Row 89394

Row ID: 89394 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 89394 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Experts know what types of data issues are linked to a type of analysis or visualization that proves the root cause of the issue. Issue —> verification —> recommendation for solving the issue. But teaching those intricacies is difficult. Why not take a company’s history of data QA work (scraped from emails, Azure DevOps, Teams meeting transcripts) and use the natural language associated with past manual data QA work to inform a QA agent that spots issues, runs a series of queries to insulate and verify the root cause, and then notifies the people who can fix the issue, with a detailed recommendation message.

FieldValue
text Experts know what types of data issues are linked to a type of analysis or visualization that proves the root cause of the issue. Issue —> verification —> recommendation for solving the issue. But teaching those intricacies is difficult. Why not take a company’s history of data QA work (scraped from emails, Azure DevOps, Teams meeting transcripts) and use the natural language associated with past manual data QA work to inform a QA agent that spots issues, runs a series of queries to insulate a…
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-25
username_encoded Z0FBQUFBQm5Lak1yQktoYmEyWXBBZ1dPSTdYS3MwT01mM28zYmV4cjNWcmwxbGU5ZDFqRnRYVWVHS2JNbUtLU0hyQmVqbWtHTHc3aTVCWWNPV1lCRzJHZG1mMklLT1l4SVE9PQ==
url_encoded Z0FBQUFBQm5Lak84VWZtWTVYMFN3Skh4cG5NQkF6c096WExOMzNUc3kyZGY5Um5xUGNrZHF1bmJxVk1YRGdPVll5WXEzTmctRGZ2Z2dwNDJ3SC1kektVLXpiSmo3c3FiczlERWtWZXZIaXJXNThhTElIU0dJeHdsNU51aU1KUUJGTUhtZHJOZ2lqN18wN3QtaC04Q0dnczQxU0Q2alN3ZEh5WEgybXFGNC1xaGotVHVSVGQ5QUhrUDRXLU90YXQtOU9TemRxcUhMOTAwT0N1UGhIWU1YYmZSNkJhQk1ncDA5Zz09

Raw Record

{
  "text": "Experts know what types of data issues are linked to a type of analysis or visualization that proves the root cause of the issue.  Issue —> verification —> recommendation for solving the issue. But teaching those intricacies is difficult.  Why not take a company’s history of data QA work (scraped from emails, Azure DevOps, Teams meeting transcripts) and use the natural language associated with past manual data QA work to inform a QA agent that spots issues, runs a series of queries to insulate and verify the root cause, and then notifies the people who can fix the issue, with a detailed recommendation message.",
  "label": "r/datascience",
  "dataType": "comment",
  "communityName": "r/datascience",
  "datetime": "2024-05-25",
  "username_encoded": "Z0FBQUFBQm5Lak1yQktoYmEyWXBBZ1dPSTdYS3MwT01mM28zYmV4cjNWcmwxbGU5ZDFqRnRYVWVHS2JNbUtLU0hyQmVqbWtHTHc3aTVCWWNPV1lCRzJHZG1mMklLT1l4SVE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak84VWZtWTVYMFN3Skh4cG5NQkF6c096WExOMzNUc3kyZGY5Um5xUGNrZHF1bmJxVk1YRGdPVll5WXEzTmctRGZ2Z2dwNDJ3SC1kektVLXpiSmo3c3FiczlERWtWZXZIaXJXNThhTElIU0dJeHdsNU51aU1KUUJGTUhtZHJOZ2lqN18wN3QtaC04Q0dnczQxU0Q2alN3ZEh5WEgybXFGNC1xaGotVHVSVGQ5QUhrUDRXLU90YXQtOU9TemRxcUhMOTAwT0N1UGhIWU1YYmZSNkJhQk1ncDA5Zz09"
}
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