Graph databases are essential in today's business world, where customers expect quick and accurate insights. These databases are uniquely capable of efficiently storing and retrieving complex relationships between data points.
With the quantity of "big data" and increasingly complex data structures, traditional databases often struggle to keep up. However, graph databases provide an effective solution through the "label property graph" model, which allows for storing arbitrary data.
PRODYNA is the best solution provider for implementing graph databases, enabling businesses to efficiently manage and leverage complex data structures to derive valuable insights.
PRODYNA has built various solutions for different customers based on Neo4j. The designed and developed custom solutions provide strategic advantages, enabling the customer to access the necessary information quickly.
The basis for fraud detection is a knowledge graph that stores all customers, contracts and insurance claims. Algorithms can then automatically detect anomalies in the graph as fraud attempts. For example, two persons have reported a car accident twice. Such abnormalities can be quickly noticed in the graph. PRODYNA developed various projects for the insurance sector.
We have created solutions for our customers that provide complete information about their products, components, and possible suppliers. These solutions enable businesses to quickly identify supply bottlenecks and alternative suppliers, allowing them to navigate supply shortages caused by events such as the COVID-19 pandemic more effectively.
Managing products and markets with unique and complex requirements, along with their corresponding variants, can be a challenging task. However, implementing a knowledge graph specifically tailored to these requirements can provide a comprehensive solution, helping businesses harmonize their diverse needs more effectively.
Implementing a knowledge system that includes information on all employees, projects, products, and research reports can help businesses network employees and facilitate collaboration, even after an employee has left the company. This results in a competitive advantage for companies with such a knowledge system over their competitors, as it helps mitigate the risk of knowledge loss due to employee turnover.
The principle is straightforward: graph databases only know nodes and edges. Both have types and properties. This model is called the "Label Property Graph" (LPG). This model is suitable for storing arbitrary information because all database models are special cases of graphs at the end, including the relational model used by most databases.
The sources for the data are the existing IT systems and their databases. In many companies, we find so-called "silos," i.e., more or less well-integrated systems, each containing a part of the information.
After importing the data into the graph database, the individual graphs are connected to form one large graph. This connection is made using known identifiers (e.g., customer number), but sometimes it is necessary to use more complex algorithms.
In the knowledge graph, spread relationships can be identified. Especially the possibility of graph databases to find paths results in exciting opportunities.
Neo4j is the market leader in graph databases. PRODYNA recognized the potential of graphs and graph databases early on and has been a partner of Neo4j for many years. As a result, we have implemented numerous solutions based on Neo4j.
The information in the data is not only the data itself but also the relationships between the individual data.
Graph databases can apply various graph algorithms to the graph. For example, pathfinding is a simple but commonly used algorithm, i.e., all possible or shortest paths between two nodes are found automatically. Another algorithm worth mentioning is Centrality, which identifies nodes with many connections.