Graph analytics is a set of analytic techniques that allows for the exploration of relationships between entities of interest such as organizations, people and transactions. One application of graph analytics — graph-enabled semantic knowledge graphs — forms the foundation of many NLP/conversational interfaces and data fabrics, and enriches and accelerates data preparation and data science.

The application of graph processing and graph databases will grow at 100% annually through 2022 to continuously accelerate data preparation and enable more-complex and adaptive data science.

Gartner Predicts

What Does Graph Analytics Enable?

  • Graph analysis shows how tightly several trends or data points are related to each other.
  • Graph models determine “connectedness” across data points and create clusters based on levels of influence, frequency of interaction and probability. Once highly complex models are developed and trained, the output is easier to store because of the expanded capabilities, computational power and adoption of graph databases. The user can interact directly with the graph elements to find insights, and the analytic results, and output can also be stored for repeated use in a graph database.
  • Graph databases therefore present an ideal framework for storing, manipulating and analyzing graph models.
  • Generating a dynamic graph about how different entities of interest — people, places and things — are related, instead of more-static relational schemes, enables deeper insights that are closer to human knowledge representation.
  • Graph technology underpins the creation of richer semantic graphs or knowledge bases that can enhance augmented analytics models, as well as the richness of conversational analytics. 

How Does This Impact Your Organization and Skills?

  • New skills including graph-specific standards, graph databases, technologies and languages such as Resource Description Framework (RDF), SPARQL Protocol and RDF Query Language (SPARQL); and emerging languages, such as Apache TinkerPop or the recently open-sourced Cypher. 

  • Commercialization of graph analytics is still at an early stage, with a small number of emerging players.

  • While current technologies still require specialist skills, SQL-to-graph interpreters are emerging. These interpreters convert graph-based procedures into disaggregated procedural SQL (and back again). These capabilities are helping to make graph technologies compatible with existing datasets

We've got you covered!

Relevant Sessions

  • Graph Techniques — an Old Revolutionary Concept for Modern Data Science
  • Boosting Machine Learning with Better Training Data
  • Magic Quadrant for Analytics and BI and Data Science and ML
  • Myths and Pitfalls of Artificial Intelligence and How to Navigate Them

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