Category: OpenAI

Introduction

Automated data storytelling is a powerful way to transform complex data visualizations into meaningful narratives. By leveraging OpenAI’s natural language generation capabilities, you can create engaging and informative stories based on your Power BI data visualizations. In this blog post, we’ll discuss the importance of automated data storytelling and guide you through the process of using OpenAI to generate narratives and summaries for your Power BI reports.

Note – Please be aware that this solution involves interacting with OpenAI’s API. I encourage users to familiarize themselves with OpenAI’s data usage policy (https://platform.openai.com/docs/data-usage-policy) and take necessary precautions to ensure the privacy and security of their data.

1. The Importance of Automated Data Storytelling

Data visualizations in Power BI enable users to analyze and gain insights from their data. However, interpreting these visualizations can be challenging, especially for users without a background in data analysis. Automated data storytelling bridges this gap by:

• Making data insights accessible: Narratives help users understand the context and significance of the data, making insights more accessible to a broader audience.
• Enhancing decision-making: Clear and concise narratives can help users grasp the implications of the data, leading to better-informed decisions.
• Saving time and resources: Generating data stories automatically reduces the time and effort required to create manual reports and analyses.

2. Prerequisites and Setup

Before we begin, you’ll need the following:

• Power BI data visualizations: Ensure that you have a Power BI report or dashboard with data visualizations that you’d like to generate narratives for.
• OpenAI API key: Sign up for an OpenAI API key if you haven’t already. You’ll use this to access OpenAI’s natural language generation capabilities. Visit https://beta.openai.com/signup/ to sign up.
• A development environment: You can use any programming language and environment that supports HTTP requests. For this tutorial, we’ll use Python and the requests library.

3. Accessing Power BI Data

In order to generate narratives based on your Power BI data visualizations, you’ll first need to extract the data from the visualizations. You can do this using the Power BI API. Follow the instructions in the “Accessing Power BI Data” section of our previous blog post on creating a Power BI chatbot to set up the necessary API access and create a Python function to query the Power BI API: https://christopherfinlan.com/?p=1921

4. Generating Narratives with OpenAI

Once you have access to your Power BI data, you can use OpenAI’s API to generate narratives based on the data. Create a Python function to send data to the OpenAI API, as demonstrated in the “Building the Chatbot with OpenAI” section of our previous blog post: https://christopherfinlan.com/?p=1921

5. Crafting Data Stories

To create data stories, you’ll need to design prompts for the OpenAI API that effectively convey the context and purpose of the data visualizations. The prompts should include relevant data points, visualization types, and any specific insights you’d like the narrative to highlight. Here’s an example of a prompt for a sales report:

openai_prompt = f"""
Create a narrative based on the following sales data visualization:

- Data: {sales_data}
- Visualization type: Bar chart
- Time period: Last 12 months
- Key insights: Top 3 products, monthly growth rate, and seasonal trends
"""

narrative = chat_with_openai(openai_prompt)

Remember to replace {sales_data} with the actual data you’ve extracted from your Power BI visualization.

6. Integrating Narratives into Power BI Reports

With the generated narratives, you can enhance your Power BI reports by embedding the narratives as text boxes or tooltips. Although Power BI doesn’t currently support direct integration with OpenAI, you can use the following workaround:

• Manually copy the generated narrative and paste it into a text box or tooltip within your Power BI report.

For a more automated approach, you can build a custom web application that combines both Power BI data visualizations and generated narratives. To achieve this, follow these steps:

1. Embed Power BI visuals using Power BI Embedded: Power BI Embedded allows you to integrate Power BI visuals into custom web applications. Follow the official documentation to learn how to embed Power BI reports and dashboards in your web application: https://docs.microsoft.com/en-us/power-bi/developer/embedded/embedding
2. Create a web application with a user interface: Design a user interface for your web application that displays Power BI visuals alongside the generated narratives. You can use HTML, CSS, and JavaScript to create the user interface.
3. Fetch narratives using JavaScript: When a user interacts with your Power BI visuals or requests a narrative, use JavaScript to send a request to your OpenAI-powered Python backend. The backend should return the generated narrative, which can then be displayed in your web application.

Here’s a simple example using JavaScript to fetch a narrative from your Python backend:

async function getNarrative() {
    const response = await fetch('http://localhost:5000/narrative', {
        method: 'POST',
        headers: {
            'Content-Type': 'application/json',
        },
        body: JSON.stringify({ data: yourData }),
    });

    const responseData = await response.json();
    document.getElementById('narrative-container').innerText = responseData.narrative;
}

Remember to replace yourData with the data you’ve extracted from your Power BI visualization.

Conclusion

Automated data storytelling enhances the value of Power BI data visualizations by providing users with engaging narratives that help them better understand their data. By leveraging OpenAI’s natural language generation capabilities, you can automatically generate insightful narratives and summaries based on your Power BI visuals. Although direct integration with Power BI is not currently available, you can still utilize OpenAI-generated narratives in your reports or create custom web applications to combine Power BI visuals with automated storytelling.

This blogpost was created with help from ChatGPT Pro.

Introduction:

In our previous blog post, we walked you through the process of integrating Power BI with OpenAI’s GPT-4 to generate textual insights. This time, we will dive deeper into leveraging the OpenAI API to analyze data in Power BI and enhance your data-driven decision-making process. By incorporating OpenAI’s natural language processing capabilities, you can extract valuable information from textual data and make informed decisions based on your Power BI analysis.

Prerequisites:

1. Familiarity with the previous blog post on integrating Power BI with OpenAI
2. A Power BI Pro or Premium account
3. OpenAI API Key
4. Python and required libraries installed (openai, pandas, powerbiclient)

Note – Please be aware that this solution involves interacting with OpenAI’s API. I encourage users to familiarize themselves with OpenAI’s data usage policy (https://platform.openai.com/docs/data-usage-policy) and take necessary precautions to ensure the privacy and security of their data.

Step 1: Fetch and Process Textual Data

Before analyzing textual data with the OpenAI API, you need to fetch and preprocess it. In this example, we will work with customer reviews data. You can import this data into Power BI from various sources like a CSV file, Excel file, or a database.

1. Import customer reviews data into Power BI and create a dataset.
2. Clean and preprocess the data as necessary (e.g., removing duplicates, handling missing values, etc.).

Step 2: Update the Python Script to Analyze Textual Data with OpenAI

1. Open the Python script you created in the previous blog post (e.g., ‘openai_powerbi_integration.py’) and locate the fetch_openai_data function.
2. Modify the function to accept the text to be analyzed as an input parameter:

def fetch_openai_data(text):

3. Update the OpenAI API call within the function to perform the desired analysis task. For example, you can modify the prompt to perform sentiment analysis:

def fetch_openai_data(text):
    prompt = f"Analyze the sentiment of the following customer review: '{text}'. Is it positive, negative, or neutral?"
    
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=100,
        n=1,
        stop=None,
        temperature=0.7,
    )
    generated_text = response.choices[0].text.strip()
    return generated_text

Step 3: Analyze Textual Data in Power BI Using the Updated Python Script

1. In Power BI, create a new column in the customer reviews dataset to store the sentiment analysis results.
2. Iterate through the customer reviews and call the fetch_openai_data function for each review. Store the sentiment analysis result in the new column:

for index, row in customer_reviews.iterrows():
    text = row["Review_Text"]
    sentiment = fetch_openai_data(text)
    customer_reviews.loc[index, "Sentiment"] = sentiment

Step 4: Visualize the Analyzed Data in Power BI

1. In Power BI, create a new report using the updated customer reviews dataset.
2. Design visualizations to display the sentiment analysis results, such as pie charts, bar charts, or word clouds. You can also create filters to allow users to interact with the data and explore specific segments, such as reviews with positive or negative sentiment.
3. Save and publish the report to share the AI-enhanced insights with your team.

Conclusion:

In this follow-up blog post, we demonstrated how to use the OpenAI API to analyze textual data in Power BI, enhancing your data analysis capabilities. By incorporating the power of OpenAI’s natural language

processing into your Power BI dashboard, you can gain deeper insights and make more informed decisions based on your data.

Remember that this is just one example of how you can integrate OpenAI with Power BI for data analysis. You can customize the Python script and the OpenAI prompt to perform other analysis tasks such as topic extraction, keyword identification, or summarization. The possibilities are vast, and with a little creativity, you can unlock the full potential of combining AI and business intelligence tools to drive your organization’s success.

As you continue exploring the integration of OpenAI and Power BI, always keep in mind the ethical considerations and usage guidelines of AI-generated content. Ensure that the generated insights align with your organization’s values and goals while adhering to responsible AI practices.

Learn More

If you’re interested in learning more, here are three example follow-up questions you could ask ChatGPT about the blog post:

1. In the blog post about using the OpenAI API to analyze data in Power BI, how can I optimize the Python script to handle a large volume of textual data for analysis without exceeding API rate limits or incurring excessive costs?
2. What are some methods to ensure data privacy and security when analyzing sensitive textual data, such as customer reviews or internal communications, using the OpenAI API and Power BI integration?
3. Are there any limitations or potential biases in the AI-generated analysis that users should be aware of when interpreting the results and making data-driven decisions based on the Power BI visualizations?

This blogpost was created with help from ChatGPT Pro.

Introduction:

As the need for data-driven decision-making grows, integrating artificial intelligence (AI) with business intelligence (BI) tools has become an invaluable asset for businesses. Two popular tools in these fields are Power BI by Microsoft and OpenAI’s GPT-4. In this blog post, we’ll walk you through a detailed process to integrate Power BI with OpenAI, unlocking powerful analytics and AI capabilities for your organization.

Power BI is a suite of business analytics tools that helps you visualize and share insights from your data. OpenAI is a cutting-edge AI research lab that has developed the GPT-4, a large language model capable of understanding and generating human-like text. By integrating Power BI with OpenAI, you can enhance your data analysis and generate insights using AI techniques.

Note – Please be aware that this solution involves interacting with OpenAI’s API. I encourage users to familiarize themselves with OpenAI’s data usage policy (https://platform.openai.com/docs/data-usage-policy) and take necessary precautions to ensure the privacy and security of their data.

Prerequisites:

1. A Power BI Pro or Premium account
2. OpenAI API Key (Sign up for OpenAI’s API service at https://beta.openai.com/signup/)

Step 1: Install Python and Required Libraries

To install Python on your computer, follow these steps:

1. Visit the official Python website: https://www.python.org/downloads/
2. You will see the download buttons for the latest Python version available for your operating system (Windows, macOS, or Linux). Click on the appropriate button to download the installer. If you need a different version or want to explore other installation options, click on the “View the full list of downloads” link below the buttons.
3. Once the installer is downloaded, locate the file in your downloads folder or wherever your browser saves downloaded files.
4. Run the installer by double-clicking on the file.

For Windows:

• In the installer, check the box that says “Add Python to PATH” at the bottom. This will allow you to run Python from the command prompt easily.
• Select the “Customize installation” option if you want to change the default installation settings, or just click on “Install Now” to proceed with the default settings.
• The installer will install Python and set up the necessary file associations.

For macOS:

• Follow the installation prompts in the installer.
• Depending on your macOS version, Python may already be pre-installed. However, it’s usually an older version, so it’s still recommended to install the latest version from the official website.

For Linux:

• Most Linux distributions come with Python pre-installed. You can check the installed version by opening a terminal and typing python --version or python3 --version. If you need to install or update Python, use your distribution’s package manager (such as apt, yum, or pacman) to install the latest version.

After installation, open a command prompt or terminal and type python --version or python3 --version to ensure that Python has been installed correctly. You should see the version number of the installed Python interpreter.

Now you have Python installed on your computer and are ready to proceed with installing the required libraries and running Python scripts.

2. Install the following Python libraries using pip:

pip install openai pandas powerbiclient
pip install msal

Step 2: Create a Power BI Dataset and Table

1. Sign in to Power BI service at https://app.powerbi.com/
2. Navigate to your workspace and click on ‘Datasets + dataflows’ in the left pane.
3. Click on the ‘+ Create’ button and select ‘Streaming Dataset.’
4. Choose ‘API’ as the connection type and click ‘Next.’
5. Provide a name for your dataset, such as ‘OpenAI_Insights,’ and click ‘Create.’
6. You will receive an API URL and an authentication token. Save these for later use.

Step 3: Create a Python Script to Fetch Data and Push to Power BI

1. Create a new Python script (e.g., ‘openai_powerbi_integration.py’) and import the required libraries:

import openai
import pandas as pd
from powerbiclient import Report, models
import requests
from msal import PublicClientApplication

2. Set up OpenAI API and Power BI authentication:

# Set up OpenAI API
openai.api_key = "your_openai_api_key"

# Set up Power BI authentication
POWER_BI_CLIENT_ID = "your_power_bi_client_id"
AUTHORITY = "https://login.microsoftonline.com/common"
SCOPE = ["https://analysis.windows.net/powerbi/api/.default"]
app = PublicClientApplication(POWER_BI_CLIENT_ID, authority=AUTHORITY)

result = None
accounts = app.get_accounts()
if accounts:
    result = app.acquire_token_silent(SCOPE, account=accounts[0])

if not result:
    flow = app.initiate_device_flow(scopes=SCOPE)
    print(flow["message"])
    result = app.acquire_token_by_device_flow(flow)

powerbi_auth_token = result["access_token"]

Replace your_openai_api_key with your OpenAI API key and your_power_bi_client_id with your Power BI client ID. The script will prompt you to authenticate with Power BI by providing a URL and a device code.

To obtain your Power BI client ID, you need to register an application in the Azure Active Directory (Azure AD) associated with your Power BI account. Here’s a step-by-step guide to help you get your Power BI client ID:

1. Sign in to the Azure portal: Go to https://portal.azure.com/ and sign in with the account you use for Power BI.
2. Navigate to Azure Active Directory: Once you’re logged in, click on “Azure Active Directory” from the left-hand menu or find it using the search bar at the top.
3. Register a new application: In the Azure Active Directory overview page, click on “App registrations” in the left-hand menu, and then click on the “+ New registration” button at the top.
4. Configure your application:
   • Provide a name for your application (e.g., “PowerBI_OpenAI_Integration”).
   • Choose the supported account types (e.g., “Accounts in this organizational directory only” if you want to restrict access to your organization).
   • In the “Redirect URI” section, choose “Web” and provide a URL (e.g., “https://localhost“). This is just a placeholder and won’t be used for our Python script.
5. Click on the “Register” button at the bottom to create the application.
6. Obtain your client ID: After registering your application, you’ll be redirected to the application’s overview page. Here, you’ll find the “Application (client) ID.” This is the Power BI client ID you need for your Python script. Make sure to copy and save it securely.
7. Grant API permissions:
   • In the application’s main menu, click on “API permissions.”
   • Click on the “+ Add a permission” button and select “Power BI Service.”
   • Choose “Delegated permissions” and check the “Dataset.ReadWrite.All” permission.
   • Click on the “Add permissions” button to save your changes.
8. Don’t forget to update the Power BI client ID in your Python script!

3. Create a function to fetch data from OpenAI and process it:

def fetch_openai_data(prompt):
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=100,
        n=1,
        stop=None,
        temperature=0.7,
    )
    generated_text = response.choices[0].text.strip()
    return generated_text

4. Create a function to push data to Power BI:

def push_data_to_powerbi(api_url, auth_token, dataframe):
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {auth_token}",
    }
    data_json = dataframe.to_json(orient="records")
    response = requests.post(api_url, headers=headers, data=data_json)
    return response.status_code

5. Use the functions to fetch data from OpenAI and push it to Power BI:

# Define your OpenAI prompt
prompt = "Summarize the key factors affecting the global economy in 2023."

# Fetch data from OpenAI
openai_data = fetch_openai_data(prompt)

# Create a DataFrame with the data
data = {"OpenAI_Insight": [openai_data]}
dataframe = pd.DataFrame(data)

# Push data to Power BI
api_url = "your_power_bi_api_url"
auth_token = powerbi_auth_token
status_code = push_data_to_powerbi(api_url, auth_token, dataframe)

# Print status code for confirmation
print(f"Data push status code: {status_code}")

6. Save and run the Python script:

python openai_powerbi_integration.py

If successful, you should see the status code ‘200’ printed, indicating that the data push was successful.

Step 4: Create a Power BI Report and Visualize Data

1. Go back to Power BI service and navigate to the ‘OpenAI_Insights’ dataset.
2. Click on the dataset to create a new report.
3. In the report editor, create a table or any other visualization type to display the ‘OpenAI_Insight’ data.
4. Save and publish the report to share insights with your team.

Conclusion:

In this blog post, we walked you through the process of integrating Power BI with OpenAI. By following the steps, you can use Power BI to visualize and share insights generated by OpenAI’s GPT-4, enhancing your data analysis capabilities. This integration opens up new possibilities for advanced data-driven decision-making, enabling your organization to stay ahead of the competition.

Remember that this is just a starting point. You can further customize the Python script to fetch and process more complex data from OpenAI, and even create dynamic, real-time dashboards in Power BI to keep your team updated with the latest AI-generated insights.

Learn More

If you’re interested in learning more, here are three example follow-up questions you could ask ChatGPT about the blog post:

1. How can I customize the OpenAI prompt to generate more specific insights or analyses for my Power BI dashboard?
2. What are some best practices for visualizing the AI-generated insights in Power BI to create effective and easy-to-understand reports?
3. Can you provide examples of other use cases where the integration of Power BI and OpenAI can be beneficial for businesses or organizations?

Make sure you provide the context of the blogpost when asking your follow-up questions. Here is an example of how you could ask it:

In the blog post about integrating Power BI with OpenAI, you mentioned creating a Python script to fetch data and push it to Power BI. How can I customize the OpenAI prompt within the script to generate more specific insights or analyses for my Power BI dashboard?

Thanks for reading!

This blogpost was created with help from ChatGPT Pro.