Data analytics is the science of analyzing raw data to draw conclusions about it. The process involves examining extensive data sets to uncover hidden patterns, correlations, and other insights. With today’s technology, data analytics can go beyond traditional analysis, incorporating artificial intelligence (AI) and machine learning (ML) algorithms that help process information faster than manual methods.

Learn how a data fabric can add value by maximizing the value of a company’s data infrastructure.

XML (eXtensible Markup Language) and JSON (JavaScript Object Notation) are two of the most popular data interchange formats available.

The generative AI industry is changing fast. New models and technologies (Hello GPT-4o) are emerging regularly, each more advanced than the last. This rapid development cycle means that what was cutting-edge a year ago might now be considered outdated. The rate of change demands a culture of continuous learning and technological adaptation.

The dual-write problem occurs when two external systems must be updated in an atomic fashion. A classic example is updating an application’s database while pushing an event into a messaging system like Apache Kafka. If the database update succeeds but the write to Kafka fails, the system ends up in an inconsistent state. However, the dual-write problem isn’t unique to event-driven systems or Kafka. It occurs in many situations involving different technologies and architectures.

In Part 1 of “Retail Media’s Business Case for Data Clean Rooms,” we discussed how to (1) assess your data assets and (2) define your data structures and permissions. Once you have a plan on paper, you can begin sizing the data clean room opportunity for your business.

What do 82% of global executives say is crucial for AI success? MIT's latest findings highlight the importance of effective data integration.