Unlock insights "trapped" in rows and columns

Graph Data Science is an evolving data analytics and machine learning field that helps you understand the connections in big data to answer critical questions and improve predictions.

Answer business critical questions and make predictions

Rather than looking at row or column headers, graphs focus on data relationships and make your data more connected, thus making access faster and more optimal.

Accelerate your path from proof of concept to production

Easily integrate with your apps and scale your analysis across hundreds of billions of nodes and relationships.

Understand relationships and improve your insights

Graphs are a more natural, connected way to look at and analyze data for deeper context and unearthing hidden patterns and insights.

_ Data privacy

Drastically improve Data Privacy, Risk and Compliance

Visualise private data and control its use while internal staff members provide fast answers to privacy inquiries. Enable privacy managers to trace data flows, investigate potential breaches, and prove compliance to regulators. Without graph technology, it’s almost impossible to understand the full lifecycle of personal data. The visualisation, data lineage, connected data analysis and pattern detection make perfect use cases for graph technology.

_ AI Recommendations

Power more accurate recommendations in real time

Increase revenue with real-time recommendation engines, a key to the success of any online business. To make relevant recommendations in real time requires the ability to correlate product, customer, inventory, supplier, logistics and even social sentiment data. Moreover, a real-time recommendation engine requires the ability to instantly capture any new interests shown in the customer’s current visit.

_ Knowledge graphs

Enrich your data for complex decision-making with knowledge graphs

Create your knowledge graph, an insight layer of interconnected data enriched with semantics, so you can reason with the underlying data and use it confidently. A knowledge graph gets richer as new data is added. Through a combination of data, graph, and semantics (meaning), you get a knowledge graph with deep, dynamic context.

_ Case study: Profile builder

An IoT anomaly detection platform

Squaredev worked with Exus to create a platform that, when installed in an IoT network, can detect any network anomaly targeting the connected devices. It acts as a security layer on the IoT network, protecting it from attacks with smart alerting. The solution is based on graph data science algorithms and models developed by Squaredev.