Introduction

Every business aggregates and analyzes data. The market is flooded with tools designed to help organizations make sense of the information they have amassed, while people with advanced data skills remain in high demand across industries.

To drive continued success and growth in the modern digital economy, organizations must empower their teams with advanced tools that can derive actionable insights at scale.

This imperative is especially important in fraud management. The ability to effectively leverage large volumes of data to power proactive detection and prevention strategies can determine whether an organization succeeds or fails against fast-evolving fraud attacks.

Centralized Data vs. Centralized Intelligence: What's the Difference?

When discussing big data and deriving actionable insights at scale, we are talking about the evolution of digital intelligence.

As the world began producing greater volumes of data, organizations developed ways to collect, process, and analyze it. They then expanded their ability to visualize that data through charts, graphs, and other reporting tools. To harness this power at the enterprise level, businesses learned to centralize data so it could benefit the entire organization.

In the age of AI and machine learning, intelligence has become significantly more sophisticated, creating an important distinction between centralized data and centralized intelligence.

With centralized data, teams are still responsible for interpreting information, visualizing patterns, identifying trends, and deciding what those trends mean. Centralized intelligence goes beyond visualization by using predictive power to produce actionable insights.

With centralized intelligence, machine learning systems can identify patterns, move through the decision process, and recommend what should happen next. Most importantly, that actionable intelligence resides within a shared system where it can be used across the organization.

Centralizing Intelligence with DataVisor's Feature Platform