As part of the BI initiative, we have created datasets that compile logic across our various datasets.
The dataset mentioned below is the transaction-level dataset that would have service mode details for each transaction.
The goal is to map out any transaction-level metric as a column in this dataset.
\uD83D\uDCD8 Data Dictionary
Here are the columns present in the data.
Databricks Table name :
loyalty.temp.weekly_execution_trans
Column Name | Datatype | Description | Source | Notes: |
TH_FISCAL_YEAR | string | The fiscal year of the transaction date | STG.DERIVED_MASTER_TABLE_NEW | |
TH_CALENDAR_MONTH | int | The calendar month of the transaction date | STG.DERIVED_MASTER_TABLE_NEW | |
TH_FISCAL_WEEK | string | The fiscal week of the transaction date | STG.DERIVED_MASTER_TABLE_NEW | |
PERIOD_DT | date | Date of the transaction | STG.DERIVED_MASTER_TABLE_NEW | |
DAY_PART_CD | string | Daypart of transaction | STG.DERIVED_MASTER_TABLE_NEW | |
TICKET_ID | bigint | Ticket_id, unique for each transaction | STG.DERIVED_MASTER_TABLE_NEW | |
OPERATOR | string | GM of the restaurant | TLOG_DIM_RESTAURANT_CURRENT | |
AFL | string | Director of the restaurant | TLOG_DIM_RESTAURANT_CURRENT | |
MFP | string | MFP of the restaurant | TLOG_DIM_RESTAURANT_CURRENT | |
LOYALTY_CUSTOMER_ID | string | Loyalty Short ID | STG.DERIVED_MASTER_TABLE_NEW | |
LOYALTY_FLAG | int | Flag, 1 or 0 | STG.DERIVED_MASTER_TABLE_NEW | |
REGISTERED_ACCOUNT_ID | string | Registered ID | STG.DERIVED_MASTER_TABLE_NEW | |
REGISTERED_FLAG | int | Registered flag, 1 or 0 | STG.DERIVED_MASTER_TABLE_NEW | |
REST_NO | int | Resturant num | STG.DERIVED_MASTER_TABLE_NEW | |
CITY | string | City | STG.DERIVED_MASTER_TABLE_NEW | |
OPS_DIV_NM | string | OPS_DIV_NM | STG.DERIVED_MASTER_TABLE_NEW | |
REST_TYPE_NM | string | Resturant type | STG.DERIVED_MASTER_TABLE_NEW | |
SERVICE_MODE | string | mode of the purchase (SCAN_REG, MOP_REG , WHITELABEL_REG, KIOSK_REG, CATERING_REG, 3P_NON_LOYALTY, KIOSK_LOYALTY_UNREG, KIOSK_NON_LOYALTY, CATERING_LOYALTY_UNREG, CATERING_NON_LOYALTY, OTHER_REG, OTHER_LOYALTY_UNREG, OTHER_NON_LOYALTY) | STG.DERIVED_MASTER_TABLE_NEW, | White_label restaturant count might be incorrect due to POS issue |
SUB_SERVICE_MODE | string | sub category of the service mode (MOBILE ORDER DRIVE THRU', MOBILE ORDER EAT IN', MOBILE ORDER TAKE OUT, UBER, DOORDASH, SKIP THE DISHES, APP SCAN , PHYSICAL CARD SCAN, WHITELABEL_REG, KIOSK_REG, CATERING_REG, KIOSK_LOYALTY_UNREG, KIOSK_NON_LOYALTY, CATERING_LOYALTY_UNREG, CATERING_NON_LOYALTY, OTHER_REG, OTHER_LOYALTY_UNREG, OTHER_NON_LOYALTY) | STG.DERIVED_MASTER_TABLE_NEW, | Scan and Pay uses a different source of data as the other metrics, and is not classifed by itself on this table. |
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\uD83D\uDCCB How to create your own views using this dataset
Open Tableau from OKTA tile:
Create a new workbook in your tableau space
in the search bar, search for the dataset, “Im using the Weekly Execution offers dataset”. Click Connect.
Create your report by dragging and dropping metrics from the table
Save your created report in a space in Tableau
You can open up your report anytime and will be able to get refreshed data , download the dataset, download the chart, set alerts and share and many more.
