Review table data model and metrics

Summary

The video script discusses the Review BQ Schema, explaining how to view data in the BigQuery console after an export job. It covers various metrics captured at conversation, sentence, and word levels, such as topic names, sentiment scores, and language codes, with a full list available in resource 27.

Takeaways

• 📊 The Review BQ Schema is discussed in this section, highlighting its importance in data analysis.
• 🔍 Once an Export job is completed, the resulting table is accessible in the BQ console for further examination.
• 👀 Users can preview data directly from the BQ console or utilize the SQL query editor for a more in-depth look.
• 📝 SQL queries can be written within the SQL workspace to view data, offering flexibility in data retrieval.
• 🗣️ Conversation-level metrics are captured, providing insights into various aspects of interactions.
• 🔍 Metrics like topic name, silence percentage, and entities identified are part of the conversation-level data.
• 📚 Sentence-level metrics are also captured, including sentiment scores, intent, and highlights, offering detailed sentiment analysis.
• 📈 A positive sentiment score indicates a more favorable sentiment towards the conversation.
• 🗣️ Word-level metrics such as speaker tag and language code are exported, adding granularity to the data.
• 📚 For a comprehensive list of all exported metrics, reference resource 27 mentioned in the course.

Q & A

What is the purpose of the Review BQ Schema?

The Review BQ Schema is used to organize and manage data exports in BigQuery, making it easier to analyze and review various conversation metrics.

How can you access the exported table in BigQuery?

Once the export job is completed, the table becomes visible in the BigQuery console, accessible for further analysis.

What is the function of the 'Preview' button in BigQuery?

The 'Preview' button allows users to view a sample of the data directly in the BigQuery console without needing to run a full query.

How can users interact with the data in BigQuery?

Users can interact with the data using the SQL query editor or by writing SQL queries directly in the SQL workspace.

What types of metrics are captured at the conversation level?

Metrics captured at the conversation level include topic name, silence percentage of user or agents, and entities identified from the conversation.

What does the silence percentage metric represent?

The silence percentage metric indicates the proportion of time during a conversation where there is no speech, either by the user or the agent.

What metrics are captured at the sentence level in the Review BQ Schema?

At the sentence level, metrics such as sentiment score, intent, and highlights are captured.

What does a higher sentiment score indicate?

A higher sentiment score indicates a more positive sentiment in the analyzed sentence.

What word-level metrics are exported in the Review BQ Schema?

Word-level metrics like speaker tag and language code are exported.

Where can one find a full list of exported metrics?

A full list of exported metrics can be found in resource 27 referenced at the end of the course.

How can SQL queries be used to analyze the data in BigQuery?

SQL queries can be used to filter, aggregate, and analyze the data in BigQuery, providing insights into various aspects of the conversations.