Listen to articleLength(6 minutes)Some organizations may have a love-hate relationship with data privacy. As the promise of harnessing data has expanded from simple computational reporting to predictive analytics and beyond, so too has enterprise responsibility for prioritizing data privacy and ethics—which may lead to some challenges.“Organizations know they need to maintain privacy, confidentiality, and security for their data assets, while also responding to a growing need to leverage that data more broadly,” said Juan Tello, a principal at Deloitte Consulting LLP and chief data officer for Deloitte, during a recent episode of Deloitte’s CDO LinkedIn Live series, “Winning with Data.”“While limitless possibilities may excite people, unlimited access to data can feel problematic,” says Mike Bechtel, a managing director at Deloitte Consulting LLP and Deloitte’s chief futurist. Individuals are increasingly seeking transactional trust related to the collection and use of their personal data and expect a clear exchange of value when they share it. “We need to find better ways to make AI, machine learning, and data science clearer and more auditable and understandable,” he says. “If we can trust it, we can govern it.

It’s about shades of gray, not big dumb binaries.”Such nuances, of course, can be difficult to manage and operationalize. Ultimately, intelligent data solutions should be more transparent to all stakeholders, including regulators, employees, and customers. Organizations typically encounter three major challenges in making it so: adapting to changing regulations and consumer preferences, evaluating and adopting emerging data privacy technologies, and bringing data together meaningfully.Adapting to Changing Regulations and Consumer PreferencesNew data privacy regulations can disrupt the way organizations manage data, from issues of data privacy to questions of data localization.

Evaluating and Adopting Emerging Data Privacy TechnologiesA host of new cloud-enabled technologies are simplifying the mechanics of data sharing across and between organizations and enabling more transparency. Early in the COVID-19 pandemic, for instance, researchers, medical authorities, and drug makers were able to pool clinical data on shared platforms to accelerate the development of treatments and vaccines.“Organizations can increasingly share more data not only within the four walls of the enterprise but across entire value chains, with important ramifications for initiatives like sustainability,” Tello says.Emerging technologies and approaches such as fully homomorphic encryption, data clean rooms, and federated data analysis may enable data sharing with greater privacy protections, Bechtel says. However, technologies alone can’t solve data privacy challenges.

“Organizations should have governance and privacy policies in place to make sure data sharing doesn’t result in a whole bucket of tears,” he says.Leaders can determine where to invest their data privacy energies by going back to the basics, Blalock continues. “Ask the question, ‘What is the problem we are actually trying to solve?’” she says. “It might reveal a need to invest in new software or enhanced training for data literacy skills, or point to a necessary policy change to increase data transparency.”