Business Intelligence Beyond The Dashboard With ClicData
Data Engineering Podcast
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Are Machine Learning and Data Science Different From Business Intelligence?
I'm always concerned that, you know, it's being treated as two separate topics. Sometimes i see it as a single topic, which is, datais datais data. You're just working with data one way or the other. So we're trying to kind of create our own, i guess, opinion of where machine learning sits in this entire pro right? We have some ideas, and i'd love to hear more as well.
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Summary
Business intelligence is often equated with a collection of dashboards that show various charts and graphs representing data for an organization. What is overlooked in that characterization is the level of complexity and effort that are required to collect and present that information, and the opportunities for providing those insights in other contexts. In this episode Telmo Silva explains how he co-founded ClicData to bring full featured business intelligence and reporting to every organization without having to build and maintain that capability on their own. This is a great conversation about the technical and organizational operations involved in building a comprehensive business intelligence system and the current state of the market.
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 Telmo Silva about ClicData,
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what ClicData is and the story behind it?
- How would you characterize the current state of the market for business intelligence?
- What are the systems/capabilities that are required to run a full-featured BI system?
- What are the challenges that businesses face in developing in-house capacity for business intelligence?
- Can you describe how the ClicData platform is architected?
- How has it changed or evolved since you first began working on it?
- How are you approaching schema design and evolution in the storage layer?
- How do you handle questions of data security/privacy/regulations given that you are storing the information on behalf of the business?
- In your work with clients what are some of the challenges that businesses are facing when attempting to answer questions and gain insights from their data in a repeatable fashion?
- What are some strategies that you have found useful for structuring schemas or dashboards to make iterative exploration of data effective?
- What are the most interesting, innovative, or unexpected ways that you have seen ClicData used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on ClicData?
- When is ClicData the wrong choice?
- What do you have planned for the future of ClicData?
Contact Info
- @telmo_clicdata on Twitter
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 show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
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Links
- ClicData
- Tableau
- Superset
- Pentaho
- D3.js
- Informatica
- Talend
- TIBCO Spotfire
- Looker
- Bullet Chart
- PostgreSQL
- Azure
- Crystal Reports
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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