Evolution of network analytics

Representing data as real world events, entities and networks.

There has been a paradigm shift over the last decade in the application of analytics: it is not the algorithms that have progressed, but rather the preparation of the underlying data.

Traditionally, statistics have focused on “observations” or “events”. However, these do not represent the richness of relationships between the data. Network analytics provides a whole new dimension to feature engineering to improve AI techniques.

However, the evolution does not stop with the data representation, in the modern online, instant gratification society, real time decisions are considered the norm.

The idea that a batch process must run to react to a dynamic event is no longer acceptable.

Furthermore, regulation and data privacy now requires more granular “row level” security in some instances or the pseudo-anonymisation of data. This presents its own challenges, especially when it is important to be able to match data at varying levels of strictness, including fuzzy matching.

Quantexa has focused on solving these challenges to enable organisations to get the most out of their data in modern data environments where data scientists expect to use any number of analytical tools at their disposal from Open Source to commercial products.

Quantexa Network Analytics Flow

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