Master Data Quality – the basis for your success
As a result of digitalization, today everything is connected to everything, worldwide. Goods and information constantly flow across various systems and end devices.
The quality of the underlying master data therefore plays a decisive role in the success of a company. Decisions based on incorrect or inconsistent information destroy the beneficial effects of smart planning and control processes.
S/DQC: With expert knowledge & machine learning to the highest master data quality
Beyond the recording and maintenance of master data, the SAP Standard offers only a few mechanisms that focus on the continuous improvement of master data quality in ERP systems.
retailsolutions closes this gap with the innovative add-on S/DQC - the Superior Data Quality Cockpit. With the combination of individual expert knowledge and the latest technological findings in artificial intelligence, you can always rely on a consistent and error-free database.
Your advantages at a glance:
- Machine Learning for intelligent troubleshooting
- Easy configuration of individual expert rules
- Validation of master data already during data maintenance
- Mass testing to identify existing mistakes
- Can be used without programming
- Modification-free integration into SAP systems
How does S/DQC work?
In a checking rule-cockpit, even complex validation rules can be configured individually and without programming. These expert rules are complemented by machine learning algorithms that identify inconsistently maintained master data attributes, especially in fuzzy data constellations.
This way, the storage of incorrect entries is already prevented during the initial creation. Identified errors are automatically displayed and the system assists the correction of the data with learned default values.
Achieve consistent master data in 4 steps
1. Definition of expert rules:
Create individual test rules in just a few minutes. You determine yourself which data is absolutely mandatory. The system then no longer permits incorrect and contradictory entries.
2. Training machine learning algorithms:
Intelligent algorithms learn from identified errors and independently derive principles from them that were not defined in advance by the checking rules. The tool suggests intelligent inputs for the correction of the error.
3. Maintaining master data with an online inspection
The tool checks for each article whether the active expert rules are fulfilled. If an error is detected, you receive suggested solutions for a possible correction. Data is not saved until all errors have been corrected.
4. Mass testing with an offline inspection
With the help of a test report, you can identify already stored master data that contain errors. Identify causes such as faulty processes, interfaces, or information deficits, and begin mass maintenance of the selected data.
Learn more about S/DQC - your tool for error-free master data
S/DQC - Introduction by retailsolutions
Further services in the area of master data:
SD/MA (Master Data Client Acquisition) is a program that aims to improve the master data collection of test and training clients. It is an ETL tool (extraction – transformation – loading) allowing master data to be extracted systematically and consistently from a production system in the form of test-storing files that can be exported to any test client. Upon importing the data to the application, they are checked for consistency.
The rapidly increasing amount of product master data affected the system performance significantly. Therefore, it is very important to discontinue every unneeded product by delisting it from the system. The Master Data Lifecycle Cockpit MD/LC allows automated identification of the products to be discontinued on the basis of previously-set customised rules and supports users by delisting, relisting, liquidating, and discontinuing of the chosen products.
Quickscan Master Data Management Quick Scan Master Data Management focuses on the critical faulty actions related to master data management in order to uncover any weaknesses and deduce optimisation possibilities therefrom. Our approach is based on the following three principal phases: preparation, analysis of weak points and key figures, and laying out specific recommendations for action.
Data quality management is analysed in the framework of Quick Scan Master Data Management while using data-profiling tools. As result, critical weak points are displayed and urgent measures are determined (e.g. deduction of business and checking rules).
Current settings are critically analysed in the framework of ERP reviews related to master data, possible weak points are determined, and specific recommendations for action are deduced thereof.
You would like to know more about Master Data Management?
Don't hesitate, arrange an appointment today with Lars Klimbingat.