AI and MDM: Engineering Data Excellence
Unlock the Potential of Artificial Intelligence with Trusted Data Quality
... high-quality data remains the foundation for success
increase spending on GenAI
in their organizations
as the greatest challenge to
realize AI potential
claim their organizations
lack the right data foundation
by Thomas H. Davenport, Randy Bean, and Richard Wang
Join the AI Webinar Series
From hype to real work
Understanding AI is just the beginning. Applying it in daily data work is where value is created. That is why we are launching a webinar series focused on real-life implementation scenarios. What you will learn:
- Where AI already supports data teams today
- How companies apply AI in data quality, governance, and enrichment
- What works in practice — and what to avoid
Meet CDQ Genius
CDQ Genius is now available as a first AI-supported interface for working with CDQ knowledge and selected CDQ services. In this first version, CDQ Genius helps data managers get quicker answers and guidance in their daily work with business partner data. Users can ask questions in natural language, explore CDQ product knowledge, and get support when working with selected data management scenarios.
What CDQ Genius can support today:
- Answer questions about CDQ products, services, and available capabilities
- Help users better understand business partner data topics, reference data, and data quality concepts
- Support selected workflows such as looking up business partners and exploring onboarding-related scenarios
- Provide a natural-language entry point instead of searching through documentation or navigating different areas manually
- Work within a controlled CDQ environment with role-based access
CDQ Genius does not yet cover all CDQ functionalities, and the available capabilities will be expanded step by step. The focus now is to make selected CDQ knowledge and services easier to access, easier to understand, and easier to use in daily data management work.
AI-supported Duplicate Detection
Sartorius was looking for a sustainable solution to mitigate duplicate-related risks: not only ensuring that every data defect could be addressed post-creation, but also onboarding clean, unique records into the system at the first instance.
Duplicate checks are seamlessly integrated into Sartorius system, running automatically in the background. The algorithm swiftly identifies potential duplicates, triggering a streamlined process. When a potential duplicate is detected, a work item is generated for manual review, ensuring accuracy and precision.
AI in Data Management – Hype or Help?
AI creates value in data management only when it works on trusted source data and governed rules. Web-trained LLMs are not a trusted source, they are reasoning layers that must operate on authoritative inputs. Our new whitepaper breaks down where AI creates real impact in master data management.
Here’s a closer look at how CDQ uses AI to boost the data cleansing process
Data Profiling and Assessment
CDQ utilizes AI algorithms to identify patterns and anomalies within datasets. This capability enables efficient detection of inconsistencies and duplicates. Furthermore, CDQ employs machine learning models to evaluate data quality using metrics such as completeness, accuracy, consistency, and timeliness.
Duplicate Detection and Merging
By employing AI, CDQ can identify and merge duplicate records by recognizing that different entries refer to the same entity, even if the data is not identical. Machine learning techniques can match similar but not identical records, improving the accuracy of duplicate detection.
Anomaly Detection
For anomaly detection, CDQ employs AI-powered models to identify outliers that indicate data entry errors or unusual patterns. CDQ's machine learning capabilities allow for the analysis of historical data to detect deviations from expected trends, signaling potential data quality issues.
Scalability and Efficiency
One of the significant advantages of CDQ is its ability to handle large datasets efficiently. This makes CDQ ideal for big data environments. By automating repetitive and time-consuming tasks, CDQ frees up human resources, allowing them to focus on more strategic activities.
Automated Data Correction
In the realm of error detection and correction, you can significantly enhance the process by automatically identifying errors like typos, incorrect formats, and invalid entries, where CDQ identifies a need for improvement. Additionally, CDQ ensures uniformity across datasets by standardizing data formats, units, and values.
Data Enrichment
Enriching datasets is another area where CDQ shows its strength. By integrating external data sources, CDQ fills in missing information and enhances data completeness. And with advanced ML-capabilities, CDQ provides a reliable tool to understand and extract relevant information from unstructured data sources.
Monitoring and Improvement
CDQ ensures continuous monitoring of data quality in real-time, providing alerts and automated responses to emerging issues. Through feedback loops, CDQ’s machine learning models learn from past corrections and user feedback, continually enhancing their accuracy and effectiveness.
By leveraging these AI capabilities, CDQ significantly enhances the accuracy, reliability, and usability of corporate data. This leads to better decision-making and operational efficiency, ultimately driving greater value for the organization.
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