In today’s fast-paced business environment, data continues to be at the forefront of many critical business decisions. However, understanding and interpreting data has become a key challenge considering the volume and complexity of data across organizations. As the demand on timely and accurate data continues to rise, precise database selection based on target analytics and overall data strategy is crucial as it can enable organizations to effectively manage and leverage data to their advantage. For organizations with highly connected data, graph technology and databases have the potential to reveal unknown relationships, improve analytics, and reduce analytical processing requirements.
Key Differences Between Graph & Traditional/Relational Databases
Introduced into wider use in 2007, graph databases are dynamic in nature, designed for storing and querying data that is connected via defined relationships. While traditional relational databases have been the established norm in most organizations, they may be limiting the amount and type of analytics that can be easily run on the underlying data. With traditional relational databases, data is stored on separate tables and relationships must be inferred via the usage of JOINs or lookups. This method can quickly become complicated and resource-intensive when attempting to navigate across several layers of relationships. Alternatively, in a graph database, the relationship between individual records (or objects) is stored as a record that can be named and contains its own set of attributes, allowing discovery of potentially hidden relationships even across distant objects.
Although many institutions have historically implemented relational and other non-graph database types, the benefits of graph databases are becoming more apparent, especially with the complexity of certain present-day data analytics needs. Leveraging graph databases can help organizations better understand data relationships in a matter of seconds, as opposed to hours or days via other database types. When the end goal is to understand specific relationships across data, a graph database is the best solution to get results in the least amount of processing time. However, graph databases are not the answer for every data set or analytics problem. Clearly defining what is expected from the data is imperative when selecting the appropriate database type to meet the end goal.
One key factor to consider is that a portion of the overall data set can be maintained in a graph database while most of the data set continues to exist in its current system and database type. In other words, an organization does not need to migrate all its data to a graph database to realize the benefits.
Graph Database Use Cases & Benefits
The versatility of graph databases means that they can be leveraged for numerous applications across multiple industries. Whether used in risk mitigation or to gain insights on specific data points, graph databases can yield various benefits:
- Suspicious Activity and Real-Time Fraud Detection
Graph databases allow for near-real-time transaction processing—thus, it’s not only the transactions that can be modeled to identify relationships revealing potentially fraudulent activity. With graphs being flexible in nature, other information can be utilized to pinpoint different customers leveraging the same email, focusing on shared IP addresses registered in various locations, or searching for data points like credit cards connected to previously identified fraud cases.
- Real-Time Product Recommendations and Customer Analysis
An organization looking to optimize its product recommendations or gain a holistic view of its customers would benefit from graph databases’ ability to query or run certain algorithms that identify and link similar customer product information and behavior, providing a complete view faster than traditional non-graph technologies. Leveraging graph analytics, many organizations can gain a 360º view of their customers while gaining valuable insights into predictive needs and trends.
- Data Privacy
As the volume of data continues to increase across organizations, managing data privacy and associated regulatory compliance requirements can become complex, especially when trying to understand the type of data that is being stored or even transformed. With graph technology, organizations can obtain an accurate picture of the complete data life cycle and its lineage. With a complete picture of where data originates, where it is copied, and ultimately utilized, organizations are better equipped to fulfill regulatory requirements such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA).
- Organizational Hierarchies
Institutions are continually tasked with tracking organizational relationships within their client and supplier databases. Specifically, they often struggle to account for and accurately represent the complexities around holding companies, subsidiaries, regional offices, and other intricacies within a traditional relational database. A graph database, however, provides the flexibility to extend the model at any time capturing the individual nuance of a particular organization, creating relationships between distant subsidiaries, and adding attributes to any relationship or object within the hierarchy. By using a graph database, these highly complicated scenarios could be simplified and even turned into a benefit for sales, marketing, and other prospects via the identification of new connections and opportunities.
What Organizations Should Consider
Evolving an institution’s data landscape can be challenging considering all the important decisions to be made, especially with various vendors offering a wide array of solutions and customizations that may or may not be what the institution needs. To fully understand the benefits and options that come with implementing a new solution like graph databases and technology, it is imperative that organizations perform a thorough vendor assessment on the type of data model that may be best suited to meet their strategic goals. Organizations should clearly define their data analytics objectives, key metrics, and corresponding business requirements while factoring in the overall data strategy to ensure the right investment is made.
While graph databases have the potential to unlock insights for many organizations, they also have a few caveats:
- Graph databases are most effective when relationships are irregular. If most relationships in the data set are regular and simple, graph databases duplicate a large amount of information.
- In some cases, graph databases can be difficult to scale. Some vendors have begun offering solutions to the one-tier architecture problem that existed in early graph database systems.
- Querying/updating languages can differ across different graph products. Relational databases typically rely on SQL as the base language. This can lead to challenges in hiring or training employees.
- Graph databases are still relatively new in use, and the technology is changing and advancing rapidly.
The choices made for an organization’s data architecture require an analysis of the pros and cons of the different infrastructure choices available. Graph databases provide another tool in the data architecture toolbox to tackle often-challenging data analytics problems. Embedding its consideration into your overall data strategy will support the path to advanced real-time analytics and improved decision-making.
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Resources
17 Uses for Graph Database and Graph Analytics. https://www.oracle.com/a/ocom/docs/graph-database-use-cases-ebook.pdf
What Is a Graph Database? https://aws.amazon.com/nosql/graph
What is a Graph Database? https://neo4j.com/developer/graph-database/