解決方案提供者如何利用人工智慧來改造其企業資料結構平台

本報告探討了解決方案提供者如何利用人工智慧轉變其企業資料結構平台，提供了有關應納入哪些人工智慧功能以推動業務利益的指導，審查了人工智慧工具和人工智慧解決方案，並介紹了人工智慧資料管理解決方案提供者。

實際好處：

• 確定需要納入您的資料管理解決方案的 AI 解決方案，以便為您的公司帶來業務效益
• 查看 AI 資料管理解決方案提供者的簡介以及他們的價值主張和重點關注應用
• 更了解企業資料結構解決方案的發展以及維持相關性所需的條件

重要問題的答案：

• 目前資料管理解決方案提供者的競爭優勢是什麼？
• 資料管理解決方案提供者需要具備哪些關鍵特徵來支援企業資料結構平台？
• 資料管理解決方案提供者如何在企業資料結構市場中發現機會？

研究亮點：

• 透過實施獨特的 AI 工具來確定如何贏得客戶，從而讓您脫穎而出
• 確定資料管理解決方案的基本AI功能以及特定於資料貨幣化的AI功能
• 了解整個企業的常見業務用例以及如何使用基於 AI 的資料管理解決方案來解決這些用例

目錄

主要發現

重大預測

主要公司

利用人工智慧增強和區分企業資料結構產品

解決企業資料結構的企業用例

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