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© 2007 Princeton Softech, Inc. Achieve Siebel Excellence Best Practices Solution in Archiving and Test Data Management Northern California OAUG, Training.

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Presentation on theme: "© 2007 Princeton Softech, Inc. Achieve Siebel Excellence Best Practices Solution in Archiving and Test Data Management Northern California OAUG, Training."— Presentation transcript:

1 © 2007 Princeton Softech, Inc. Achieve Siebel Excellence Best Practices Solution in Archiving and Test Data Management Northern California OAUG, Training Day January 2007 Stephen Mohl Siebel Specialist

2 © 2007 Princeton Softech, Inc. 2 What If …? What if you could easily identity and remove outdated data from your Siebel database? What if your users could still view the archived data from within the Siebel application? What if you could easily populate test databases with masked production data?

3 © 2007 Princeton Softech, Inc. 3 Princeton Softech Optim ™  Manage enterprise application data throughout the information lifecycle  Apply business rules to subset, archive, store and access enterprise application data  Protect data privacy  Leverage a single solution to support and scales across applications, databases, and platforms  Optimize the business value of your IT infrastructure

4 © 2007 Princeton Softech, Inc. 4 What is Enterprise Data Management?

5 © 2007 Princeton Softech, Inc. 5 Manage application performance and data volume growth cost effectively. Ensure regulatory compliance by maintaining data needed for potential audit. Preserve data snapshot prior to upgrade. Siebel Archiving Business Drivers

6 © 2007 Princeton Softech, Inc. 6 Comprehensive Performance Management Strategy Add Capacity Train Users Reconfigure Application Tune System Archive Outdated Data

7 © 2007 Princeton Softech, Inc. 7 Siebel Archiving Solution Results  Archive subsets of Siebel data -Complete business object -Audit-ready “snapshot in time”  Delete inactive, historical data from production  Archive associated attachments from file system  Locate and browse archived data  Combined Reporting of active and inactive data

8 © 2007 Princeton Softech, Inc. 8 Siebel Archiving Solution Results  Store data on cost-effective media -Access across medium types  Access archive data across Siebel versions -Archive in 7.7, but access in 7.8  Provide scalable, enterprise support  Selectively restore data for additional business processing -Production/Reporting/Auditing

9 © 2007 Princeton Softech, Inc. 9 Integration Schematic Siebel On Premise Applications Local DB Siebel Mobile Applications Development Environment Siebel Tools Design PST EAI Siebel Application Server Business Logic Layer and Core Service Data Layer Services User Interface Services Bus. Process Bus. Process Siebel Teller Applications Multiple Client Device Support Siebel Portal Framework Federated Data Sources Oracle PSFT Legacy JDEdwards OLTP Siebel Universal CustomerMaster Sync SiebelRepository Web Server OLTP Files PSTCX_Table Runtime Archive File Directory Archive Files

10 © 2007 Princeton Softech, Inc. 10 Translating Siebel Object Model to Optim- Siebel  The Business Object becomes an Access Definition (AD)  BO’s primary BC table becomes Optim’s start table  Links, Joins and Multi value links determine: -Tables included in the Access Definition - Relationships between the tables -Separate Access Definitions for Optim-Siebel Archiving that use ‘Cascade Delete property’ determine the setting of Optim ‘Delete After Archive’

11 © 2007 Princeton Softech, Inc. 11 Archive Process Optim Server Archive File Directory Staging Area -- ---- ---- ---- ------- ---- CUSTOMERS -- -- ------ -- --------- ---- -- -- ------ -- --------- ---- ORDERS -- ---- ---- ---- ------- ---- -- ---- ---- ---- ------- ---- -- ---- ---- ---- ------- ---- -- ---- ---- ---- ------- ---- -- ---- ---- ---- ------- ---- DETAILS -- ---- ---- ---- ------- ---- CUSTOMERS -- -- ------ -- --------- ---- -- -- ------ -- --------- ---- ORDERS -- ---- ---- ---- ------- ---- -- ---- ---- ---- ------- ---- -- ---- ---- ---- ------- ---- -- ---- ---- ---- ------- ---- -- ---- ---- ---- ------- ---- DETAILS Production DB Access Archive Purge Restore Storage Archive Files

12 © 2007 Princeton Softech, Inc. 12 Support for:  Databases -Oracle, Sybase, Informix, DB2, UDB, SQL/Server -Complex interrelationships  Applications -Custom & Packaged, Legacy, Oracle E-Business Suite, PeopleSoft Enterprise, JD Edwards EnterpriseOne, Siebel, Amdocs CRM -Multiple, interrelated applications, databases & platforms Archiving Process Flow OLTP Production OLTP Optim Engine Optim Engine Template  NT, Solaris, AIX, HP/UX  OS/390, z/OS  Archive while online for 24x7 operations  Access DB2 from Unix  Place DB2 Archive on Unix  Templates -Define a business object -Created from schema RI, Erwin import, GUI or a combination -Multiple DBMS support  Out of the box -Siebel 7.5, 7.7, & 7.8  Establish policies -Constraints or condition checks, used to determine eligibility -Time or other parameters provided at run time Archive  Optim Archive -Compressed -Secured -Indexed retrieval  Widest selection of Information Lifecycle options -SAN, NAS -Nearline (Centera, RISS, DR550, Intellistore) -Offline (Tape, CD, Optical) -Enterprise Vault, Tivoli  Industry standard archive -Does not require a DBMS -Does not require the application (Decommissioning) -Cannot be altered  Allows for deferred purge operation -Auditable -Proves data archived is identical to purged data -Allows for user review prior to purge

13 © 2007 Princeton Softech, Inc. 13 Ensure Referential Integrity Ex: Activities Archive File

14 © 2007 Princeton Softech, Inc. Enterprise Test Data Management

15 © 2007 Princeton Softech, Inc. 15 Challenges of Siebel Test Data Management  Siebel doesn’t provide a solution or methodology for TDM  Siebel has a very complex data model consisting of many tables with multiple relationships between tables  Siebel Industry Applications share a common repository -Each application doesn’t use all tables and relationships that are found in Siebel tools  Configuration at each customer will determine the final use case

16 © 2007 Princeton Softech, Inc. 16 Solution Goals  Extract precise subsets of related data to build realistic, “right-sized” test databases -Create referentially intact subsets -Remove the bulk of production data -Minimize the load on testing and staging servers  Speed iterative testing tasks with reusable processing definitions and Extract Files to ensure consistency

17 © 2007 Princeton Softech, Inc. 17 Benefits  Maximize allocated disk space  Increase number of test/dev environments  Reduce infrastructure costs  Realize development and test efficiencies -Reduce the cycle times for test upgrades -Reduces time and resources required to backup and maintain non- production environments

18 © 2007 Princeton Softech, Inc. 18 Repeat ?*%$! Wait Production Database Copy Share test database with everyone else Extract RI Accuracy? Right Data? Expensive, Dedicated Staff, Ongoing Responsibility. Changes After Complex Subject to Change Extract Write SQL Manual examination: Right data? What Changed? Correct results? Unintended Result? Someone else modify? After Production Database Copy Changes Current Practice? Clone Production Request for Copy #1 - Clone Production #2 - Write SQL

19 © 2007 Princeton Softech, Inc. 19 Training Production Database Conceptual Options Production Clone Reduced Clone Resized Clone Tables are Truncated, but database footprint still the same Database resized and re-indexed QA Dynamically load relationally intact data set’s and objects based on selection criteria's Stage Test

20 © 2007 Princeton Softech, Inc. 20  Compare the "before" and "after" data from an application test  Compare results after running modified application during regression testing  Identify differences between separate databases  Audit changes to a database  Compare analyzes complete sets data – finding changes in rows in tables -Single-table or multi-table compare -Creates compare file of results -Displays results on screen Comparing Data

21 © 2007 Princeton Softech, Inc. 21 What about data privacy?  Provide the fundamental components of test data management and enable organizations to de-identify, mask and transform sensitive data  Companies can apply a range of transformation techniques to substitute customer data with contextually-accurate but fictionalized data to produce accurate test results  By masking personally-identifying information, it protects the privacy and security of confidential customer data, and supports compliance with local, state, national, international and industry-based privacy regulations

22 © 2007 Princeton Softech, Inc. 22 De-Identifying Test Data  Removing, masking or transforming elements that could be used to identify an individual -Name, address, telephone, SSN / National Identity number  No longer confidential; therefore acceptable to use in open test environments  Masked or transformed data must be appropriate to the context -Consistent formatting (alpha to alpha) -Within permissible range of values

23 © 2007 Princeton Softech, Inc. 23 Transformation Techniques  String literal values  Character substrings  Random or sequential numbers  Arithmetic expressions  Concatenated expressions  Date aging  Lookup values  Intelligence

24 © 2007 Princeton Softech, Inc. 24 Example: Bank Account Numbers  First Financial Bank’s account numbers are formatted “123-4567” with the first three digits representing the type of account (checking, savings, or money market) and the last four digits representing the customer identification number  To mask account numbers for testing, use the actual first three digits, plus a sequential four-digit number  The result is a fictionalized account number with a valid format: -“001-9898” becomes “001-1000” -“001-4570” becomes “001-1001”

25 © 2007 Princeton Softech, Inc. 25 Example: First and Last Name  Direct Response Marketing, Inc. is testing its order fulfillment system  Fictionalize customer names to pull first and last names randomly from the Customer Information table: -“Gerard Depardieu” becomes “Ronald Smith” -“Lucille Ball” becomes “Elena Wu” - Optim ships with over 5,000 male/female names and over 80,000 last names

26 © 2007 Princeton Softech, Inc. 26 Example: Addresses  Direct Response Marketing, Inc. is testing its order fulfillment system  Fictionalize customer addresses to pull an entire address from the Customer Information table: -“111 Campus Drive Princeton NJ 08540 ” becomes “1223 E. 12 th Street NY, NY 10079” - Optim ships with over 100,000 valid CASS addresses

27 © 2007 Princeton Softech, Inc. 27 Example: Intelligence  Generating valid social security numbers (as defined by the US Social Security Administration)  Generate valid credit card numbers (as defined by credit card issuers)  Generate desensitized e-mail addresses  Generate Email address based on format: name@domain

28 © 2007 Princeton Softech, Inc. 28 Social Security Numbers and Credit Cards F. NameL. NameCredit Card#SSN# JohnDenver5298774132478855254-77-6644 VanessaJones4324115574123654154-74-7788 F. NameL. NameCredit Card#SSN# JohnDenver 5326458711224956854-77-6644 VanessaJones 4972584612457744154-74-7788 Production Database Data before Masking Data after Masking… Masked with Valid CC# and SS# How are these numbers valid? Test Database Valid For Social Security NumbersFor Credit Card Numbers A Social Security Number (SSN) consists of nine digits. The first three digits is called the "area number'. The central, two- digit field is called the "group Number". The final four-digit field is called the "serial Number". All numbers must fit the latest available criteria for each section. Most credit card numbers are encoded with a "Check Digit". A check digit is a digit added to a number (either at the end or the beginning) that validates the authenticity of the number. A simple algorithm is applied to the other digits of the number which yields the check digit.

29 © 2007 Princeton Softech, Inc. 29 Using Custom Masking Exits  Apply complex data transformation algorithms and populate the resulting value to the destination column  Selectively include or exclude rows and apply logic to the masking process  Valuable where the desired transformation is beyond the scope of supplied Column Map functions  Example: Generate a value for CUST_ID based on customer location, average account balance, and volume of transaction activity

30 © 2007 Princeton Softech, Inc. 30 Implementation Time Line Project Scope Identify Application(s) Access requirements Application locations Develop Resource Plan Training Plan Project Plan Project Start PLANNING AND DISCOVERY Analysis Analyze Infrastructure IT & Business Processes Enhanced access Define Retention policies Archive location Business objects Update Resource plan Project Plan IMPLEMENTATION Design & Build Testing Production Design Architecture Business objects Conduct Team training Prepare Environments Test Plans Build (Optional) Business Objects Enhanced data access Conduct End user training Test Archive Data Access Backup Prepare Go live plan Production Environment Go Live Recurring archive process Project Team Activated Project Delivered SUPPORT Review Support Conduct Project Review Value measurement Prepare Success Story Provide User support Monitor Maintenance Issue resolution

31 © 2007 Princeton Softech, Inc. Questions & Answers

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