Here's how you can handle data privacy and security questions in your visualization interview.

One of the biggest challenges as a data analyst, is to remain in complaint with any data privacy law and ensuring you have full understanding of the data you are about to use in your projects. Every data has its own confidentiality, depends on how and what part of the data you are willing to show in your projects.

Coincidentally, I know a lot about this. From my experience, definitely knowing the European GDPR is important since it is pretty strict. When working in insurance, other laws and data security measures also apply, such as VAIT, which raise deployment and data masking topics. Understanding these regulations and what it means for your deployment process or data which is not supposed to be shown, is crucial. Knowing the specifics of these laws will help you articulate how you ensure compliance in your visualizations, such as anonymizing personal data or obtaining consent for data usage or Masking data in general. Its important to know how it can be done in databases and BI-tools.

While working on confidential data (or in general any data), try to be as transparent, accountable, take proper consent, be unbiased and follow regular compliance. These may sound too technical, but this is how it works when you are working on a secure data!

Try your very best to encrypt the data you are working with. Too much exposure will sneak in weak authentication and access control on the data.

From my experience, never show data on a single person basis. Mask sensitive information in the database using masking policies. Implement row-level security to prevent unauthorized access and have a solid business intelligence security concept in mind before or during work. These practices are essential for maintaining data privacy and security throughout the entire process. This includes anonymizing data, setting up secure access controls, and ensuring that all team members are aware of and adhere to privacy policies. By doing so, you demonstrate a commitment to protecting sensitive information and maintaining trust with stakeholders. Sometimes its also needed by laws, such as GDPR, HIPAA, or VAIT.

Ive recently worked with the Snowflake database, and even if it was not me who implemented the solutions, data masking and tokenization were applied. Data Masking replaces sensitive data with fictitious but realistic data (***). For example, using Snowflake's dynamic data masking, you can obscure specific columns, such personal information based on user roles and access levels. This way, unauthorized users see masked values instead of actual data. 🔑 Tokenization substitutes sensitive data with unique tokens that have no exploitable meaning. Snowflake supports integrating tokenization solutions where original data is stored securely, and only tokens are used in databases and applications. Other databases probably have similar solutions.

Having strong security concepts in Power BI is essential for protecting sensitive data and ensuring compliance. When working with confidential data, especially in industries like insurance, robust security measures are non-negotiable. Implementing security concepts in Power BI, such as row-level security (RLS), helps control access to data at a granular level. RLS ensures that users can only see data relevant to their roles, preventing unauthorized access and enhancing data integrity. By configuring RLS, you can create a more secure and reliable data environment.

Try to focus on one area - you can not know them all. Specific regulations are dependent on industry and company. Get to know a certain industry and your knowledge will be very helpful and appreciated from future employers. :)