It seems you mentioned having "same three file" which I interpret as having three similar files (e.g., three Excel or CSV files containing payment data like the one you shared). To create an effective data model in Power BI for analyzing the 24-hour payment trend across these files, you’ll need to combine and structure the data properly. Below is a step-by-step guide to perform data modeling in Power BI with multiple files.
---
### Assumptions
- You have three files (e.g., `File1.xlsx`, `File2.xlsx`, `File3.xlsx`) with similar structures, each containing columns like `T_TRANSACTION`, `T_DATE_VALUE`, `T_FILTERED`, `T_AMOUNT`, `T_ENTITY`, and `T_TYPE`.
- Each file represents a subset of your payment data (e.g., different days, batches, or entities).
- The goal is to create a unified 24-hour payment trend dashboard as discussed earlier.
---
### Step-by-Step Guide to Data Modeling in Power BI
#### 1. **Load the Three Files into Power BI**
- Open **Power BI Desktop**.
- Click **Get Data** > **Excel** (or **Folder** if the files are in the same directory).
- If using **Excel**, load each file individually:
- Select `File1.xlsx` > Click **Load** or **Transform Data**.
- Repeat for `File2.xlsx` and `File3.xlsx`.
- If using **Folder** (recommended for multiple files):
- Choose **Get Data** > **Folder**, browse to the directory containing your files, and click **Combine & Transform**.
- Power BI will detect similar tables across the files and create a single table by appending the data. Ensure the column names and data types match across all files.
- In the Power Query Editor:
- Check that `T_FILTERED` is set to **Date/Time** type.
- Remove any unnecessary columns (e.g., if some files have extra metadata).
- Rename the table (e.g., `PaymentData`) if needed.
#### 2. **Append the Data**
- If you loaded each file separately, append them into a single table:
- In the Power Query Editor, click **Home** > **Append Queries**.
- Select `File1`, `File2`, and `File3` to combine them into one table (e.g., `PaymentData`).
- Click **OK** and ensure the data aligns correctly (e.g., same column order and types).
- Click **Close & Apply** to load the combined data into the model.
#### 3. **Create a Date Table (Calendar Table)**
- To enable time intelligence and ensure all 24 hours are represented (even with no data), create a separate Date table:
- Go to **Modeling** > **New Table**.
- Use the following DAX to generate a Date table:
```
DateTable = CALENDAR(DATE(2024, 12, 1), DATE(2024, 12, 31))
```
- Adjust the date range based on your data (e.g., if it spans multiple months).
- Create additional columns for the hour:
```
HourBucket = FORMAT(TIME(HOUR([Date]), 0, 0), "h:mm AM/PM")
```
- This generates hourly buckets like "1:00 AM", "2:00 AM", etc.
- Mark this as a **Date Table**:
- Go to **Modeling** > **Mark as Date Table**, and set the `Date` column as the unique identifier.
#### 4. **Create a Relationship**
- Ensure a relationship exists between your `PaymentData` table and the `DateTable`:
- Go to the **Model** view.
- Drag the `T_DATE_VALUE` (or a derived date column from `T_FILTERED`) from `PaymentData` to the `Date` column in `DateTable`.
- Set the relationship to **1-to-many** (one date in `DateTable` can relate to many transactions in `PaymentData`).
- Ensure the `HourBucket` from `DateTable` will be used for hourly aggregation.
#### 5. **Add HourBucket to PaymentData (Optional)**
- If you want to keep the hour logic in the `PaymentData` table (instead of relying on `DateTable`):
- In Power Query Editor, add a custom column for `HourBucket` using:
```
Text.From(DateTime.Hour([T_FILTERED])) & ":00 " & if DateTime.Hour([T_FILTERED]) < 12 then "AM" else if DateTime.Hour([T_FILTERED]) = 12 then "PM" else "PM"
```
- This ensures each transaction is tagged with its hour.
#### 6. **Create a Measure for Payment Count**
- Go to **Modeling** > **New Measure**.
- Enter:
NumberOfPayments = COUNTROWS('PaymentData')
- This counts the number of transactions based on the filters applied (e.g., by date and hour).
#### 7. **Build the 24-Hour Trend Dashboard**
- **Add a Matrix Visual**:
- Drag a **Matrix** visual.
- Set:
- **Rows**: `Date` (from `DateTable`) and `HourBucket` (from `DateTable` or `PaymentData`).
- **Values**: `NumberOfPayments`.
- Enable "Show items with no data" in the visual options to display all 24 hours, even if no payments occurred.
- **Add a Line Chart**:
- Drag a **Line Chart** visual.
- Set:
- **Axis**: `HourBucket`.
- **Values**: `NumberOfPayments`.
- **Legend**: `Date` (to show trends for each day).
- Sort `HourBucket` using a custom sort order (see Step 8).
- **Add a Slicer**:
- Drag a **Slicer**, set it to `Date`, and allow users to filter by day.
#### 8. **Sort HourBucket**
- Create a sorting table to ensure hours are in the correct order (1:00 AM to 12:00 AM):
- Go to **Modeling** > **New Table**.
- Enter:
```
HourSort = DATATABLE("HourBucket", STRING, "SortOrder", INTEGER,
{
{"1:00 AM", 1}, {"2:00 AM", 2}, {"3:00 AM", 3}, {"4:00 AM", 4},
{"5:00 AM", 5}, {"6:00 AM", 6}, {"7:00 AM", 7}, {"8:00 AM", 8},
{"9:00 AM", 9}, {"10:00 AM", 10}, {"11:00 AM", 11}, {"12:00 PM", 12},
{"1:00 PM", 13}, {"2:00 PM", 14}, {"3:00 PM", 15}, {"4:00 PM", 16},
{"5:00 PM", 17}, {"6:00 PM", 18}, {"7:00 PM", 19}, {"8:00 PM", 20},
{"9:00 PM", 21}, {"10:00 PM", 22}, {"11:00 PM", 23}, {"12:00 AM", 24}
}
)
```
- Relate `HourBucket` in `PaymentData` (or `DateTable`) to `HourBucket` in `HourSort`.
- Set the sort order of `HourBucket` to use the `SortOrder` column.
#### 9. **Verify and Test**
- Check that the Matrix and Line Chart show all 24 hours for each day, with zeros where no payments occurred.
- Use the Slicer to filter by specific days and confirm the trends.
#### 10. **Publish and Share**
- Save your Power BI file and publish it to the Power BI Service if needed.
---
### Expected Output
Your Matrix visual might look like this:
| Date | HourBucket | Number of Payments |
|------------|------------|--------------------|
| 12/11/2024 | 1:00 AM | 33 |
| 12/11/2024 | 2:00 AM | 0 |
| 12/11/2024 | 3:00 AM | 0 |
| ... | ... | ... |
| 12/11/2024 | 12:00 AM | 0 |
| 12/12/2024 | 1:00 AM | 25 |
| 12/12/2024 | 2:00 AM | 10 |
| ... | ... | ... |
The Line Chart will plot the 24-hour trend for each selected day.
---
### Tips for Success
- **Consistency**: Ensure column names and data formats are identical across the three files before appending.
- **Missing Hours**: The `DateTable` with `HourBucket` ensures all 24 hours are displayed, even if your data is sparse.
- **Upload Files**: If possible, upload the three files here, and I can guide you through the exact modeling process with your data.
### Next Steps
- Upload the three files (e.g., as `.xlsx` or `.csv`) if you’d like me to assist with the specific data modeling.
- Let me know if you need help with any step (e.g., appending, creating the Date table, or sorting hours)!
Would you like to proceed with uploading the files?