Types of Rules Wizards

Types of Rules Wizards

We encourage customers to begin by identifying whatever data errors are causing impacts to their business, and start with rules to identify those exceptions. Some of the Rule types are straight forward, while others were created for specific customer use cases, and the potential application to your environment may not be readily apparent. Over time, potential rules and use cases become more apparent as you build out a more holistic data governance approach.

The Rules wizards you can configure are organized in the following categories:


Basic, logical checks you would typically do in any environment, without necessarily knowing the contents of the data

Business Context

Validations and checks based on a specific business understanding of what the data should be

Process Quality

Is the data valid or what is expected based on the process or purpose


Alerts if data not updated or process not completed by a set time or duration

X-System Integrity

Compare and reconcile details and records across systems – without having to consolidate or pull the data.

Within these categories are various rule types that can be configured to validate data and generate the exceptions. There may also be different approaches to start with different rule types that generate the Exceptions you’re looking for. We encourage users to explore different rules and approaches, as there is some art to determining what works best within your environment and business processes.

Type of Audit Rule


Example (s)



Availability and completeness of data. Are there missing values?

All the records where address field is missing in customer ship-to information.


Record exists in one system’s set of Records, but not in another system’s set of what should be the same Records.

Order A exists in ERP’s list of Orders, but not in Transportation System’s list of Planned Orders


Group of Records of a certain type exist in one system but not in another. Summarized by type

Shipments for Customer X, not found in list of Shipments by Customer


Product Category B, not found in list of Products.

Business Context


Are the values within a data element unique in the dataset?

Duplicate customers in customer table. Data is not unique


Are the Count of Unique Values within a range or equal to a defined number

Expect unique locations to be between 95-100 based on 100 ship-from locations. Over 100 indicates error, under 95, something may be wrong


Are the values in data within the expected range? Either within a continuous range, within a Pick List, or one of a specific value.


Manufacturing yield should always be between 0.01 and 1

Order type in sales orders should only be one of ZOST, ZOCO, ZOFR.

Item_category in shipments should only be one of values in the item_category reference


Ability to write Custom SQL (Non-SAP)

Any Custom SQL Query

Process Quality

Address Validation

Validates existing address against Google Maps. Also enables standardization

123 Main Rd, should actually be 123 W Main St.


Does valid data exist in reference systems to execute a process

Does each SKU have a valid Bill-of-Distribution?

Does each Item have weight/measure populated?


Is the count of records within the expected range?

Count of sales orders in open order extract should be between 800k and 1M records


Is the aggregated quantity within the expected range?

Total volume of open order quantity should be between 75M and 100M units



Monitoring if Data not present or updated by a certain time, send alert

If Orders not uploaded by 5pm.

If Updated Forecast not received by Plan Generation at 12am.


Monitors Data to determine if process from event 1 to event 2 exceeds

If Product attributes not populated within 1 week of New Product being created in system

If Booking Confirmation not received within 3hrs of Booking Request.

X-System Integrity


Compare the count of records between 2 different data sources at same or different granularity

Compare total Orders or Shipments in ERP at transactional level to Order or Shipment summary loaded in data warehouse at aggregated level



Create your own custom validation rule within one data set and compare to the same query in a separate data set or system.



Compare aggregated quantity between 2 different data sources

Compare the total shipment volume for last 3 months in ERP with shipment volume loaded in data warehouse at aggregated level – by product line


Holistic comparison of count of records and volume for metric fields across different attributes

Compare Open Sales Orders in SAP with Sales Order extract in JDA for count, total of orderquantity, qtyopen by order_type, item_category, plant, key customer accounts, product line


Compares Records at a Field level and highlights discrepancies

For Planned Orders in ERP vs Planned Orders in Warehouse System, validate dates, quantity and price and show discrepancies




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