Enhanced Financial Reporting
This Business Use Case is about Data Integration and Transformation as well as Data Storage and Retrieval.
Key Features and Benefits
- Semantic Data Harmonization: Harmonizes financial data for consistent and accurate financial reports.
- Ontology Mapping: Maps database schemas to a unified ontology for improved data integration and clarity.
- RDF Generation: Converts financial data to RDF format for flexible exchange and regulatory compliance.
- Automated ETL Processes: Automates ETL processes to reduce errors and ensure timely, accurate financial reporting.
Harmonize Financial Data and Ensure Compliance with Semantic and Linked Data Integration Solutions
Manual data integration and inconsistent data formats can lead to errors, inefficiencies, and delays, compromising the reliability of financial reports and potentially leading to regulatory non-compliance.
Applying Semantic and Linked Data Management ensures financial information from different sources is harmonized, providing a consistent and accurate foundation for reporting. Database Schema to Ontology Mapping helps translate complex financial data schemas into an understandable ontology, improving data integration. RDF Generation converts financial data into RDF format, enabling flexible and semantically rich data exchange. While ETL processes automate the data flow, reducing manual intervention and errors, Master Data Management (MDM) creates high-quality Golden Records for reliable financial reporting. Finally, Data Quality Controls validate the data before reporting, ensuring that it meets the required regulatory standards and maintains data integrity throughout the process.
Technical Capabilities
Technical capabilities encompass the range of skills, tools, and methodologies to implement and manage advanced technological solutions.
Semantic and linked data management involves organizing and connecting data based on semantic relationships. This capability provides data interoperability and simplifies the integration of various data sources by utilizing standards such as RDF and OWL.
ETL processes involve extracting data from numerous sources, transforming it into a suitable format, and loading it into a target system. This capability is required to integrate data and prepare it for analysis and reporting.
Technical Use Cases
Explore the specific functionalities and applications of technology solutions.
Database schema to ontology mapping refers to translating a database’s structural framework (schema) into a conceptual knowledge representation (ontology). The process aligns the database’s entities and relationships with the ontology’s classes and properties, allowing for better data integration, interoperability, and semantic querying.
RDF generation is the process of converting data into the RDF (Resource Description Framework) format, a standard model for data exchange on the Internet. The data is structured as triples, each consisting of a subject, predicate and object, to represent information in a way that is both machine-readable and semantically rich.
Master Data Management (MDM) is evolving through the integration of Knowledge Graphs and Semantic technologies, allowing for more flexible and integrated data structures. This approach, known as Semantic Master Data Management, enables enterprises to generate Golden Records for local Data Products, such as Customer Data, by exploiting semantic relationships and ontologies. Companies that deploy MDM based on Knowledge Graphs can gain a more holistic view of their data assets, improve data quality, and improve decision-making processes throughout the company.
Data management relies heavily on ETL (Extract, Transform, Load) operations, which allow data to be transferred from many sources into a central database. The process includes extracting data, transforming it to meet certain needs (which can include data cleansing, profiling, encryption, and masking to ensure data quality and security), and then loading it into a target system like a data warehouse or RDF graph database.
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