Business Intelligence In The Palm Of Your Hand With Zing Data
Data Engineering Podcast
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Zing Data
Zing data really shines for the 40 to 50% of us on data teams right now, which is giving product usage data and user data. The reason Zing is probably not the best choice at present is that you need a Jupyter notebook in order to do deep analytical models. We still think that's a gap that we can really close in the next few months to a year. At present, this is where we are.
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Summary
Business intelligence is the foremost application of data in organizations of all sizes. The typical conception of how it is accessed is through a web or desktop application running on a powerful laptop. Zing Data is building a mobile native platform for business intelligence. This opens the door for busy employees to access and analyze their company information away from their desk, but it has the more powerful effect of bringing first-class support to companies operating in mobile-first economies. In this episode Sabin Thomas shares his experiences building the platform and the interesting ways that it is being used.
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 Sabin Thomas about Zing Data, a mobile-friendly business intelligence platform
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Zing Data is and the story behind it?
- Why is mobile access to a business intelligence system important?
- What does it mean for a business intelligence system to be mobile friendly? (e.g. just looking at charts vs. creating reports, etc.)
- What are the interaction patterns that don’t translate well to mobile from web or desktop BI systems?
- What are the new interaction patterns that are enabled by the mobile experience?
- What are the capabilities that a native app can provide which would be clunky or impossible as a web app on a mobile device?
- Who are the personas that benefit from a product like Zing Data?
- Can you describe how the platform (backend and app) are implemented?
- How have the design and goals of the system changed/evolved since you started working on it?
- Can you describe a typical workflow for a team that uses Zing?
- Is it typically the sole/primary BI system, or is it more of an augmentation?
- What are the most interesting, innovative, or unexpected ways that you have seen Zing used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Zing?
- When is Zing the wrong choice?
- What do you have planned for the future of Zing Data?
Contact Info
Parting Question
- 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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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