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Data Science and Analtyics

TRANSFORMING DATA INTO ACTIONABLE INTELLIGENCE

The data scientists, engineers, and cybersecurity professionals at G2 bring expert knowledge of mathematics and computer science to bear on our customers' toughest problems, providing timely threat detection and innovative solutions that inform and protect our clients' most prized resources.

DATA DISCOVERY AND FRESH PERSPECTIVES

Our data scientists perform data integration and preparation to set the stage for lightning-speed data discovery, machine learning, in-depth forensic analysis, and model building. At G2, we provide statistical analytics to enable machine learning and pattern recognition in complex datasets, and provide the tools for analysts to perform filtering, aggregation, and visualization. We extract the value out of data, putting it at the fingertips of decision makers at every step of the way.

STATISTICAL AND GRAPH MODELING, AND MACHINE LEARNING

We build and select statistical and machine learning models to provide actionable insight through forensic analysis. Batch analytics based on data characteristics, statistical descriptors, and appropriately chosen machine learning algorithms, allow automated detection of potential network hazards. Using appropriate data modeling, scalable to big data through advances in indexing, our graph models allow custom queries and pivots, and unique pattern identification and visualization. Our anomaly detection, using machine learning and principal component analysis, exposes unusual behavior by machines and humans, both on- and off-network.

STREAMING ANALTYICS

G2 created a streaming analytics framework that supports analytics on streaming network data, thereby accelerating threat detection; allowing near-real-time, split-second machine decision making; and generating immediate alerting. Streaming analytics utilize models built from historical data to assess information as soon as it is received. Machines take temporary actions on-the-fly and send appropriate messages to those humans and machines that need to know. By sending data where it needs to go, analytics at the edge enable model updating, and data enrichment and integration. By alerting those who need to know, streaming analytics provide near-real-time protection and detection to mitigate costs of all types.