Privacy is a conundrum for data-driven businesses, and it can seem self-defeating. Analytics projects must use privacy protections to comply with major regulations, such as HIPAA, GDPR, and CCPA, but many organizations find their efforts produce data that is no longer useful in analytics.

There’s no silver bullet to make data safe. No single technique provides the desired outcomes for every type of data, in every use case. Safeguarding data is a contextual challenge. Varied and granular controls are needed depending on the data, the use case, the users, and their location.

Fortunately, there is a way to balance both utility and privacy.

This free checklist is a step by step guide on how to prepare, implement, and automate the data to support both utility and privacy of your important business data.