Enhance Your Power BI Dashboards with ChatGPT

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Table of Contents

Enhance Your Power BI Dashboards with ChatGPT

Table of Contents

  1. Introduction
  2. Passing Data from PowerBI to Open AI
  3. Setting Up the Prompt
  4. Creating the Flow in Power Automate
  5. Storing the Response in SharePoint
  6. Safeguards and Measures
  7. Analyzing Metrics and Ratings
  8. Conclusion

Passing Data from PowerBI to Open AI

In this section, we will explore how to pass data from PowerBI to an open AI model, and process the data to obtain a response. We will also learn how to display the response in our PowerBI report.

Setting Up the Prompt

To effectively analyze the quality of the data entered into work orders, we will set up a prompt for the large language model to analyze the defect report. We will compare the defect report against best practices and receive feedback. This feedback will include a general assessment, feedback on the data, coaching tips, and an overall rating.

Creating the Flow in Power Automate

In this section, we will Create a flow that triggers a Power Automate flow when data is selected. We will pass the data to the Open AI model and retrieve the response. This response will be stored in a SharePoint list.

Storing the Response in SharePoint

To store the response from the large language model, we will create a SharePoint list. This list will serve as a repository for the responses generated by the model. We will set up the necessary fields and establish a connection between the work order data and the response data.

Safeguards and Measures

To avoid duplicates and unnecessary processing, we will implement safeguards and measures in our flow. We will check for existing entries in the SharePoint list to prevent duplicate submissions and optimize the processing of the data.

Analyzing Metrics and Ratings

In this section, we will explore how to analyze the metrics and ratings generated from the large language model. We will create measures to track the number of work orders and categorize them Based on their ratings. This will allow us to gain insights into the quality of the data and identify areas for improvement.

Conclusion

In conclusion, this tutorial has demonstrated how to pass data from PowerBI to an open AI model, process the data, and display the response in a PowerBI report. By leveraging the capabilities of the large language model, we can analyze the quality of work order data and provide valuable feedback. This workflow can save time, improve data accuracy, and enable targeted coaching for data entry personnel.

Article

In today's tutorial, we will explore the process of passing data from PowerBI to an open AI model, processing the data, and displaying the response in a PowerBI report. This workflow can greatly enhance the analysis of work order data and provide valuable insights for maintenance and reliability professionals.

Introduction

As maintenance and reliability professionals, it is crucial to analyze the quality of data entered into work orders. This analysis allows us to assess the accuracy and completeness of the data and make informed decisions. In this tutorial, we will learn how to leverage the capabilities of the large language model in Open AI to automate the analysis process.

Passing Data from PowerBI to Open AI

The first step in our workflow is to pass the data from PowerBI to an open AI model. By selecting the Relevant work order data, we can send it to the open AI model using a flow in Power Automate. This flow triggers the API call to the open AI model and retrieves the response.

Setting Up the Prompt

To ensure accurate analysis, we need to set up a prompt for the large language model in Open AI. This prompt instructs the model to review the defect report and analyze it against best practices. We can customize the prompt to provide specific instructions and expectations for the model.

Creating the Flow in Power Automate

In Power Automate, we can create a flow that triggers when data is selected in PowerBI. This flow passes the selected data to the open AI model and retrieves the response. The response is then stored in a SharePoint list for further analysis and integration into the PowerBI report.

Storing the Response in SharePoint

To store the responses from the large language model, we need to create a SharePoint list. This list serves as a repository for the responses generated by the model. We can set up the necessary fields, such as the work order number and the defect report assessment, to store the response data.

Safeguards and Measures

To ensure the efficiency and accuracy of our workflow, we need to implement safeguards and measures. These safeguards prevent duplicate submissions and unnecessary processing of data. By checking for existing entries in the SharePoint list before submitting a new response, we can avoid duplications and unnecessary API calls.

Analyzing Metrics and Ratings

Once we have collected the responses from the large language model, we can analyze the metrics and ratings to gain insights into the quality of the work order data. By creating measures and categorizing the data based on ratings, we can track the number of work orders and identify areas for improvement.

Conclusion

In conclusion, leveraging the capabilities of the large language model in Open AI can greatly enhance the analysis of work order data in PowerBI. By automating the process of passing data to the model, retrieving responses, and integrating them into the PowerBI report, we can improve data accuracy, save time, and provide targeted coaching for data entry personnel.

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