Use Cases and Demo

Customer 360 View: Integrate data from various systems to create a unified customer profile, enabling better customer segmentation, personalized marketing, and improved customer service.

Product Master Data: Manage complex product hierarchies, attributes, and relationships to enhance product information management and cataloging.

Supplier Relationship Management: Create a comprehensive knowledge graph of suppliers, their capabilities, and relationships to optimize procurement and risk management strategies.

Demand Forecasting: Integrate data from multiple sources to improve demand forecasting accuracy, enabling better inventory management and supply chain planning.

Financial Risk Assessment: Combine financial data, risk indicators, and regulatory information to perform comprehensive risk assessments and ensure compliance.

Regulatory Compliance: Track and manage compliance requirements, regulations, and their impact on business processes and operations.

Sales Forecasting: Integrate historical sales data, customer demographics, and market trends to generate more accurate sales forecasts.

Churn Prediction: Analyze customer behavior and interactions to predict customer churn and identify retention strategies.

Patient Data Integration: Create a unified patient profile by integrating data from electronic health records, clinical trials, and other sources to improve patient care and outcomes.

Clinical Research: Manage and analyze clinical trial data, research findings, and scientific literature to advance medical research and drug discovery.

Document Management: Create a semantic search and discovery framework for organizational documents, enabling better access to enterprise knowledge and resources.

Employee Skill Mapping: Map employee skills, expertise, and experiences to enhance resource allocation, talent development, and succession planning.

Predictive Maintenance: Integrate data from IoT devices and enterprise systems to predict equipment failures and optimize maintenance schedules.

Real-time Analytics: Analyze real-time data from manufacturing processes to improve operational efficiency, quality control, and supply chain management.

Help Desk Knowledge Base: Create a comprehensive knowledge base for customer service and support by integrating product information, troubleshooting guides, and customer feedback.

Chatbot Enhancement: Enhance chatbot capabilities by providing a rich knowledge graph that includes customer data, interaction history, and product information.

Product Recommendation: Improve product recommendations by analyzing customer preferences, behavior, and external trends.

Inventory Management: Optimize inventory levels by integrating sales data, customer demand, and supply chain information.

Innovation Management: Integrate research data, patents, and market trends to identify opportunities for innovation and new product development.

Scientific Literature Analysis: Analyze scientific literature and research findings to uncover insights and trends in specific domains.

Citizen Services: Enhance citizen services by integrating data from various government agencies and departments to create a unified view of citizen interactions and needs.

Policy Analysis: Analyze the impact of government policies on different sectors, regions, and populations to inform decision-making and policy formulation.

By leveraging SAP HANA Cloud Knowledge Graph Engine, organizations can gain deeper insights, enhance decision-making, and drive innovation across various domains.

A customer is a leading pharmaceutical company engaged in research and development (R&D) for innovative drug therapies. Due to the vast amount of data generated from various sources like clinical trials, research papers, and patents, they face challenges in efficiently managing and leveraging this data to gain insights and accelerate drug discovery.

Implement SAP HANA Cloud Knowledge Graph Engine to integrate, process, and analyze diverse data, facilitating advanced queries and knowledge discovery to support drug R&D.

1. Data Integration
2. Knowledge Graph Creation
3. Advanced Querying and Analytics
4. Collaboration and Data Sharing

1. Data Integration

   Ingest structured and unstructured data from various sources such as clinical trial databases, research publications, patent databases, and gene/protein databases (e.g., PubMed, ClinicalTrials.gov, GenBank).

   Utilize SAP HANA Data Lake and SAP Data Intelligence to enable seamless data integration and preparation.

2. Knowledge Graph Creation

   Define a comprehensive ontology for the pharmaceutical domain, including classes for genes, proteins, drugs, diseases, clinical trials, and patents.

   Map data to the ontology and create a knowledge graph using the Knowledge Graph Engine.

   Establish relationships between different entities, such as "gene-protein," "drug-disease," and "drug-clinical trial."

3. Advanced Querying and Analytics

   Enable researchers to perform complex queries across integrated data sets using the SPARQL query language.

   Implement graph analytics to uncover hidden relationships and generate insights, such as drug repurposing opportunities, potential side effects, and new drug targets.

   Use machine learning algorithms to predict drug efficacy and safety based on the knowledge graph.

4. Collaboration and Data Sharing

   Share the knowledge graph with internal teams and external collaborators, promoting collaboration and reducing data silos.

   Ensure data governance and security using SAP HANA Cloud's built-in features, such as user access control and data encryption.

By implementing SAP HANA Cloud Knowledge Graph Engine, the customer successfully integrated diverse data sources, created a domain-specific knowledge graph, and facilitated advanced analytics. This resulted in improved research productivity, faster drug discovery, and enhanced collaboration, reinforcing their position as a leader in pharmaceutical innovation.

The relationships between classes are fundamental in an ontology, as they describe how concepts are connected.

Here is a generic example of an ontology with some classes and their relationships.

Case	Detail
Ontology of a University	Structured Representation Classes Person Student Professor Course Department Relationships between classes Student is enrolled in Course Professor teaches Course Course belongs to Department Department has Professor