The promise of online services is that they will make your life easier in exchange for collecting data about you. The reality is that they use more information than you realize for purposes that are not what you intended. There have been many attempts to harness all of the data that you generate for gaining useful insights about yourself, but they are generally difficult to set up and manage or require software development experience. The team at Prifina have built a platform that allows users to create their own personal data cloud and install applications built by developers that power useful experiences while keeping you in full control. In this episode Markus Lampinen shares the goals and vision of the company, the technical aspects of making it a reality, and the future vision for how services can be designed to respect user’s privacy while still providing compelling experiences.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I’m interviewing Markus Lampinen about Prifina, a platform for building applications powered by personal data that is under the user’s control
Interview
Introduction
How did you get involved in the area of data management?
Can you describe what Prifina is and the story behind it?
What are the primary goals of Prifina?
There has been a lof of interest in the "quantified self" and different projects (many that are open source) which aim to aggregate all of a user’s data into a single system for analysis and integration. What was lacking in the ecosystem that makes Prifina necessary/valuable?
What are some of the personalized applications for this data that have been most compelling or that users are most interested in?
What are the sources of complexity that you are facing when managing access/privacy of user’s data?
Can you describe the architecture of the platform that you are building?
What are the technological/social/economic underpinnings that are necessary to make a platform like Prifina possible?
What are the assumptions that you had when you first became involved in the project which have been challenged or invalidated as you worked through the implementation and began engaging with users and developers?
How do you approach schema definition/management for developers to have a stable implementation target?
How has that schema evolved as you introduced new data sources?
What are the barriers that you and your users have to deal with when obtaining copies of their data for use with Prifina?
What are the potential threats that you anticipate for users gaining and maintaining control of their own data?
What are the untapped opportunities?
What are the topics where you have had to invest the most in user education?
What are the most interesting, innovative, or unexpected ways that you have seen Prifina used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Prifina?
When is Prifina the wrong choice?
What do you have planned for the future of Prifina?
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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