Data Quality for Accurate and Explainable Decisions
Our expertise spans traditional data cleansing, streamlined rule-based validation, and augmented data quality driven by our proprietary eXplainable AI technology.
Why Data Quality Matters for Decision Intelligence
Data quality refers to how reliable and usable information is within decision-making processes. When data is flawed or inconsistent, work slows down, analytical results become less trustworthy, and teams spend excessive time correcting issues. According to Gartner research, poor data quality costs organizations an average of $12.9 million annually, underscoring the financial and strategic impact of bad data.
While organizations might attempt broad cleanup efforts, indiscriminately improving all data can introduce additional complexity and expense without materially improving decision outcomes.
This is why Rulex Platform addresses this challenge by focusing on decision‑critical data. By reducing noise and applying transparent technologies, it increases trust in the information that underpins analytics and operational decisions.
See case studies
How Rulex Improves Data Quality
Data Cleansing
This stage addresses common issues that affect the formal integrity of datasets. This includes the meticulous handling of missing values and outliers, identification and correction of syntax errors, elimination of duplicates, and harmonization of fragmented data, amongst other data hurdles.
Each of these operations can be seamlessly executed and monitored in an intuitive WYSIWYG environment, designed to be accessible even to users without advanced technical skills.
Rule-Based Validation
When organizations already know which data quality requirements must be enforced, those expectations can be translated directly into explicit validation rules. Using business logic defined by domain experts, Rulex’s rule-based validation allows companies to formalize their existing knowledge and apply it systematically across their data.
Rules are created in spreadsheets using simple if–then syntax and imported into Rulex Platform. Business users can swiftly test and refine rules, remaining in control of the whole process, and generating custom results and data quality analyses in minutes.
AI-Augmented Data Quality
When rules are incomplete or not explicitly defined, Rulex Robotic Data Correction (RDC) provides additional support. The solution, using an explainable AI approach, identifies logical inconsistencies that traditional methods often fail to detect.
Rulex RDC learns directly from the data, automatically generates corrective rules, and proposes targeted corrections. Business experts can review and validate these suggestions, preserving transparency while improving overall data reliability.
Data Quality as a decision layer
Treating information as a decision layer rather than a simple repository changes how quality is managed. The objective is no longer to cleanse everything, but to validate what actually affects decisions.
This approach filters irrelevant information, strengthens confidence in critical data, and supports the evolution from descriptive analytics to decision-oriented processes, where insights directly guide actions and operational performance.
Rulex’s data quality in action
See real-world success stories.
EFFECTIVE MASTER DATA MANAGEMENT
For a world-leading supply chain company, managing master data across multiple planning systems and fragmented ownership often lead to errors, redundancies, and misalignments that negatively impacted downstream planning decisions and overall business performance.
Rulex helped simplify its data management process, starting by extracting and aggregating BOM, transactional and other planning data from SAP tables. The company’s domain expertise was formalized into business rules – governing data structure, relationships, and integrity – written in simple English syntax within a spreadsheet and connected to Rulex Platform’s Rule Engine and the relevant data. This enabled business experts to independently manage the entire data validation logic, from definition to testing and refining, in a fully no-code environment.
Through seamless integration with existing scheduling tools, master data quality checks were automated and built to scale, resulting in higher data accuracy and more reliable supply chain operations.
50M+ records analyzed | 350K+ records corrected
SELF-HEALING MASTER DATA FOR GLOBAL SUPPLY CHAIN PLANNING
As part of a global supply chain transformation, a Fortune 50 industrial client consolidated planning across 100+ regional business units, overseeing diverse product lines. Planners managed up to 6,000 material-location combinations each, across more than 70 million parameters globally. As performance expectations increased, reliable master data became essential to scale best practices and drive productivity. Data quality analyses exposed multiple master data inconsistencies, including missing fields, conflicting values, and systems not always updated to reflect actual material flows. These issues required time-consuming and error-prone manual corrections.
To address this, Rulex implemented an XAI-driven solution that autonomously detected anomalies, generated corrective rules directly from business data, and suggested targeted remediation actions. Domain experts reviewed and approved each proposed change, progressively refining the ruleset while maintaining full governance and control.
Over time, the system iteratively improved data quality, enabling faster and more reliable planning and decision-making.
1M records cleaned in under 5 minutes | 40%+ planning productivity improvement | 100% data accuracy
MEDICAL DIAGNOSTIC DATA VALIDATION
For a leading European medical company operating multiple diagnostic centers, laboratory results had to be reviewed manually one by one to verify machine performance and assess the plausibility of output values. This process was time-consuming, resource-intensive, and limited scalability.
Rulex implemented an intelligent automation solution leveraging both Rulex’s Rule Engine and Rulex Platform’s integration capabilities. Medical experts were able to autonomously define around 100 validation rules in spreadsheets using intuitive plain English syntax. These rules were linked to the analysis data using the Rule Engine task, and the solution applied these rules automatically, approving clear results and flagging uncertain cases for manual review.
Fully integrated with existing management systems, the approach accelerated validation, improved operational efficiency, and freed medical staff for high-value clinical activities.
80% efficiency improvement | Faster result validation
FAQs
1. What is data quality and why does it matter?
Data quality refers to the reliability, accuracy, readiness, and consistency of information. High-quality data reduces errors, improves analytics, and supports better business decisions.
2. How does data cleansing work in Rulex?
Rulex identifies missing values, outliers, duplicates, and inconsistencies through dedicated no-code tasks, harmonizing data from multiple sources automatically.
3. What is rule-based validation?
Rule-based validation in Rulex is powered by the Rule Engine task, which allows business experts to define rules in a spreadsheet using a simple if-then logic and automatically apply them to relevant data to ensure data accuracy and compliance with internal standards.
4. What is AI data quality?
Rulex’s proprietary explainable AI (XAI) technology detects hidden logical errors, derives corrective rules, and suggests fixes that experts can review and apply, strenghtening reliability.
5. How does Rulex improve master data management (MDM)?
Rulex improves master data management (MDM) by consolidating data from multiple systems, applying validation rules, and automating corrections, Rulex ensures consistent, accurate, and decision-ready master data.
6. Can Rulex integrate with existing systems?
Yes. Rulex is designed to integrate with existing enterprise systems, including ERP platforms (such as SAP), databases, data warehouses, scheduling tools, and third-party applications. This allows companies to enhance existing workflows and decision-making processes while maintaining full compatibility with their operational environment. This integration capability is part of Rulex’s broader data agility approach.
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