Auteur: Neo4j

4 Business Advantages of Graph Databases

 

Over the past decade, the data landscape has shifted significantly. Today's data and applications demand higher performance, agility, speed, and interconnectivity than ever before.

The “interconnectivity” piece of that puzzle has the potential to take businesses to new levels of innovation and growth. ECommerce businesses, for instance, rely on the connections between customers' profiles and their behaviors/interests to make relevant product recommendations.

And it goes beyond real-time retail suggestions. Pfizer leverages data connections to manage the logistics behind billions of vaccines. CSX operates a transportation system at a massive scale by interconnecting its data. The Shopify team uncovers merchant fraud across its global eCommerce platform by understanding the relationships in their data. And the list goes on.

But many businesses struggle to facilitate this level of connectivity with traditional relational databases (RDBMS). Because today’s data constantly proliferates in volume and complexity, RDBMS can’t keep up. Their rigid structures can’t adapt to modern-day data with its deep context and interconnections.

Graph database technology is a better approach to overcoming these evolving data challenges. It’s a more intuitive, rapid-paced, and robust way to uncover connections that already exist within your data.

What Is a Graph Database?

You don’t need to understand the mathematical wizardry of graph theory to understand graph database technology. Graphs are actually more intuitive to understand than relational databases.

A graph is composed of two elements: a node and a relationship.

Each node represents an entity (a person, place, thing, category, or other pieces of data). Each relationship represents how the nodes are associated. Social media channels like Twitter use graph database technology to connect hundreds of millions of monthly active users.

The illustration below represents three Twitter users in a graph database. Each node (labeled User) belongs to a single person and uses relationships to describe how each user is connected. As we see below, Peter and Emil follow each other, as do Emil and Johan. We also see that Johan follows Peter, but Peter hasn’t (yet) reciprocated.

 

User follows

If this example makes sense to you, then you’ve already grasped the basics of graph technology.

Graph Database Technology Vs. RDBMS Technology

Graph database technology is distinctly different from traditional RDBMS technology.

RDBMS technology uses columns and rows to represent data relationships. To connect the dots between related data, relational databases must integrate numerous siloed and distributed datasets with SQL queries. This code becomes complex, difficult to manage, and computationally heavy as the relational database processes millions of recursive traversals and joins.

Ultimately, real-world data doesn’t exist as columns and rows. Humans don’t intuitively think about data relationships in this way, either. When we take in new information, we instinctually map out the data in relation to what we already know. This is the approach that graph databases take: querying relationships as nodes and relationships, rather than columns and rows.

It’s comparable to a detective's investigation board, with clues as nodes and red strings connecting them. Just as detectives use graph theory to uncover hidden insights, so can enterprises! In fact, it’s how ICIJ revealed the financial secrets of the most corrupted global elites in the Pandora Papers and the Panama Papers.

Shutterstock

A detective's investigation board (Fer Gregory, Shutterstock)

When Should You Use Graph Database Technology?

Graph database technology is specifically designed and optimized for highly interconnected datasets. Rather than operating within the bounds of columns and rows, it uses graph storage and infrastructure to find neighboring data efficiently.

Some of the most common and impactful graph use cases include:

Fraud Detection & Analytics: Real-time analysis of data relationships is essential to uncovering fraud rings and other sophisticated scams.

Artificial Intelligence & Machine Learning: Harnessing context within data improves predictions within existing network structures, giving AI providers a competitive advantage.

Real-Time Recommendation Engines: Graph-powered recommendation engines help companies personalize products, content, and services by building a contextualized map of offers with historical and real-time data.

Knowledge Graphs: Knowledge graphs tap into your organization’s institutional memory, making it fast and simple for business units to find the correct information at the right time.

Network & Database Infrastructure Monitoring: Graph databases rapidly make sense of complex interdependencies central to managing networks, data centers, cybersecurity, and IT infrastructure.

Master Data Management (MDM): The schema-optional graph database model allows you to organize and manage your master data with flexibility. It also lets you harness real-time insights and a 360° view of your customers, products, and employees.

At their core, all of these use cases require solving complex problems with dynamic and interconnected datasets.

Advantages of Using Graph Database Technology

When you’re on your own, new tech might be fun to play around with or to use on a personal side project. But when you’re at work, it’s a whole different story. You must operate in a world of budgets, timelines, corporate standards, and competitors. And in that world, the only test for new tech is that it better work damn well (and way better than anything else you already have).

Graph databases fit that bill, because they offer:

1. Simple and natural data modeling Graph databases provide flexibility for data modeling, depending on relationship types. Since the graph model has no inherent rules, graph data stores add as much or as little semantic meaning as the domain requires. You can do this without constraints like normalization or restructuring of the data using denormalization. The intuitive data model also makes it easy for technical and business users to understand.

2. Performance and scalability Graph databases scale with your ever-growing data volume. Even when your big data grows exponentially, native graph processing with index-free adjacency minimizes your cost and hardware requirements while maximizing performance across connected datasets.

3. Flexibility for evolving data structures Graph technology provides flexible schema evolution. In a constantly changing business environment, you need the option to add or drop data entities or relationships and extend or modify your data model. Graph databases allow for evolving data structures that match today’s agile development environments.

4. Better, faster, and more powerful querying and analytics Graph data stores provide superior query performance with connected data using native storage and native indexed data structure. Connections in property graphs enable you to illustrate and traverse many relationships and find context for your next breakthrough application or analysis.

Why You Should Care About Graph Database Technology

Early on, a handful of companies tapped into the power of graph technology as a business driver. These early adopters include Google, Facebook, LinkedIn, and Microsoft, as well as upstarts like Airbnb and Uber. Well-established entities such as NASA, eBay, UBS, and many of their peers use graph technology to improve customer experiences and increase their competitiveness.

Today, graph-powered applications are used by more than 75% of the Fortune 500, including:

● 7 of the world’s top 10 retailers ● 3 of the top 5 aircraft manufacturers

● 8 of the top 10 insurance companies

● All of North America’s top 20 banks

● 8 of the top 10 automakers

● 3 of the world’s top 5 hotels

● 7 of the top 10 telcos

These successes strongly indicate graph technology’s impact on a business’s innovation and bottom line.

How Can YOUR Business Use Graph Technology?

The real world is richly interconnected, and graph databases aim to mimic those sometimes-consistent/sometimes-erratic relationships intuitively. That makes the graph paradigm unique: It maps relationships like the human brain maps and processes the world around it.

By adopting graph databases, organizations achieve the next-level performance, agility, speed, and interconnectivity that modern application development demands.

Graph database technology can change the game by uncovering the existing relationships in your data. Even if you’re just a beginner, it’s never too late to start.

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