解决方案提供者如何利用人工智慧来改造其企业资料结构平台

本报告探讨了解决方案提供者如何利用人工智慧转变其企业资料结构平台，提供了有关应纳入哪些人工智慧功能以推动业务利益的指导，审查了人工智慧工具和人工智慧解决方案，并介绍了人工智慧资料管理解决方案提供者。

实际好处：

• 确定需要纳入您的资料管理解决方案的 AI 解决方案，以便为您的公司带来业务效益
• 查看 AI 资料管理解决方案提供者的简介以及他们的价值主张和重点关注应用
• 更了解企业资料结构解决方案的发展以及维持相关性所需的条件

重要问题的答案：

• 目前资料管理解决方案提供者的竞争优势是什么？
• 资料管理解决方案提供者需要具备哪些关键特征来支援企业资料结构平台？
• 资料管理解决方案提供者如何在企业资料结构市场中发现机会？

研究亮点：

• 透过实施独特的 AI 工具来确定如何赢得客户，从而让您脱颖而出
• 确定资料管理解决方案的基本AI功能以及特定于资料货币化的AI功能
• 了解整个企业的常见业务用例以及如何使用基于 AI 的资料管理解决方案来解决这些用例

目录

主要发现

重大预测

主要公司

利用人工智慧增强和区分企业资料结构产品

解决企业资料结构的企业用例

概述

Actionable Benefits:

• Identify Artificial Intelligence (AI) solutions that need to be embedded into data management solutions for better enterprise business benefits.
• Review profiles of AI-infused data management solution providers with value proposition and key focus applications.
• Gain a better understanding of the evolution of enterprise data fabric solutions and what needs to be done to remain relevant.

Critical Questions Answered:

• What are the competitive advantages of current data management solution providers?
• What are key characteristics that data management solution providers must have to support enterprise data fabric platforms?
• How can data management solution providers identify opportunities in the enterprise data fabric market?

Research Highlights:

• Identify how to win customers by implementing unique AI tool differentiators.
• Identify characteristics between basic AI capabilities and data monetization-specific AI capabilities for data management solutions.
• Understand business use cases that are common across enterprises and how to address these use cases using AI-based data management solutions.

Who Should Read This?

• Product managers at data management solution providers looking to identify key competitive differentiators
• Data management product engineers tasked with integrating new AI features into a data management solution to drive new customer acquisition.
• Large business application solution providers trying to strengthen their data management value proposition by identifying top data management vendors.

TABLE OF CONTENTS

Key Findings

Key Forecasts

Key Companies

Enhancing and Differentiating Enterprise Data Fabric Offering with AI

Solving Enterprise Use Cases for Enterprise Data Fabric

Final Thoughts