How do you balance data privacy and data utility when integrating data from different domains or sectors?

Balancing data privacy and utility in cross-domain data integration is essential. This involves adhering to privacy regulations, anonymizing or encrypting sensitive data, and granting access on a need-to-know basis. Clear data governance policies should outline how data can be shared, stored, and used, ensuring compliance with relevant laws. Regular audits and privacy impact assessments help identify and address risks. Collaboration with domain experts and data owners facilitates mutual understanding and trust. The goal is to maximize data utility while safeguarding individual privacy and maintaining data security, ultimately supporting data integration across diverse domains and sectors.

In short- It is essential to understand the data lifestyle of the cross domain data that consumed horizontally. That helps in defining your data security and privacy innovocation within data pipelines that drives data consumption needs. Follow the loose coupling architecture that easy to change the specific piece within pipelines instead of big bang aaproach. At the same time register your associated business terms within your data governance tool that influences data quality rules that needs to be enhanced to defined.

Data quality is key for any data within your platform. Question is when, where, why and how. Based on how well your data integration pipelines are defined and your data governance strategy that will help driving DQ life cycle consistently across the enterprise. All depends on your environment. Complexities and its current state. Hence, use guiding score of the data and define dq priorities and address it accordingly.

The data privacy and security techniques will set up the scope of the functionality that users will consume/use on the tables from data model. So evaluate adn define what is the purpose of the data integration with several section, after define at first the access controls to meet all possible use cases and then apply data anonymization, pseudo-anonymization (data masking for example) or encryption to maximize the security of your data while preserver the maximum functionality possible based in the use cases.

Encryption techniques are essential for protecting sensitive data during storage and transmission. By encrypting data, you can ensure that even if an unauthorised person gains access to the data, they won't be able to read or use it. Encryption can also help protect against data breaches and cyber-attacks.

Instead of building your own data encryption, masking or tokenization, try to utilize industry standard data masking tools such as protegrity or IMMUTA that works based on your tags that assigned or mapped within your governance tool for a given business term for fine grain and course grain data access needs based your data security and privacy framework.

Data integration must be followed loosely coupled architecture with possible lowest grained data that can further be used for many use cases. Also important to leverage data virtualization tool like Denedo for further publishing info for consumption. Even Alteryx kind of tools for further aggregation prior to consumption. Keep your data integration pipelines as simpler as possible. This mindset of loose coupling drivers future adoption of the technological modernization within your enterprise data hub.

Try to utilize your business data quality - such as integrity, consistency, data completeness, reconciliation, etc. Dashboards back on your governance tool that business can view the health of there data from one tool I stead of having too many.