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Data Architecture Principles

Data Architecture best practices have evolved over time as data sources and data usage have expanded exponentially. Here are a few key modern Data Architecture principles for today's data-driven marketplace

Data Stewardship: Managing a Valuable Asset

Data stewardship ensures data is being used in a way that generates business value while maintaining compliance with regulatory standards and organizational policies.

Conversational AI and Analytics: Contextualizing Communication

For many organizations, the true value of this technology is in the data it creates. Here’s how organizations use conversational AI data to accelerate innovation and growth.

Data Exploration: The First Step in Data Analysis

Data exploration, where data analysis begins, allows data teams to better understand a dataset they’re working with, enabling advanced data analytics.

Generative AI in Healthcare

In this article, we’ll explore what generative AI is and share use cases that demonstrate the power of generative AI in healthcare.

The Role of AI in Insurance

Get an in-depth view on what is fueling the rise of AI in insurance and see examples of insurers leveraging AI for efficiency, risk management, and more.

Use Cases of AI in Government

From streamlining bureaucratic tasks to improving citizen safety, the use of AI in government is opening the way for more efficient, transparent and responsive services. Learn more here.

AI in Finance: How Generative AI and Large Language Models Can Be Applied to Financial Services Workflows

In this article, we’ll examine seven use cases that illustrate the remarkable ways in which artificial intelligence is advancing the finance industry.

Why Data Scientists Choose Python for Machine Learning and Artificial Intelligence

Many data scientists rely on Python for machine learning (ML), artificial intelligence (AI), and data modeling projects.