Today, the world’s leading digital enterprises (such as Google, Amazon, and Netflix)  have combined to contribute to the largest repository of information in history. 

Facebook, for example, has nearly three billion unique accounts in its system, making it the largest ‘country’ in the world. But with such high volumes comes the need for better ways to collect, store, and analyze all this data. It makes sense then that this new challenge requires new solutions. 

Graph database technology is a fairly new segment in the field of databases. But it goes well beyond data management and application development- it is a re-imagining of how data works in relation to one another.

A New Evolution in Connecting Data

New database demands require higher speeds in performance and flexibility. Current relational databases work best for problems that already have tabular data, an organized structure, and a defined schema. Because of this rigidity, developers have to create their applications to structure data accordingly. 

This becomes a challenge when the pool size of the data becomes too big and complex. When exploring certain relationships in data, multiple queries in such a rigid structure will lead to slow down and bottlenecks. 

To illustrate this, we can use an Employee to Department example. If you would want to connect an Employee’s ID in a table to their Department ID in a separate table, you would need to produce a Join that has foreign keys that could connect both IDs to one another. To connect even more data points with each other, you would need a Join table that holds more foreign keys. You must also know the exact values for both the Person and the Department IDs to know which person is actually connected to which department. These costly Join operations are often solved by denormalizing the values further, but this can lead to a loss in data integrity.

The graph database prioritizes and stores relationships that connect data points to one another. But instead of just establishing relationships between data points in separate tables, each data model, called a node, can have its own set of relationships with other nodes. 

When you run an operation, the graph database will use the list of relationships contained in each node to establish the appropriate connections in the graph. The nodes will then connect with one another through multiple data points to create a visual representation of the database.

Instead of relying on keys to match an Employee to their Employee or Department ID in different tables, a graph database would present a node containing an Employee’s list of relationships to various Department nodes. To find out which departments a person belongs to, a simple search of belongs_to relationships within the Employee’s node will present the department nodes it is connected to.

This level of precision and speed allows graph databases to process and analyze data in real-time. It also allows for multiple relationships between nodes to persist, leading to better data integrity.

Neo4j and the Uses of Graph Databases

Neo4j is a database management system that utilizes a graph search framework to traverse databases and answer queries in a faster, more efficient manner. It creates and maintains data connections in a logical fashion while lessening translation friction between developers and executives- those involved in sales teams can easily explain its uses through visual examples, while developers can meet application requirements much faster due to its ease of use.

Neo4j technology has enabled leading enterprises to transform data into tangible solutions.

Real-Time Product Recommendations

In highly competitive industries such as retail and e-commerce, product placement and customer recommendations play a large role in increasing visibility and probability of purchase. Using data collected from transactions and customer feedback, a company can create a system that surfaces relevant products to customers in real time. 

Walmart used Neo4j to optimize its online shopping experience by constructing a real-time recommendation engine. It was designed to create highly personalized product recommendations while instantaneously offering suggestions for one-size-fits-all product queries. 

For the engine’s foundation, Walmart built a database that could connect huge amounts of complex buyer and product data to create insights into customer behavior and trends. The graph database also allows them to quickly search a customer’s past purchases and combine this with any current online visits and searches to create real-time recommendations.

By using Neo4j, Walmart now has a keener sense of what their online customers prefer to see and buy online. They are also able to optimize the up-sell and cross-sell of major product lines. These changes ensure that the company sustains its competitive advantage over other online retailers.

Identity and Access Management

Expanding enterprise networks and multiple accounts originating from single users- these are just some of the daily issues that revolve around Identity and Access Management (IAM) amongst IT professionals. It is through IAM solutions that organizations can manage the identification, access, and control of resources for individuals, business units, and departments. 

Telenor Group is a mobile network services firm. Their business revolves around creating self-service account management systems.  Due to performance and response issues with their older IAM platforms, they had to develop a new system that would be more adequate for the needs of their customers.

Neo4j’s native graph query engine can be used to handle changes to an entire organization automatically; it can even build or rebuild entire directories for personnel or asset management or create complex access and control structures.

The new application can execute a complex identity and access management system that assigns service privileges to a client’s whole organization. With a graph database IAM solution powered by Neo4j, Telenor was able to deliver better performance, scalability, and improved maintenance for its self-service portals.

Content and Asset Management

Neo4j was created due to a need for a better content management system. The goal was for the new system to recommend photos based on unique, but related search terms. These parameters actually lead to much better-related search results, and during the testing phases, lead to increased engagement for the new platform.

The principles behind this initial version of Neo4j could be used for search and data-intensive processes found in content and asset management. Lufthansa Systems, a technology company under the airline Lufthansa, developed an in-flight digital asset management system specifically for their commercial airlines.  They use this system to keep track of the digital content being delivered across the fleet, which includes managing the hardware being used, the content licensing, rules, and much more. Due to the requirements for an advanced system that could evolve over time, the graph database used in this system has the operational flexibility to efficiently receive, store, and use Lufthansa’s data while maintaining high performance.

So How Can Graph Databases Help My Organization?

The advantages of using graph databases like Neo4j have to lead to new and flexible solutions. If you are looking for a database framework that could help you navigate through a modern data challenge, Amihan can help and provide insight into how Neo4j can work for you. Simply fill up the form below and we will get in touch.