential and Intellectual Property (IP) . 3 Credit Cards . 8 Financial Data . 10 Protected Health Information (PHI) . 15 Personally Identifiable Information (PII) . 18 Regulations, Compliance and Standards. 28 Financial Regulations . 28 Payment Card Industry (PCI) . 32 Privacy Regulations . 33 US and Canada Federal Regulations . 65 Data Theft Risk Indicators . 75 Suspicious User Activity . 75 Indicators of Compromise . 77 Employee Discontent . 78 Quick Policies . 79 Web DLP policy . 79 Email DLP policy . 81 Mobile DLP policy . 81 Discovery policies . 82 Acceptable Use . 82 Company Confidential and Intellectual Property . 83 Employee Discontent . 85 Financial Information . 85 Indicators of Compromise. 88 Payment Card Information (PCI) . 89 Protected Health Information (PHI) . 90 Personally Identifiable Information (PII). 92 Regulations . 100 Suspicious User Activity. 102 Predefined Classifiers. 104 File-type classifiers . 105 NLP scripts . 109 Predefined Policies and Classifiers i Contents Dictionaries . 135 Regular Expression patterns . 147 ii TRITON AP-DATA TRITON AP-DATA Predefined Policies and Classifiers Predefined Policies and Classifiers | TRITON AP-DATA | Version 8.1.x For your convenience, Websense® TRITON® AP-DATA includes hundreds of predefined policies and content classifiers. Predefined Policies enable you to quickly and easily define what type of content is considered a security breach on your network. Predefined Classifiers can be used to detect events and threats involving secured data. Predefined Policies Predefined Policies and Classifiers | TRITON AP-DATA | Version 8.1.x Predefined policies enable you to quickly and easily define what type of content is considered a security breach on your network.
    [Show full text]
  • Infotype Detector Reference | Data Loss Prevention Documentation
    8/23/2020 InfoType detector reference | Data Loss Prevention Documentation InfoType detector reference Cloud Data Loss Prevention (DLP) uses information types—or infoTypes—to dene what it scans for. An infoType is a type of sensitive data, such as a name, email address, telephone number, identication number, or credit card number. Every infoType dened in Cloud DLP has a corresponding detector. Cloud DLP uses infoType detectors in the conguration for its scans to determine what to inspect for and how to transform ndings. InfoType names are also used when displaying or reporting scan results. For more in-depth information about infoType detectors, see InfoTypes and infoType detectors (/dlp/docs/concepts-infotypes). The Cloud DLP team releases new infoType detectors and groups periodically. To get the latest list of built-in infoTypes, call the infoTypes.list (/dlp/docs/reference/rest/v2/infoTypes/list) method of Cloud DLP. tant: Built-in infoType detectors are not a 100% accurate detection method. For example, they can't guarantee iance with regulatory requirements. You must decide what data is sensitive and how to best protect it. Google mends that you test your settings to make sure your conguration meets your requirements. Global InfoType Description ADVERTISING_ID Identiers used by developers to track users for advertising purposes. These include Google Play Advertising IDs, Amazon Advertising IDs, Apple's identierForAdvertising (IDFA), and Apple's identierForVendor (IDFV). AGE An age measured in months or years. CREDIT_CARD_NUMBER A credit card number is 12 to 19 digits long. They are used for payment transactions globally. CREDIT_CARD_TRACK_NUMBER A credit card track number is a variable length alphanumeric string.
    [Show full text]