We are only just beginning to see the potential benefits of machine learning and advanced analytics: rapid fraud detection, hyper-targeted personalization engines, predictive analytics for inventory planning and price optimization, breakthroughs in drug discovery and precision medicine. All of it is powered by the massive amounts of data being created every day by people, processes, and IoT devices - and much of that data can be considered “sensitive” or “Personally Identifiable Information (PII)” under various data privacy and industry-specific regulations.
Data platform architects must grapple with figuring out how to make the organization’s data available while still meeting compliance requirements. Attempted solutions usually involve complex ETL or static data masking; however, these solutions don’t address the fact that completely removing the sensitive values also removes a lot of the data’s utility.
The answer lies with data de-identification, the process by which data values are transformed or obfuscated in order to protect sensitive information while leaving it in a form that is still useful for the business. At Okera, we believe that a flexible, scalable access control framework that leverages de-identification is key to realizing the benefits of Big Data and advanced analytics, without running afoul of regulatory compliance.
In this webinar, we’ll cover:
- An overview of why traditional methods to redact sensitive data are unsuitable for the modern enterprise analytics platform
- Anonymization vs. pseudonymization, and when it’s most appropriate to use either one
- The case for dynamic policy enforcement of de-identification types
- Examples of specific de-identification policies being used by F500 enterprises, and how to author them in Okera’s visual policy builder
Can't attend live? Register anyway and you'll receive the recording after the event.