The services result from successfully conducted proof of concept projects within our AI MDM Community. Based on the CAMELOT Intelligent Data Management approach, we have gathered the profound knowledge and experience in the field and accounted for lessons learned along the way. We have advanced from the initial pilot project level and stabilized the services to ensure smooth and consistent performance. Supported by a powerful front-end, a friendly and convenient UI makes services easily manageable by all user groups.
As a result: A whole portfolio of intelligent data management services that you can flexibly customize to your needs and select them based on your demand. It can work on your architecture, our architecture, as a stand-alone service or tightly embedded in your workflows. In the backend powered by sophisticated data science and machine learning solutions, on the frontend with interfaces easy to use by business users.
CAMELOT Intelligent Data Services are powered by the SAP Data Intelligence Platform. The underlying functionality allows to deploy machine learning and data science technologies at an operational level, enabling execution for modular data pipelines across distributed architectures.
Currently, CIDS focus on three main areas:
- Automated data extraction for maintenance,
- dynamic data quality rules,
- vendor hierarchy optimization.
Automated data extraction for master data maintenance
The CAMELOT AI Driven Data Extraction Tool (CADET) is built for replacing the manual data entry procedure through automated data extraction from both structured and unstructured documents. By eliminating the manual component of work, the risk of a human error is reduced significantly. An additional step of human entry validation ensures the best possible data quality and enables further learning of the algorithm. The result is high-quality data for better insights and analytics and saved time in data maintenance.
Dynamic data quality rules
The rule mining service addresses the derivation of rules and logical patterns from data. This can be a long and costly process, especially if done by a professional. We offer an automated solution for that – a tool that extracts business logic from available data in the form of rules. Those can be later used for automated data population and overall reduction of master data inconsistencies resulting in higher data quality and hence strengthened decision-making.
Vendor hierarchy optimization
Finally, vendor hierarchy optimization utilizes the power of the publicly available data to enrich vendor-related information. This provides better quality of data in a cheaper and faster way offering spend consolidation potential. The tool can be seamlessly integrated into existing ERP/MDG systems, allows updating relevant datasets and is scalable to similar use cases and data objects.
CIDS – the Outlook
What’s next? Soon we will continue working on delivering further services for the CIDS. Most of them – just like their predecessors – have successfully been implemented as PoC projects. As in the case with our current tools, the focus will remain on solutions that deliver high-quality data in a faster and cheaper way. Currently, the following areas are considered:
Up Next: Future CIDS Services
- Fraud detection. Utilizing anomaly detection and pattern recognition, the tool will address prevention, detection and investigation of fraudulent activities in transactions or suspicious behavior.
- Automatic approval of data changes. Beside time saved in data maintenance, the solution enhances data quality through avoidance of manual errors.
- Consistency checks. This service provides automated consistency checks for documents. Based on semantics classification and image extraction, it simplifies adherence to compliance standards and reduces data maintenance efforts.
- SAP MDG Assistant. A chatbot designed for a large variety of interactions depending on the knowledge and expertise of the end user with both verbal and written chat entries possible. The assistant reduces time in data maintenance and assists in translating typical business language to material attributes and values.
- Supply Chain Scenario Mining. The tool extracts supply chain configurations that can serve as a basis for automated material data population, which eliminates the human error factor, enhancing data quality and providing a reliable basis for decision making.
Our next articles will give you a detailed overview of the services’ functionality and the underlying architecture.