Top Use Cases for Graphs

Graph databases are becoming more and more popular. According to Gartner, graphs and graph technology should be experiencing dramatic growth until 2025. Still, one type of database cannot be the best possible solution for all problems. Other types of databases (tabular, relational, etc.) still have their place in today's world. However, sometimes they are simply and overwhelmingly outperformed by their graph relatives. Let's walk through some of the strongest, most common use cases for graphs and graph databases, and touch upon how solutions such as GraphAware's Hume - a no code graph-powered insight engine built on top of Neo4j - can help with data management and analysis when it comes to these.

Knowledge Graphs?

One of the top use cases for graphs is creating Knowledge Graphs. Simply put, Knowledge Graphs are collections of nodes and relationships representing your data enriched by semantics. It is much easier to surface insights and gain knowledge from data in this form. Knowledge Graphs allow you to store all your organization's knowledge in one place. Thus, you can connect your data silos and analyze your data more easily and quickly. Not to mention collaborating with others is much easier.

Knowledge Graphs are frequently compared to human brains, KGs representing organizations' knowledge bases are called organizations' second brains, or single sources of truth. This is because the reasoning used in KGs is very similar to the one humans use. The nodes are connected by relationships - creating a complex, densely connected structure that mirrors the reality of our world. We are native to this structure and the reasoning behind it. That's where its power lies - no endless tables that have to be looked through row by row, table by table, to get the information you want, no endless documents that have to be read before understanding some of the relationships among the data.?

Knowledge Graphs are fast and intuitive; they can hold large amounts of data, leverage the power of graph algorithms, and when visualized, you can easily and quickly perform enhanced analysis on your data. KGs make it easy to leverage your own reasoning capabilities in combination with graph algorithms, thus augmenting your intelligence.?

Fraud Detection

Fraud has many shapes and forms - identity theft, credit card fraud, phishing, account takeovers, and much more. The good news is that all these different kinds of fraud can be fought and detected with graphs. We recently hosted a webinar where we looked at fraud detection and demoed how a credit card fraud investigation could look like in a graph-powered insights engine like Hume.?

In short, graphs allow you to connect your data into a single source of truth and query your database quickly. This alone has the potential to let data analysts and investigators identify patterns and surface insights more quickly.?

Hume - a graph-powered insight engine serving as a bridge between your graph database and the end-user takes this a leap further. It allows you to stay on top of the developments in your data, leverage natural language and unstructured data processing capabilities, see the data in a temporal and geospatial context and identify patterns and complex connections - all of which can be done without coding. You know what I am hinting at - time savings - immense time savings. And time savings in fraud detection = catching fraudsters faster, preventing fraud, and protecting people from being taken advantage of.?

Recommendation Engines

Recommendation engines are everywhere. From search engines suggesting what information you might be interested in based on your searches, through online stores suggesting what to pair your new jeans with, to music and video streaming platforms serving you with recommendations for music, podcasts, movies, series, and more. Website sections titled People also bought, You may also like, Style with, and Suggested for You are best produced with graph solutions.?

Recommendation engines need to be able to learn - thus leveraging machine learning capabilities, and they need to be able to serve recommendations quickly - within milliseconds. Recommendation engines do not have much value if they provide you with suggestions that you are not interested in - however, you are more likely to simply ignore these. But imagine a recommendation engine that would take a whole minute before providing you with a suggestion. You'd be long gone, maybe surfing a different site. Not exactly something you want to see if it's your business that is missing out on the chance to sell more products or services to your customers.?

Graphs are an excellent solution for recommendation engines as they can store a lot of densely connected data, the time to query is short, and in this case, they are a superb match for Machine Learning projects.?

Law Enforcement

Criminal investigations, while different from fraud detection, are an ideal use case for graphs because of the same reasons. Whether you're fighting human trafficking, cybercrime, or money launderers, you need to connect large sets of distributed data, and identify patterns within them. This is very easy to do with graphs.?

Additionally, Hume lets you analyze data from different sources and in various formats (both structured and unstructured), stay on top of the developments in your data thanks to automated alerting, and leverage geospatial and temporal context of your data. Allowing even more patterns, suspicious activities, and connections to surface. Role-based access controls keep the data safe and protected while making collaboration easy.?

Supply Chain and Manufacturing

Supply chains are by nature very complex. And the organizations' supply chains are not the only relevant supply chains - customers', partners', and even competitors' supply chains are often all of interest and relevance to the focal company. Together, these supply chains create a complex supply chain ecosystem that is highly interconnected and hard to keep track of. Similarly, products and services have many different components, which may come from multiple sources. The networks of partners, suppliers, competitors, customers, substances, and other elements, can be very well represented in a graph.?

Knowledge Graphs can be helpful in managing these networks and ecosystems, including the relationships within them. Graphs can depict the complexities of these networks in a relatively simple manner. Together with graph algorithms, graphs can easily show you which dependencies to keep in mind, which elements can be replaced by others, and - if you're using Hume - you can also leverage temporal and spatial aspects of the insight engine and improve and simplify your organization and contingency planning.?

Knowledge Research

Research commonly includes connecting dispersed data sources, extracting entities and keywords from unstructured data, and grouping documents based on their contents. All of this then has to be put into a perspective - one has to understand the connections, relationships, dependencies, and patterns among the multitude of concepts and extracted entities. Knowledge research is thus another excellent use case for graphs as they are the perfect structure to represent large amounts of connected data.?

Take an example of drug repurposing - you can ingest your data in the form of drugs and the elements they consist of and depict the different effects and side effects of different substances into a knowledge graph. This could allow you to create new drugs specifically made for people who have specific allergies to medical substances or take other medications which could cause cross-reactions and have negative side effects.?

Master Data Management

Enterprise data is often unstructured and fragmented, making it hard to stay on top of changes and manage it well. When stored in a graph, managing this data is much easier. The data can be connected into a single source of truth - the second brain of your organization, and accessed, retrieved, and managed quickly and with ease.

The first step to managing your data is having it in one place - connected, integrated, and at your fingertips. The second step is to be able to search through it, retrieve it, analyze it, and more. With graphs, you are able to connect the data from different sources and analyze it no matter its original form and structure. Moreover, it's easy to search in your data set, merge duplicate documents, and keep your data up to date - which is especially useful for ensuring organisational compliance with ever-changing laws and regulations.?

Customer 360

Finally, marketers and organisations can leverage graphs to gain a 360 degree view of their customers. The information including all the interactions the customers had with the company, all the purchases they made, as well as additional information about the customers like their demographics, psychographics, and buying habits can be stored in a graph. KGs make it possible to easily analyze the data and spot patterns within it.?

Being able to leverage this information allows companies to target the right customers with the right offering, and the right message, at the right time. Thus creating additional value for customers and gaining a competitive edge through the application of graphs into their operations. And that's what GraphAware is all about ;)?