Use Your Data Warehouse To Power Your Product Analytics With NetSpring

Episode • Mar 10, 2023 • 49m

Summary

With the rise of the web and digital business came the need to understand how customers are interacting with the products and services that are being sold. Product analytics has grown into its own category and brought with it several services with generational differences in how they approach the problem. NetSpring is a warehouse-native product analytics service that allows you to gain powerful insights into your customers and their needs by combining your event streams with the rest of your business data. In this episode Priyendra Deshwal explains how NetSpring is designed to empower your product and data teams to build and explore insights around your products in a streamlined and maintainable workflow.

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 Priyendra Deshwal about how NetSpring is using the data warehouse to deliver a more flexible and detailed view of your product analytics

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what NetSpring is and the story behind it?
- What are the activities that constitute "product analytics" and what are the roles/teams involved in those activities?
When teams first come to you, what are the common challenges that they are facing and what are the solutions that they have attempted to employ?
Can you describe some of the challenges involved in bringing product analytics into enterprise or highly regulated environments/industries?
- How does a warehouse-native approach simplify that effort?
There are many different players (both commercial and open source) in the product analytics space. Can you share your view on the role that NetSpring plays in that ecosystem?
How is the NetSpring platform implemented to be able to best take advantage of modern warehouse technologies and the associated data stacks?
- What are the pre-requisites for an organization's infrastructure/data maturity for being able to benefit from NetSpring?
- How have the goals and implementation of the NetSpring platform evolved from when you first started working on it?
Can you describe the steps involved in integrating NetSpring with an organization's existing warehouse?
- What are the signals that NetSpring uses to understand the customer journeys of different organizations?
- How do you manage the variance of the data models in the warehouse while providing a consistent experience for your users?
Given that you are a product organization, how are you using NetSpring to power NetSpring?
What are the most interesting, innovative, or unexpected ways that you have seen NetSpring used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on NetSpring?
When is NetSpring the wrong choice?
What do you have planned for the future of NetSpring?

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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