Case studies of successful knowledge graph deployments in various industries
Are you tired of traditional database models that fail to capture the interconnectedness of data in the real world? Do you want to take advantage of the latest in data management technology to drive innovation, gain insights, and streamline your business operations? Then you need to consider knowledge graphs!
Knowledge graphs are powerful tools that allow you to represent complex, interrelated data in a way that is easy to understand and manipulate. But how do you deploy and use them effectively in different industries and contexts? We’ve scoured the web for the best case studies of successful knowledge graph deployments in various industries, so you don’t have to. Read on to discover how others have leveraged the power of knowledge graphs to elevate their businesses and transform their industries.
What is a knowledge graph?
Before we dive into the case studies, let's define what we mean by "knowledge graph." A knowledge graph is a database model that represents information as a network of entities, relationships, and attributes. Rather than organizing data in rows and columns or tables, knowledge graphs use nodes and edges to map out the connections between different pieces of information.
Knowledge graphs are based on linked data standards, which means that they combine structured and unstructured data sources and provide a flexible and intuitive way to map complex relationships. They allow you to capture both the explicit and implicit connections between data points, reflecting the way that real-world knowledge is organized and conceptualized.
Healthcare Industry: Two case studies from pharmaceutical companies
Let's begin with the healthcare industry, where knowledge graphs have the potential to drive innovation in research and development, as well as in patient care and outcomes.
The Roche Knowledge Graph
Roche, one of the world’s leading pharmaceutical companies, has deployed a knowledge graph to manage data from clinical trials and advance drug development. The Roche Knowledge Graph integrates millions of data points from thousands of sources, including medical literature, regulatory agencies, and patient data. It leverages semantic web technologies to annotate and link the data points, creating a searchable and flexible resource for researchers and developers.
The Roche Knowledge Graph has transformed the way that Roche manages drug development. With the ability to easily explore and analyze a wide range of data sources, researchers can now identify new drug targets, assess clinical trial feasibility, and evaluate the safety and efficacy of potential drugs. Moreover, the Roche Knowledge Graph provides a common language for different teams and systems, increasing collaboration and reducing information silos.
The Merck KGaA Knowledge Graph
Merck KGaA, the global science and technology company, has also adopted a knowledge graph to manage and analyze diverse data sources in drug discovery and development. The Merck KGaA Knowledge Graph integrates scientific publications, patents, clinical trial data, and chemical and biological data, among other sources. It uses semantic annotations to capture the relationships between different data elements and to support exploration and discovery.
Merck KGaA has seen impressive gains in efficiency and innovation thanks to the deployment of the knowledge graph. The company is now able to discover new drug targets and repurpose existing drugs for new indications, among other benefits. Moreover, the Merck KGaA Knowledge Graph has enabled the company to enhance collaboration between different teams and to streamline its data management processes.
Finance Industry: Two case studies of successful deployments of KG in finance
The finance industry is another area where knowledge graphs have the potential to make a big impact. By integrating data from multiple sources and modeling complex relationships between different financial instruments, knowledge graphs can provide new insights, reduce risk, and improve decision-making.
The Lloyds Banking Group Knowledge Graph
Lloyds Banking Group has developed a knowledge graph to support its anti-money laundering efforts. The knowledge graph ingests and integrates data from multiple sources, including transaction data, party information, and watchlist data. It identifies and prioritizes information based on risk indicators and generates alerts to support compliance investigations.
By deploying a knowledge graph, Lloyds has improved its ability to identify and mitigate financial crime risks. The knowledge graph has enabled the bank to identify relationships between different entities and to detect suspicious patterns of behavior that may have gone unnoticed using traditional data management techniques.
The NTT DATA Financial Knowledge Graph
NTT DATA, a global IT services company, has developed a knowledge graph to support financial risk management. The NTT DATA Financial Knowledge Graph ingests and integrates data from market data sources, credit bureau data, and other data sources, creating a complete view of financial risk exposure. The knowledge graph supports a range of risk management activities, including credit risk assessment and stress testing.
With the NTT DATA Financial Knowledge Graph, financial institutions can gain a more accurate and comprehensive understanding of their risk profiles. By modeling complex relationships between different financial instruments and data sources, the knowledge graph can identify emerging risks and support more effective and timely risk management.
Retail Industry: Two case studies of the successful deployment of a knowledge graph in retail
Knowledge graphs can also be useful in the retail industry, where they can help to manage and analyze data on customer preferences, purchasing behavior, and supply chain logistics.
The Walmart Knowledge Graph
Walmart, the world's largest retailer, has developed a knowledge graph to manage and analyze data from its sprawling supply chain. The Walmart Knowledge Graph integrates information on products, suppliers, logistics, and pricing, among other factors. It supports a range of analytics and decision-making activities, including demand forecasting, inventory optimization, and pricing analysis.
The Walmart Knowledge Graph has enabled the company to gain new insights into its operations and to optimize its supply chain for greater efficiency and profitability. Rather than working with siloed data sources and fragmented information, Walmart now has a unified view of its supply chain, allowing it to make more informed and strategic decisions.
The Zalando Fashion Graph
Zalando, a leading European online fashion retailer, has developed a knowledge graph to manage and analyze data on customer preferences and behavior. The Zalando Fashion Graph ingests and integrates data from a range of sources, including transaction data, clickstream data, and customer interaction data. It creates a unified view of each customer's preferences and provides personalized recommendations and offerings.
By deploying a knowledge graph, Zalando has been able to increase customer engagement and to drive sales. The Zalando Fashion Graph enables the retailer to create a more personalized and seamless experience for customers, enhancing loyalty and satisfaction.
These are just a few examples of the diverse ways in which knowledge graphs can be deployed to support innovation and transformation in different industries. By leveraging the latest in data modeling and management technology, businesses can gain deeper insights, reduce risks, and streamline operations. If you are interested in harnessing the power of knowledge graphs for your business, make sure to explore all the possibilities and to choose the best knowledge graph solution for your needs.
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