Data Graph

The Graph Advantage

You’ve heard it before; the volume of data we generate and collect grows exponentially as you read this. This incredible influx of data has powered the major advances in AI and Big Data the past decade. It will continue to be the foundation of innovation as we move into the next decade.

In the digital age, companies of every size and scale need adequate data infrastructure to leverage their data assets. Graph databases are revolutionary in their flexibility. They can process and analyze mass amounts of complex and interrelated data (i.e. messy data).

A little history of relational databases

The original relational database was developed in the 1970s. It organized data into tables (rows and columns) in order to demonstrate the relationship between different data points. This worked well at the time because information volumes grew slowly. However, as we moved into the digital age, there was a need for establishing relationships between different tables. This job became too complex and slow for any data analytics team. So, graph databases were developed to address the limitations of relational databases.

How graph works

A knowledge graph is a database that stores information in a visual format and generates a graphical representation of the relationships between multiple data sets and points. Graph databases work by processing units of data (Graphics Processing Units or GPUs) to create sophisticated, 3D graphics. In the database, information is broken down into “meshes” made up of triangles (sometimes millions or billions of them). The points of intersection (nodes) and the lines (edges) are assigned properties and are linked together to show the relationship between data points. Each node represents a concept and each edge represents a relationship.

Graph databases are “flexible” because they are not one-size-fits-all. They operate uniquely based on the information they are dealing with. Because they can analyze numerical data and diverse data like audio, video, picture, and social media metrics, they have an extremely wide range of applications. A company’s data assets greatly increase when diverse data can be analyzed. Graph databases can find in-depth insights into markets and customers that would not be found by other analytics methods. In business terms, a graph database will give you the best understanding of your consumer base in order to develop a competitive advantage.

Examples of graph applications across different industries

  • Inventory/supply chain logistics: Graph databases allow the manufacture to understand the crossover between materials and products. This optimizes the system by making it more efficient and sustainable.

  • Financial services industry: Fin-tech is a hot topic right now. Graph databases are gaining popularity in the financial sector for their real-time compliance abilities. The money moving around in the database are essentially the data points. Therefore, graph allows financial institutions to identify and fraudulent activities, as well as perform superior investment analytics.

  • Resource management systems: graph databases can streamline operations and make electric power grids, water distribution systems and traffic management systems more effective.

  • Social media: Data=opportunity. Every social media platform has become a goldmine of insights on consumer behavior- you just must have the right analytical tools to find these patterns.

  • Retail, e-commerce, consumer goods/services: Recommendation Engines are how Amazon delivers superior customer experience.

  • Other tech giants like Google, Netflix, and Facebook rely on graph to keep track of the massive networks of data they deal with and to paint a picture of their users likes bases on searches, likes, follows, etc…

As enterprises and industries become increasingly modern, graph databases will become standardized, offering superior data solutions for companies that want to leverage graph’s flexibility and performance to create competitive advantages. Some experts are suggesting that graph databases will even replace the relational market by 2030.  Graph technologies will continue to evolve, becoming more widely available and reinventing data management and analytics.