It's indeed a tricky balance between data utility and privacy. One effective strategy is data minimization, where companies only collect PIA that's necessary for a specific purpose. Additionally, many are investing in robust encryption and pseudonymization techniques to protect data without compromising its utility. They’re also using privacy impact assessments (PIAs) regularly to identify risks and update practices accordingly. With AI and big data, differential privacy is gaining traction as it allows for data analysis without exposing individual details. It’s about creating a culture of privacy where regular training and awareness are key to staying compliant.