68. What That Means with Camille: Data Anonymization

<p><span style="font-weight: 400;">In this episode of</span> <em><span style="font-weight: 400;">What That Means</span></em><span style="font-weight: 400;">, Camille discusses data anonymization with Kristin Ulrich, Senior Solutions Specialist at SAP for HANA architecture. Find out why data privacy should matter to you.  </span></p> <p> </p> <p><span style="font-weight: 400;">The conversation covers:</span></p> <p><span style="font-weight: 400;">-  What data anonymization means </span></p> <p><span style="font-weight: 400;">-  The difference between pseudonymization and anonymization   </span></p> <p><span style="font-weight: 400;">-  What questions you need to ask before sharing data with another company</span></p> <p><span style="font-weight: 400;">-  How different legislations impact data anonymization and data sharing </span></p> <p><span style="font-weight: 400;">...and more. Don’t miss it! </span></p> <p> </p> <p><em><span style="font-weight: 400;">The views and opinions expressed are those of the guests and author and do not necessarily reflect the official policy or position of Intel Corporation.</span></em></p> <p> </p> <p><strong>Here are some key take-aways:</strong></p> <p><span style="font-weight: 400;">-  Data anonymization means information is irreversibly altered and can no longer be identified directly, or indirectly</span></p> <p><span style="font-weight: 400;">-  The type of parameters or methodology applied to a data set will depend on on each individual data set</span></p> <p><span style="font-weight: 400;">-  There is always a level of risk involved with anonymization, you cannot guarantee complete anonymity </span></p> <p><span style="font-weight: 400;">-  If you are working with companies in different countries make sure you understand any data regulations laws, in some cases they are continually evolving.  </span></p> <h2> </h2> <p><strong>Some interesting quotes from today’s episode:</strong></p> <p><span style="font-weight: 400;">“And every time we speak about data anonymization, it's r