Friday, August 9, 2013

OPTIM - FAQ's -- III

Optim to convert critical information such as Social Security number (TRANS SSN), credit card number (TRANS CCN), and e-mail IDs (TRANS EML). There are several other functions available in Optim in the data privacy area that provide various masking functions. For example, some of the functions include:
LOOKUP, a function that uses a lookup table to determine the destination column value

HASH_LOOKUP, a function that determines the destination column value from a lookup table according to a value derived from a source column

RAND_LOOKUP, a random lookup function that selects a value for the destination column from a lookup table based on a random number as the subscript in the lookup table

TRANS SSN flags and their descriptions
Flags   Description
n          Generates a random area number and an appropriate group number and serial number.
r          Generates a random area number corresponding to the state of the source SSN and an appropriate group number and serial number.
v          Validates the source group number to ensure the SSN has used it.
-           Generates an SSN with dashes separating the fields (for example, 123-45-6789). Requires a character-type destination column at least 11 characters long.

The TRANS CCN function is used to generate valid and unique credit card numbers (CCNs). A CCN usually consists of a six-digit issuer identifier followed by a variable-length account number and a single-check digit as the final number. By default, TRANS CCN algorithmically generates a consistently altered CCN, but it can also generate a random value for CCN.

The execution steps followed in this section (used to mask credit card numbers) are similar to the steps followed during the execution of the SSN scenarios. All three scenarios described in this section differ from each other with respect to the source file, control file, destination file, and flags used in TRANS CCN function.

TRANS CCN flags and their descriptions

Flags   Description
n          Generates a random CCN, not based on a source value, that includes a valid issuer
identifier associated with American Express, MasterCard, VISA, or Discover.
r          Generates a random CCN that includes the first four digits of the source issuer identifier.
6          Generates a random CCN that includes the first six digits of the source issuer identifier.

TRANS EML

The TRANS EML function is used to generate an e-mail address. An e-mail address has two parts—the user name and the domain name, separated by the at sign (@).

The TRANS EML function generates e-mail addresses based on either the destination data or a literal concatenated with a sequential number. The domain name can be formed using either the source data or randomly chosen from a long list of e-mail service providers. E-mail addresses can also be converted to upper or lower case.

The execution steps followed in this section (where the scenarios will work on e-mail accounts) are similar to the steps followed during the execution of SSN scenarios. Hence, usage of only one is given here, and the rest can be executed in the same way with only the change of the flag values.

TRANS EML flags and their descriptions
Flags   Description
n          Ignores the source value and generates an e-mail address with a random domain name from a list of large e-mail service providers.
.           Separates the name1col and name2col values with a period (.).
-           Separates the name1col and name2col values with an underscore (_).
i           Uses only the first character of the name1col value.
l           Converts the e-mail address to lower case.
u          Converts the e-mail address to upper case.

Note: Some of the articles are grab from various Websites / Blogs.

What is Data Masking

Protects sensitive information by transforming it into de-identified, realistic-looking data while retaining original data properties.
  • Data remains relevant and meaningful.
  • Preserves the shape and form of individual fields.
  • Preserves intra-record relationships.
  • Preserves join / foreign key relationships.
  • Minimize risk of a data security breach.
  • Improve compliance with data privacy laws & regulations.
  • Reduce costs through outsourcing & off-shoring. 
Challenges while Data Masking ?
  • Data Utility - masked data must look and act like the real data.
  • Data Relationships - must be maintained after masking.
  • Existing Business Processes - needs to fit in with existing processes.
  • Ease of Use - must balance ease of use with need to intelligently mask data.
  • Customizable - must be able to be tailored to specific needs.

Note: Some of the articles are grab from various Websites / Blogs.

Data Growth


Data Growth
The growth in application data may causes
  • Slow application performance
  • Cost associated with data storage and maintenance increases significantly
  • Application data management increases application complexity and cost in application upgrades
Data Privacy policies and Regulation

Customer data are sensitive in nature. It should be protected to prevent any kind of misuse. This is also requires to fulfill the compliance and data regulation

Test Data Management
Different development and testing environment frequently needs clones of production data for different purpose.





Note: Some of the articles are grab from various Websites / Blogs.

What is Enterprise Data Management

  • A business objective – focused on the creation of accurate, consistent and transparent data content.
  • EDM emphasizes data precision, granularity and meaning and is concerned with how the content is integrated into business applications as well as how it is passed along from one business process to another. 
What are the Challenges
  • Highly dispersed, fragmented and duplicated data – No enterprise view
  • No standards on internally and externally generated data – Low Data quality
  • Usage of disparate technologies for implementing identical processes in different department
  • Nomenclature and standardization issues – Lack of centralized Metadata management
  • Non-integrated processes and information – poor Master data management
  • Uncontrolled and/or unauthorized access to data – Data security issues
  • Lack of governance and ownership of data assets
  • Privacy, legal and regulatory compliance



Note: Some of the articles are grab from various Websites / Blogs.