First step before analyzing and working on a brand's data is to check the quantity of data stored in system. Data health check indicates how accurate, complete and consistent the data stored in the system is along with the customer’s data.
The Data Health Check (DHC) also verifies the quality of data mapping, and performs an overall checking on the brand's basic KPIs till the date of the ongoing data health check.
The following are fields where the data capture is shown.
- Customers registered, Customers transacted, Sales, Transactions, Units sold: Gives an overall look of the main KPIs of the org for a selected duration.
- Loyalty data: It gives a year on year KPIs data for a selected duration.
- Demography data: It gives details about the mapping of marital status, gender and date of birth.
- Store mapping: It gives details of states and cities where stores are present. Store-wise KPIs are shown, along with closed stores (if any) for a particular year.
- Product data capture: It gives details of product mapping.
- Month on Month summary of KPIs: Gives monthly summary details of KPIs.
You need to provide the following details before running the Notebook.
|Org_id||Enter the org ID(identifier) of the organization or brand.|
|Start date and End date||Duration for which the data health check(DHC) notebook is going to run. Format: yyyymmdd. For example, 20210925.|
|Marital status, Gender, and Date of birth (DOB)||Enter the respective column names from the users table|
|Operational stores year||Enter the year to check whether all stores were operational or not. Format: yyyy. For example, 2021.|
Read command 2 before running the notebook. The markdown comments will be updated with detailed data insights based on the inputs and obtained results.
The final output appears on the dashboard as Data Health Check.
Open your cluster-specific link provided for the Notebook.
|Data health check||India SEA|
|Data health check - OU level enhancement||India SEA|