Summary

The global climate impacts everyone, and the rate of change introduces many questions that businesses need to consider. Getting answers to those questions is challenging, because the climate is a multidimensional and constantly evolving system. Sust Global was created to provide curated data sets for organizations to be able to analyze climate information in the context of their business needs. In this episode Gopal Erinjippurath discusses the data engineering challenges of building and serving those data sets, and how they are distilling complex climate information into consumable facts so you don’t have to be an expert to understand it.

Announcements

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Your host is Tobias Macey and today I’m interviewing Gopal Erinjippurath about his work at Sust Global building data sets from geospatial and satellite information to power climate analytics

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what Sust Global is and the story behind it?
- What audience(s) are you focused on?
Climate change is obviously a huge topic in the zeitgeist and has been growing in importance. What are the data sources that you are working with to derive climate information?
- What role do you view Sust Global having in addressing climage change?
How are organizations using your climate information assets to inform their analytics and business operations?
- What are the types of questions that they are asking about the role of climate (present and future) for their business activities?
- How can they use the climate information that you provide to understand their impact on the planet?
What are some of the educational efforts that you need to undertake to ensure that your end-users understand the context and appropriate semantics of the data that you are providing? (e.g. concepts around climate science, statistically meaningful interpretations of aggregations, etc.)
Can you describe how you have architected the Sust Global platform?
- What are some examples of the types of data workflows and transformations that are necessary to maintain your customer-facing services?
How have you approached the question of modeling for the data that you provide to end-users to make it straightforward to integrate and analyze the information?
- What is your process for determining relevant granularities of data and normalizing scales? (e.g. time and distance)
What is involved in integrating with the Sust Global platform and how does it fit into the workflow of data engineers/analysts/data scientists at your customer organizations?
Any analytical task is an exercise in story-telling. What are some of the techniques that you and your customers have found useful to make climate data relatable and understandable?
- What are some of the challenges involved in mapping between micro and macro level insights and translating them effectively for the consumer?
How does the increasing sensor capabilities and scale of coverage manifest in your data?
- How do you account for increasing coverage when analyzing across longer historical time scales?
How do you balance the need to build a sustainable business with the importance of access to the information that you are working with?
What are the most interesting, innovative, or unexpected ways that you have seen Sust Global used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Sust Global?
When is Sust the wrong choice?
What do you have planned for the future of Sust Global?

Contact Info

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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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