As a businessperson, you probably see a lot of evolving patterns in the data interactions you encounter. In this complex and interdependent world, it is natural to think of data evolution as something that is both dynamic and continually changing, incorporating networks of people and their interactions. Companies today need to monitor the relationships between millions of individuals who engage in many ways to efficiently serve their communities and provide the best possible user experience.
Financial services companies keep tabs on their account holders’ purchasing behavior, and money flows to sell them more services, identify fraud, and minimize losses. Furthermore, project managers monitor the interrelationships between suppliers and schedules to plan and achieve their project objectives. Almost every sector has some connection or interdependence that might benefit from tracking data and resource flows across several channels within an integrated framework.
In this article, you will learn how graph databases can be used to make it easier to handle the relationships between data and make it easier for developers and data analysts to use that data to make business decisions.
What is a Graph Database?
A graph database is a customized NoSQL database that stores and queries data with defined relationships. In a graph database, data points are called nodes connected to related data via edges. The data associated with each node are referred to as properties. Unlike relational databases, graph databases are not constrained by preset schema, allowing data to be connected naturally during an application’s life.
Graph databases are gradually becoming one of the fastest-growing areas in data management due to their simplicity and use.
Applications of a Graph Database
Developers and analysts use graph databases for various applications. Using relationships to process the transactions in your graph databases, you can identify products in which a single purchase is connected to another customer, product, geographic, or other data.
Fraud detection
With a graph database, you can process (almost) real-time purchases and financial transactions, allowing you to detect fraud. A graph database makes it easier to determine whether a specific email address and a credit card are associated with previous fraudulent purchases.
Fraud detection also helps you to distinguish accounts where numerous users share a single email address. It enables you to identify where numerous people are connected to a single IP address despite having multiple physical locations in separate accounts.
Master data management (MDM)
Master data management (MDM) is a comprehensive record of your organization’s operations. It comprises all information on accounts, business units, customers, locations, partners, products, and users. A graph database helps you link this master data to solve significant business problems. With its instant commercial benefit, you can gain a competitive edge by effectively managing and understanding your linked data and networks.
Network and IT administration
Using a graph database, you can quickly link together all of your network and IT operations monitoring tools. You can receive helpful performance insights and better assess vulnerabilities, troubleshoot solutions, plan capacity, and better prepare your business with impact analysis based on user guides.
Identity and access administration (IAM)
A graph database can help you detect and manage to change authorizations, groups, roles, and products. As the complexity of these interrelationships increases, you can track all the data and better manage access to your native graph with real-time results. You can establish an intuitive access management relationship due to the interconnected structure of a graph database. Therefore, you can be faster and more precise while increasing the organization’s overall efficiency.
Recommendation engines
Using a graph database, you can store a customer’s friends, hobbies, and purchasing history. Based on your study of the links between these factors, you can provide a recommendation engine that provides suggestions for what the user would enjoy and prefer. For instance, it is possible to infer with a high degree of accuracy that a consumer will appreciate products like those purchased by another user if they have the same purchasing history and behavior.
Types of Graph Databases
By their data model, graph databases are often divided into two major categories: RDF graphs and property graphs. The RDF graph emphasizes data integration, whereas the property graph is concerned with queries and analytics. These database types are comparable in that they both include points (vertices) and the relationships between them (edges).
RDF graphs
RDF (Resource Description Framework) graphs are meant to adhere to W3C (World Wide Web Consortium) specifications. It is a departure from relational database storage. It communicates information in graphs using three components: subject, object, and predicate.
Real estate graphs
Property graphs are more adaptable representations; hence they are used more frequently across sectors. A property graph depicts the relationships between data points and information about the subject and the nature of these interactions.
Conclusion
As they constitute the backbone of current data analytics capabilities, graph databases continue to expand in popularity. According to some analysts, they account for as much as 80% of recent data and analytics developments. This trend is anticipated to continue as firms seek methods to better exploit data to their advantage via connections or edges between data points or nodes.
As a result of its efficacy and scalability across networks, a graph database, graph technologies, and graph interactions will continue to demonstrate their worth and become increasingly intertwined and indispensable for business applications in the technological environment. A graph database is excellent for data storage because it facilitates the retrieval of independent data but is highly interconnected.
