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    • Data compliance for Data Scientists
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  • Contents
  • Data Compliance for Data Scientists
  • Deprecation for Data Engineer \ Analyst
  • Data quality and visibility for Quality Engineer
  • Data preparation for Viz Engineer
  • Service Provider and Pre-Sales
  • Newcomer onboarding
  • Customer churn analysis for Data Scientist
  • Recommendation engine with observable and manageable pipelines
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Use cases

PreviousFeaturesNextData compliance for Data Scientists

Last updated 2 years ago

Contents

Data Compliance for Data Scientists

Develop ML-models meeting compliance standards and make sure that you manage Personally Identifiable Information (PII) properly.

Deprecation for Data Engineer \ Analyst

Be in control of making dramatic changes to your data. Provide transparent deprecation process so as to inform all stakeholders in proper time and manage risks of downstream failure.

Data quality and visibility for Quality Engineer

Import test suite results from pre-defined libraries or custom frameworks directly to the ODD Platform and share them with your team and other stakeholders to build trustworthy and transparent communication about data health of your product.

Data preparation for Viz Engineer

There are numerous BI tools and their data preparation capabilities vary. Examine your data sources, metadata and tags using the ODD Platform to predict BI tool performance, set dashboard security levels and prepare the data.

Service Provider and Pre-Sales

Manage customer expectations successfully by examining an existing architectural landscape and gathering info about a toolset for better project scope planning.

Newcomer onboarding

Gather information about your data sources and owners, metadata description and lineage diagram in one place, so that a newcomer gets down to real tasks faster.

Customer churn analysis for Data Scientist

An imperfect notification system in your streaming pipeline may cause a problem when a training model receives irrelevant data. This use case shows how to launch enriched notifications provided by ODD & Open Telemetry integration.

Recommendation engine with observable and manageable pipelines

Build training models for your recommendation system without failures and obsolete data. This use case is about how ODD's alerts and end-to-end lineage helps avoid asynchronous run schedules of a model and an Airflow pipeline.

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