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Achieve Siebel Excellence Best Practices Solution in Archiving and Test Data Management Northern California OAUG, Training Day January 2007. Stephen Mohl Siebel Specialist. What If …?. What if you could easily identity and remove outdated data from your Siebel database?.

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Achieve Siebel ExcellenceBest Practices Solution inArchiving and Test Data ManagementNorthern California OAUG, Training DayJanuary 2007

Stephen Mohl

Siebel Specialist

what if
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What if you could easily identity and remove outdated data from your Siebel database?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?

princeton softech optim
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
siebel archiving business drivers
Siebel Archiving Business Drivers

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.

comprehensive performance management strategy
Comprehensive Performance Management Strategy

Tune System

Add Capacity

Reconfigure

Application

Train

Users

Archive

Outdated

Data

siebel archiving solution results
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
siebel archiving solution results8
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
integration schematic

Siebel

Mobile Applications

Local DB

Design

PST

Runtime

Archive

File Directory

Archive Files

PST

CX_Table

Integration Schematic

Siebel On Premise Applications

Siebel Teller Applications

Siebel Portal

Framework

Multiple Client Device Support

Sync

Web Server

Development

Environment

Siebel Tools

Siebel Application Server

User Interface Services

Business Logic Layer and Core Service

Bus. Process

Siebel

Repository

EAI

Data Layer Services

Federated Data Sources

OLTP

Files

Siebel Universal

Customer

Master

Oracle

PSFT

OLTP

Legacy

JD

Edwards

translating siebel object model to optim siebel
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’
archive process

CUSTOMERS

CUSTOMERS

-- ---- ---- ---- ------- ------ ---- ---- ---- ------- ----

-- ---- ---- ---- ------- ------ ---- ---- ---- ------- ----

ORDERS

ORDERS

-- -- ------ -- --------- ------ -- ------ -- --------- ------ -- ------ -- --------- ------ -- ------ -- --------- ----

-- -- ------ -- --------- ------ -- ------ -- --------- ------ -- ------ -- --------- ------ -- ------ -- --------- ----

DETAILS

DETAILS

-- ---- ---- ---- ------- ------ ---- ---- ---- ------- ------ ---- ---- ---- ------- ------ ---- ---- ---- ------- ------ ---- ---- ---- ------- ----

-- ---- ---- ---- ------- ------ ---- ---- ---- ------- ------ ---- ---- ---- ------- ------ ---- ---- ---- ------- ------ ---- ---- ---- ------- ----

Archive Process

Production DB

Archive

Purge

Optim

Server

Storage

Archive

File Directory

Archive Files

Restore

Access

Staging Area

archiving process flow

Optim

Engine

Archive

Template

Template

Template

Archiving Process Flow

Production

OLTP

OLTP

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
  • 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
  • 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
  • NT, Solaris, AIX, HP/UX
  • OS/390, z/OS
  • Archive while online for 24x7 operations
  • Access DB2 from Unix
  • Place DB2 Archive on Unix
ensure referential integrity
Ensure Referential Integrity

Ex: Activities Archive

File

challenges of siebel test data management
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
solution goals
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
benefits
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
current practice

Clone Production

  • Complex
  • Subject to
  • Change

Request for Copy

Extract

Wait

After

Production

Database

Copy

Changes

Extract

After

Changes

Manual examination:

Right data?

What Changed?

Correct results?

Unintended Result?

Someone else modify?

Expensive,

Dedicated Staff,

Ongoing

Responsibility.

  • RI Accuracy?
  • Right Data?
Current Practice?

#1 - Clone Production

#2 - Write SQL

Repeat ?*%$!

Write SQL

Production

Database

Copy

Share test database

with everyone else

slide19

Conceptual Options

Tables are Truncated,

but database footprint

still the same

Production

Database

Production

Clone

Database resized

and re-indexed

Reduced

Clone

Resized

Clone

Training

Stage

Dynamically load

relationally intact data set’s

and objects based on selection

criteria's

QA

Test

comparing data
Comparing Data
  • 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
what about data privacy
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
de identifying test data
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
transformation techniques
Transformation Techniques
  • String literal values
  • Character substrings
  • Random or sequential numbers
  • Arithmetic expressions
  • Concatenated expressions
  • Date aging
  • Lookup values
  • Intelligence
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”

Example: Bank Account Numbers

Complexity 1

example first and last name
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

Complexity 2

example addresses
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. 12th Street NY, NY 10079”
    • Optim ships with over 100,000 valid CASS addresses

Complexity 3

example intelligence
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

Complexity 3

social security numbers and credit cards
Social Security Numbers and Credit Cards

Production Database

Data before Masking

Test Database

Valid

Valid

Data after Masking…

Masked with Valid CC# and SS#

How are these numbers valid?

using custom masking exits
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

Complexity 4

implementation time line

Project

Start

Project Team

Activated

Project

Delivered

SUPPORT

IMPLEMENTATION

PLANNING AND DISCOVERY

Project Scope

Analysis

Design & Build

Testing

  • Identify
    • Application(s)
    • Access requirements
    • Application locations
  • Develop
    • Resource Plan
    • Training Plan
    • Project Plan

Production

  • Analyze
    • Infrastructure
    • IT & Business
    • Processes
    • Enhanced access
  • Define
    • Retention policies
    • Archive location
    • Business objects
  • Update
    • Resource plan
    • Project Plan

Review

  • Design
    • Architecture
    • Business objects
  • Conduct
    • Team training
  • Prepare
    • Environments
    • Test Plans
  • Build (Optional)
    • Business Objects
    • Enhanced data access

Support

  • Conduct
    • End user training
  • Test
    • Archive
    • Data Access
    • Backup
  • Prepare
    • Go live plan
    • Production Environment
    • Go Live
    • Recurring archive process
  • Conduct
    • Project Review
    • Value measurement
  • Prepare
    • Success Story
  • Provide
    • User support
    • Monitor
    • Maintenance
    • Issue resolution
Implementation Time Line