Can you draw a map of all the paths data takes from source systems to production insight delivery?  How many tools, technologies, configurations, and paths do your data take during its production process?   What is the ‘run-time lineage’ of data in your organization?  

Can you create a map of those data journeys?  And if something goes wrong, do you know where to find the problem or judge its impact?  Can you answer simple questions from the business user?  When will my dashboard be ready?  Why should I trust this data?

DataKitchen CEO Christopher Bergh will break down a ‘Data Journey’ and show mapping the run-time lineage of your data-to-insight production process is the essential missing piece of your analytics architecture.    And once you have that mapping, how to actively monitor, track and test a data journey to ensure rapid delivery of insight, low production data errors, and high rate change to production systems with few regression errors.

Specifically, Chris will cover:

  • What is a Data Journey, and why is run-time lineage essential
  • How you can map your Data Journey
  • Active monitoring, testing, and checking your data journey reduces hassles and increases customer trust in your data and insights.
  • How you can start this process today with DataOps Observability.


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Monitor every Data Journey in an enterprise, from source to customer value, in development and production.

Simple, Fast Data Quality Test Generation and Execution. Your Data Journey starts with verifying that you can trust your data.

Orchestrate and automate your data toolchain to deliver insight with few errors and a high rate of change.

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