Success story
Rule-Based Data Quality for Supply Chain
Master Data
Improve master data quality with validation rules defined directly by business experts, applied at scale across complex supply chain operations.
records analyzed
for a single
supply chain organization
records corrected
for a single
supply chain organization
rule definition
by business experts
master data quality checks
A world-leading supply chain company was managing large volumes of master data across multiple planning systems, both online and offline. The data covered interconnected areas including materials, production, and distribution logistics, where inconsistencies could directly affect the efficiency and reliability of supply chain operations.
The company’s business experts already knew the validation logic that the data needed to meet. The challenge was applying that knowledge consistently and at scale, without relying on developers to translate and maintain the rules.
Rulex enabled these experts to define data quality rules themselves and automate their execution across large volumes of supply chain master data, turning business knowledge into a scalable and repeatable validation process.
Turning supply chain expertise into automated validation
The process started by extracting and aggregating data tables from multiple SAP sources. Different categories of supply chain information – including BOMs, transactions, SLs, PIRs, and other relevant datasets – were brought together to create the data foundation for validation.
Business experts then defined the required controls themselves in Microsoft Excel, using simple if-then syntax and without writing code. The rules were imported into Rulex Platform together with the data to be analyzed, turning domain knowledge into executable validation logic.
Once in place, the checks no longer had to be performed as a separate manual activity. Rulex integrated the validation process into the company’s existing scheduling tool, allowing master data quality controls to run automatically while maintaining compatibility with its data visualization systems.
The result was an end-to-end process designed not only for the company’s current data volumes, but also to scale alongside its long-term growth and digital transformation plans.
The impact
The solution made supply chain master data validation easier to manage while extending it across large volumes of information:
- 50M+ records analyzed across supply chain data for a single organization.
- 350K+ records corrected.
- Validation rules defined directly by business experts in Excel.
- No coding skills required to formalize supply chain knowledge.
- Quality checks fully automated through the customer’s existing scheduling tool.
- Compatibility maintained with existing data visualization systems.
- A scalable validation process designed to support future growth and digital transformation.
How Rulex delivers rule-based data quality
Not every data quality problem requires AI to discover what is wrong. When organizations already know the conditions their data should satisfy, Rule-Based Validation provides a direct way to turn that knowledge into consistent, automated controls.
In Rulex Platform, this logic is operationalized through the Rule Engine task. Domain experts can define transparent if-when-then rules in external files such as Microsoft Excel, then connect those rules and the relevant datasets for automated execution
This approach complements the other data quality methods available in Rulex Platform:
Together, these approaches allow organizations to match the data quality method to the nature of the problem rather than relying on a single technique for every type of data issue.
Tech point
Take a closer look at the Rulex Platform capabilities behind this rule-based data quality solution.
01. Data integration and orchestration
Rulex Platform can import and combine data from enterprise databases, files, remote file systems, and APIs, automatically converting incoming information into a common tabular format for processing. Database connectivity includes systems such as SAP HANA, SQL Server, Oracle, Snowflake, and PostgreSQL, among others.
Connections can be stored as Saved resources and reused across flows, with permissions controlling who can access or modify their configuration. This allows data connections to be managed separately from individual workflows and supports more controlled, scalable data integration across enterprise environments.
02. Business Rules Engine
Rulex’s Rule Engine task connects business-defined rules directly to the datasets they need to evaluate. Rules can be written in intuitive if-when-then syntax in external configuration files, without altering the underlying workflow, then executed automatically at scale. Priorities, output attributes, and result detail levels can be configured to support different operational or compliance requirements, while embedded functions and mathematical formulas allow more sophisticated validation logic when needed. Through API automation, rules can also be triggered in real time or integrated into existing enterprise systems.
Why Rulex for supply chain master data quality
Supply chain experts already know what good data looks like – the difficulty has always been applying that knowledge consistently, across millions of records, without turning it into a new coding or maintenance burden.
Rulex closes that gap: domain experts define the rules, and the platform runs them automatically at scale. Combined with Rulex Platform’s broader data quality capabilities, organizations can rely on business rules wherever the requirements are already known, and turn to other methods when the problem calls for a different approach.
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Frequently asked questions
When should organizations use Rule-Based Validation for data quality?
Rule-Based Validation is particularly suited to data quality requirements that can be expressed through known business logic. Domain experts can formalize this knowledge as transparent if-then rules and apply it consistently across the relevant data.
Do business users need coding skills to create validation rules in Rulex?
No. In this project, business experts defined rules themselves in Microsoft Excel using simple if-then syntax. The rules were then imported into Rulex Platform together with the data to be analyzed, allowing domain knowledge to be operationalized without programming.
How does Rule-Based Validation differ from Rulex's AI-augmented data quality approach?
Rule-Based Validation starts from existing business knowledge and turns it into executable validation logic. Rulex’s Robotic Data Correction takes a different approach, using explainable AI to uncover logical inconsistencies that may not be captured by predefined business rules, generate corrective rules from the data, and propose remediation actions for expert review. Organizations can use either approach independently or combine them: applying known business rules to address defined data quality requirements, then using AI to identify additional inconsistencies that those rules do not cover.
Can Rulex's Business Rules Engine handle complex validation requirements?
Yes. Beyond straightforward validation conditions, Rulex’s Rule Engine supports configurable rule priorities, output attributes and result detail levels, as well as embedded functions and mathematical formulas for more sophisticated business logic. Its architecture is designed to execute rules across millions of records.
Can Rulex apply business rules across large supply chain datasets?
Yes. Rulex is designed to apply validation logic at scale. In this project, more than 50 million records were analyzed, with over 350,000 records corrected for a single supply chain organization, while master data quality checks were automated through the company’s existing scheduling tools.