My favorite question to ask to fellow attendees last month at the Gartner Data & Analytics Summit in Frankfurt, Germany:
“So, what are you hoping to get out of this week?
I enjoyed meeting with a wide variety of professionals across the data and analytics spectrum at the event, all of whom had different reasons for being there.
- Some said they were looking for a solution that could solve their GDPR issues related to analytics.
- Others were working out what data for analytics, machine learning, and AI within their line of business will look like in the next 6-12 months.
- A common theme we heard from European-based CIOs, CDOs, Privacy Officers, Business Analysts, and Data Scientists included the need to discover, catalog, govern, secure, and access data with a mind towards privacy – and without slowing down their business.
Every single European with whom I spoke brought up GDPR. Also, organizations of all sizes, and across all industries, continue to struggle with what GDPR means for the collection and use of their data. Some attendees even told us that GDPR has contributed to them being scared of their own data.
Alas, this presented a natural segue into my Immuta presentation, “Can Privacy Exist With Machine Learning?” – essentially a conversation around how we got to where we are today in regard to privacy, particularly in data sets thought to be non-identifying, and techniques that can be used to provide both utility and privacy of data. (See the article “The End of Privacy” by Andrew Burt, our Chief Privacy Officer and Legal Engineer.)
Other conversations revolved around getting a handle on managing secure access to data spread across multiple sources. Whether on-premises and living in traditional systems like Hadoop, MS SQL, Oracle, etc., or in the cloud in new web services like Amazon EMR, Redshift, or Azure SQL Data Warehouse, organizations need a way to apply consistent controls on how to dynamically use that data.
With the ability to quickly discover and access data in a way that is legal, ethical, and compliant, businesses can unlock data that was previously considered too sensitive for analytics and data science. Having a platform that allows Data Owners, Data Governors, and Data Scientists/Analysts to collaborate with excited groups charged by their organization with becoming data and algorithm driven. These organizations also don’t want to be forced into a specific tool or application to securely access data.
Data Scientists and analysts want to be able to use software platforms they’re comfortable with and transition to new ones without constraint. Immuta’s standard access patterns (SQL, File System, HDFS, and Spark) allow for the flexibility of a single connection to disparate systems from any platform or language without having to learn and inject yet another API.
These conversations enforce Gartner’s Top Strategic Predictions for 2019 and Beyond: Continuous Change Amid AI, Privacy, Diversity, and Cloud: https://www.gartner.com/smarterwithgartner/gartner-top-strategic-predictions-for-2019-and-beyond/
I’m excited to be on team Immuta, working at the intersection of these very topics. Thank you to everyone who came to our talk, and stopped by the booth for a conversation and demo. We look forward to seeing you again!
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