Completing The Feedback Loop Of Data Through Operational Analytics With Census
Data Engineering Podcast
00:00
Creating a Workflow in a Data Warehouse
Sensus creates a feedback loop from the action to the source and back. The biggest problem that i've come across is that most data teams report to a c f o. Data teams should be modelled after growth teams, he says. And they should be bigger than them and more powerful than them.
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Summary
The focus of the past few years has been to consolidate all of the organization’s data into a cloud data warehouse. As a result there have been a number of trends in data that take advantage of the warehouse as a single focal point. Among those trends is the advent of operational analytics, which completes the cycle of data from collection, through analysis, to driving further action. In this episode Boris Jabes, CEO of Census, explains how the work of synchronizing cleaned and consolidated data about your customers back into the systems that you use to interact with those customers allows for a powerful feedback loop that has been missing in data systems until now. He also discusses how Census makes that synchronization easy to manage, how it fits with the growth of data quality tooling, and how you can start using it today.
Announcements
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- Your host is Tobias Macey and today I’m interviewing Boris Jabes about Census and the growing category of operational analytics
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Census is and the story behind it?
- The terms "reverse ETL" and "operational analytics" have started being used for similar, and often interchangeable, purposes. What are your thoughts on the semantic and concrete differences between these phrases?
- What are the motivating factors for adding operational analytics or "data activation" to an organization’s data platform?
- This is a nascent but quickly growing market with a number of products and projects operating in the space. How would you characterize the current state of the segment and Census’ position in it?
- Can you describe how the Census platform is implemented?
- What are some of the early design choices that have had to be refactored or augmented as you have evolved the product and worked with customers?
- What are some of the assumptions that you had about the needs and uses for the platform which have been challenged or changed as you dug deeper into the problem?
- Can you describe the workflow for a customer adopting Census?
- What are some of the data modeling practices that make it easier to "activate" the organization’s data?
- Another recent trend in the data industry is the growth of data quality and data lineage tools. What is involved in using the measured quality or lineage information as a signal in the operational systems, or to prevent a synchronization?
- How can users test and validate their workflows in Census?
- What are the options for propagating Census’ runtime information back into lineage and data quality tracking?
- Census supports incremental syncs from the warehouse. What are the opportunities for bringing streaming architectures to the space of operational analytics?
- What are the challenges/complexities in the current set of technologies that act as a barrier?
- What are the most interesting, innovative, or unexpected ways that you have seen Census used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Census?
- When is Census the wrong choice?
- What do you have planned for the future of Census?
Contact Info
- Website
- @borisjabes on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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