Analysis

 To help you analyze and visualize the server provisioning data in Power BI and build machine learning models with Python, we can break the task down into two key parts:

1. Power BI Insights and Visualizations:

• Data Preparation:

  • REQ/RITM/RTSK Analysis: Group the data by different levels like REQ, RITM, and RTSK to summarize how many requests were opened/closed, task durations, etc.
  • Date and Duration Calculations: Calculate the time taken for each request, task, and item to be completed using the fields like REQ Opened At, REQ Closed At, RTSK Opened At, and RTSK Closed At. Visualize these to show trends in time taken.
  • Task Status Breakdown: Create visualizations showing how many tasks are still active, completed, and in progress (REQ State, RITM State, RTSK State).
  • Categorical Analysis: Create charts based on fields like Server Type, Product, Product Type, and Automation Status to understand distribution and categories.
  • Assignee/Business Name Analysis: Look at the workload by RITM Closed By Business Name, RTSK Assignee Business Name to show which teams or individuals are handling the most tasks.
  • Geographical Insights: Using Requested For Region Curr, create a map or region-based charts for where most of the provisioning tasks are happening.

• Potential Visualizations:

  • Bar Charts: Task counts by states (open, closed, active).
  • Line/Area Charts: Trend of server provisioning requests over time (by REQ Opened At).
  • Heatmaps: Task duration analysis using RTSK Calendar Duration or Task Effort (Hour) to see bottlenecks.
  • Pie Charts: Distribution of tasks by Server Type, Product Type, or Automation Status.
  • Geographical Maps: Showing provisioning across regions using Requested For Region Curr.
  • Task Duration KPI: A card visual for average/median task completion time and overall efficiency.

2. Machine Learning Prediction in Python:

For machine learning, we can predict time-to-completion (task duration) or whether a request will be completed on time. Here’s how you can approach it:

• Feature Engineering:

  • Target Variable: You can use RTSK Closed At minus RTSK Opened At or REQ Due Date to predict how long a task takes to close or whether it will meet its deadline.
  • Time-Related Features: Create new features like the day of the week, month, or time gaps (differences between REQ Opened At and REQ Closed At) to capture time trends.
  • Categorical Features: Convert categorical fields like RTSK Assigned Group, Server Type, Product, Automation Status into numerical formats for ML (using one-hot encoding or label encoding).
  • Historical Data: Use past requests and tasks (like the total number of closed requests by RTSK Assignee Business Name) as features.

• Modeling:

  • Regression Models: Predict the time to complete a task using regression algorithms such as Linear Regression, Random Forest, or Gradient Boosting. The target variable could be RTSK Business Duration.
  • Classification Models: Predict whether a task will be closed on time (before RTSK Due Date) using models like Logistic Regression, Decision Trees, or XGBoost.
  • Model Evaluation: Use metrics like R-squared, MAE (Mean Absolute Error) for regression, or Accuracy, Precision/Recall for classification.

• Steps in Python:

  1. Load and Preprocess Data: Use pandas for cleaning and preprocessing.
  2. Feature Engineering: Create meaningful features (time-based, categorical, etc.).
  3. Train-Test Split: Split the data into training and testing datasets.
  4. Model Training: Train a regression or classification model based on the target.
  5. Model Evaluation: Evaluate the performance on the test set.

Would you like me to help you further with setting up specific visualizations in Power BI or developing Python scripts for machine learning? Let me know where you'd like to dive in first!