A graph database is designed around the concept of a mathematical graph. Unlike relational databases, they allow you to connect data together. This enables users to take advantage of specific functions from within the graph database that help during data analysis. While a graph database stores the same kind of data as any other, it allows you to see how data may be related without having to run a JOIN to understand the relationship. There are a growing number of graph database use cases to be aware of.
The structure of a graph database enables it to map different types of relational and unstructured data. This means that it can provide a view of both simple and complex relationships between seemingly unrelated data. All of these factors mean that graph database users can see the links between data without having to first create a hypothesis about a particular data set and test it.
Graph databases are becoming increasingly popular due to the proliferation of unstructured enterprise data. The structure of a graph database makes it a perfect place to store, manage and link these new data types. Not only do graph databases enable succinct data connectivity, they also provide users with a faster path to accurate data analytics. With this in mind, our editors have compiled this list of the most common graph database use cases you need to know.
Master Data Management
Master data is made up of essential company-wide data points. This data typically provides insight related to the core of the business, including customers, suppliers, accounts, employees, goals, and operations. Decisions about what constitutes as master data are made by management teams and business stakeholders. Once these data standards have been met, users can analyze the data as they need to identify key metrics that reveal areas of concern so appropriate actions can be taken to improve operations.
Since master data consists of a series of connections, managing it using a relational database structure can be both complex and slow. In addition, real-time querying is a daunting task due to the fact that users often need to integrate master data with cross-enterprise applications. Graph databases support the relationships between data, so they offer a more efficient and effective way to organize it. This means more relevant recommendations for those working with it, and as such, even greater flexibility.
Compliance (Think HIPPA, GDPR)
The growing presence of regulations is putting a strain on the enterprise, especially those organizations that store sensitive customer data. On the whole, these laws require companies in possession of personal information to manage it in a specific way. Companies also must be able to produce the data, as well as its location as it pertains to an audit. It was recently revealed that the majority of organizations are not complying with GDPR. Could this be because of outdated database technologies?
Regulatory compliance is perhaps the most obvious graph database use case. In fact, a database that tracks and retains the relationships among non-alike data is basically a dream. Graph systems enable single queries that can offer a visual representation of the results. In this way they help organizations maintain compliance by tracing data throughout enterprise systems in a more organized manner than a relational database.
Identity and Access Management
Identity and Access Management refers to the protection and secure provisioning of a user’s digital identities. The majority of cyber threats now exploit or otherwise circumvent traditional login systems such as passwords. Through fraudulent logins or stolen identities, hackers can disrupt business practices, steal proprietary data and finances, and cause significant damage to victims’ reputations. Thus Identity and Access Management offers capabilities and tools to ensure that user logins remain secure and their permissions cannot be exploited to devastating effect.
Graph databases provide organizations the ability to manage multiple roles, groups and authorizations in a way that traditional database technologies struggle with. The graph structure enables users to track IAM relationships with speed, as well connect data along different relationship lines.
Analyst house Gartner, Inc. recently proclaimed that the future of BI and analytics is AI and machine learning. The divide between the open source community and those that use commercial software products is narrowing. Gartner predicts that 75 percent of end-user products will soon be developed with commercial tools in mind. Gartner notes that many software providers are equipping their analytics products with built-in connectors to the open source environment while offering the needed enterprise functionalities.
Since graph databases provide a connective layer between data not present in other database structures, they are ripe for use in conjunction with machine learning software. Machine learning tools can make use of these connections to further enhance and speed up the process of analysis. This is especially beneficial when it comes to machine recommendations; a scenario where the machine scans the database and provides the user with a starting point or initial points to sift through.
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