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 :
Code Block language sql loyalty.tempreportbi.weekly_execution_transtransaction_level
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
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Open Tableau from OKTA tile:
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Create a new workbook in your tableau space
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in the search bar, search for the dataset, “Im using the Weekly Execution offers dataset”. Click Connect.
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table |
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Save your created report in a space in Tableau
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