Graph analysis models are powerful enablers of fine-grained predictive modeling of human behavior as they help in identifying the likely behavior of individuals in their fuller context of groups, relationships, and influence. These models offer microscopic and detailed views of the customer experience by focusing on human actions and interactions. Graph analysis has the potential of transforming a wide range of applications in the public, private, and research sectors. It’s developing rapidly into one of the most promising new segments in the Big Data market, and is the core application of various commercial and open source graph databases. Graph analysis is already being used across several industries in antifraud, influence analysis, sentiment monitoring, market segmentation, engagement optimization, experience optimization, and other applications where complex behavioral patterns in Big Data must be rapidly identified. It’s all in-memory, and massively parallel graph database architectures along with wide range of NoSQL databases can normalize Big Data by effectively discovering, correlating, and preprocessing behavioral data from every possible source.

Following is the process of getting started with graph analysis:

Standardizing on a Graph Analysis Language

Common programming languages can be used as one method of allowing for a more familiar interface with graph analysis and embedding it into standard applications. Standardizing on a graph analysis language will accelerate the sharing of data science discoveries and processing into operational applications. Multiple language options are available at this point for developing graph analysis such as Python, R, Scala, SPARQL, and so on. Organizations must evaluate these languages with their data science teams to determine their preference.

Implement Graph Databases

A graph is developed on the assumption that no data sets are related to each other. A graph database can be used to:

• Store outputs obtained from graph analysis and the modeling process
• Facilitate further analysis by senior analysts who do not have skilled data scientists
• Allow business process experts to review and offer commentary on the models that help determine alternative nodes, clusters, link weights, edges, and proximity

Formulate a Data Science Team or Lab

Organizations must establish a data science laboratory team or group. The responsibility of this data science team is to discover new methods in converting outputs of graph analysis into production.

By leveraging graph analytics, organizations can create a semantic set of relationships that are most relevant to them. Graph analysis will push Big Data evolution to the next plateau of scale and sophistication