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Data Observability: A Key Tool for CDOs to Gain Insights and Impact with Chief Product Officer, Ramon Chen

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Manage episode 379764254 series 3451197
Content provided by Michael Burke and Chris Detzel, Michael Burke, and Chris Detzel. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Michael Burke and Chris Detzel, Michael Burke, and Chris Detzel or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

This episode of Data Hurdles podcast features guest Ramon Chen, Chief Product Officer at Acceldata, discussing the emerging concept of data observability. Data observability involves monitoring and gaining visibility into your data supply chain to identify issues and optimize.

Key Topics Covered:

What is data observability? It means tracking data from raw sources through the supply chain to consumption, monitoring for reliability, quality, and performance issues.

How data observability integrates with MDM systems by providing useful data profiling. It gives insights into data before it reaches MDM.

The relationship between data observability and AI/ML. Good data quality is crucial for AI/ML. Data observability helps ensure quality data inputs.

Real business benefits like cost savings from optimizing cloud data systems, operational efficiency, risk reduction, and improved analytics.

Data observability gives CDOs the visibility they need to prove value and make an impact on data management. It is becoming essential.

Predictions that data observability will see major growth and adoption over the next 3-5 years as it becomes mainstream.

Key Quotes:

"Data observability involves monitoring the health and reliability of data as it flows through systems in the supply chain."

"It provides a 360 degree view of your data landscape."

"Data observability helps CDOs prove value by equating technology investments to business impact."

"It represents the biggest shift in data management that I've seen in my career."

  continue reading

44 episodes

Artwork
iconShare
 
Manage episode 379764254 series 3451197
Content provided by Michael Burke and Chris Detzel, Michael Burke, and Chris Detzel. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Michael Burke and Chris Detzel, Michael Burke, and Chris Detzel or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

This episode of Data Hurdles podcast features guest Ramon Chen, Chief Product Officer at Acceldata, discussing the emerging concept of data observability. Data observability involves monitoring and gaining visibility into your data supply chain to identify issues and optimize.

Key Topics Covered:

What is data observability? It means tracking data from raw sources through the supply chain to consumption, monitoring for reliability, quality, and performance issues.

How data observability integrates with MDM systems by providing useful data profiling. It gives insights into data before it reaches MDM.

The relationship between data observability and AI/ML. Good data quality is crucial for AI/ML. Data observability helps ensure quality data inputs.

Real business benefits like cost savings from optimizing cloud data systems, operational efficiency, risk reduction, and improved analytics.

Data observability gives CDOs the visibility they need to prove value and make an impact on data management. It is becoming essential.

Predictions that data observability will see major growth and adoption over the next 3-5 years as it becomes mainstream.

Key Quotes:

"Data observability involves monitoring the health and reliability of data as it flows through systems in the supply chain."

"It provides a 360 degree view of your data landscape."

"Data observability helps CDOs prove value by equating technology investments to business impact."

"It represents the biggest shift in data management that I've seen in my career."

  continue reading

44 episodes

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