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.

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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.





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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.