2023-05-29-Gartner-2023中国数据分析和AI重要趋势_56页_3mb

报告摘要

Gartner Webinars Summary

Core Content Overview

Gartner Webinars provide actionable insights, objective guidance, and tools to enhance organizational performance in data and analytics (D&A) initiatives. The webinars focus on three main themes: "Think Like a Business", "From Platforms to Ecosystems", and "Don't Forget the Humans". These themes guide organizations in leveraging D&A for business value, building flexible and interconnected systems, and ensuring human-centric decision-making.

Main Themes and Key Points

Theme 1 — Think Like a Business

Core Concepts:

D&A should be viewed as a business function, not just a supporting one.
Organizations should proactively manage D&A as a product to be "sold" internally.
Balancing cost and value is essential for successful D&A initiatives.

Key Initiatives:

Value Optimization: Link D&A projects to business outcomes and optimize resource allocation.
Data Sharing Is Essential: Facilitate cross-functional data sharing to drive innovation and efficiency.
Observability: Ensure transparency and visibility across D&A processes.
Practical Data Fabric: Implement data fabric to support integrated, flexible, and reusable data architectures.
Converged and Composable Ecosystems: Build modular, adaptable systems that can be combined for various use cases.

Challenges:

AI risk management must be integrated into the D&A lifecycle.
Governance maturity is a key prerequisite for data mesh implementation, with only 18% of organizations reporting mature governance.

Case Study: JPMorgan Chase

Implemented a Data Mesh approach by defining data products and aligning technology with these products.
Benefits included empowering data owners, enforcing decisions through data sharing, and improving visibility across the enterprise.

Theme 2 — From Platforms to Ecosystems

Core Concepts:

D&A ecosystems are more than just technical platforms; they encompass broader organizational and societal connectivity.
Integration of D&A into the broader organizational ecosystem enhances value creation and reduces costs.

Key Initiatives:

Practical Data Fabric: Focus on business use cases and metadata to identify opportunities.
Data Sharing: Enable seamless sharing of data and analytics products across the organization.
Converged and Composable Ecosystems: Leverage modular components to create flexible and scalable solutions.
Emergent AI: AI systems are becoming more complex and capable with less data, requiring strategic integration and change management.
Consumers Become Creators: Users are increasingly involved in creating and using analytics, necessitating trust-building and user engagement strategies.

Additional Insights:

Data Fabric Maturity: As of June 2022, data fabric is at the Emerging stage, with Transformational potential.
Data Mesh Prerequisites: Organizations must have a mature D&A governance framework to implement data mesh effectively.

Theme 3 — Don't Forget the Humans

Core Concepts:

Human judgment remains critical in decision-making, even with the rise of AI.
D&A must be designed with user accessibility, trust, and relevance in mind.

Key Initiatives:

Enable Decision Automation: Use AI to support, not replace, human decision-making.
Master Prompt Engineering: Develop skills to effectively use generative AI tools.
Build Trust in Black-Box Models: Ensure transparency and accountability in AI systems.
Consumer-Centric Approach: Encourage users to engage with D&A tools and become creators of value.

Examples:

Generative Data and Analytics: Expected to account for 20% of large enterprise D&A spend by 2026.
ChatGPT and LLMs: Highlight the importance of prompt engineering and model fine-tuning skills.
Data Literacy: Focus on improving understanding of data, not just behavior, to drive better utilization.

Key Trends and Technologies

Collaborative Analytics: Encourages teamwork and shared insights.
Data Fabric: A converged and composable ecosystem that supports integrated data management.
Data Mesh: An emerging approach where domain teams manage data as a product.
AI Governance: Ensuring ethical, secure, and responsible use of AI.
Value Stream Mapping: Helps identify and optimize key decision-making activities.
Emergent AI: AI that can handle complex tasks with less data, requiring new skills and processes.
Observability: Ensures visibility and transparency in D&A systems.
D&A Sustainability: Focus on long-term value and ethical implications of data usage.

Gartner Insights and Resources

2023 China D&A Trends: Highlights the shift from traditional platforms to ecosystems and the importance of human involvement.
2023 Data & Analytics Technology Adoption Roadmap: Provides insights on the adoption timeline, risk perception, and value of 38 emerging D&A technologies.
2023 CIO Agenda: Offers four strategies for maximizing digital ROI through effective technology investment.
2023 Data & Analytics Strategy Roadmap: Outlines five stages for optimizing D&A strategies to drive digital growth.
Gartner Webinars and Events: Includes the Data & Analytics Summit and other sessions to help leaders build and execute world-class D&A strategies.

Summary of Key Takeaways

D&A must be treated as a business function, not just a technical one.
Data Fabric and Data Mesh are key technologies for creating flexible and integrated systems.
Human judgment remains central to decision-making, even with AI advancements.
Organizations should focus on value optimization, data sharing, and observability to drive business success.
Governance maturity is crucial for the successful implementation of data mesh and AI initiatives.
Prompt engineering and model fine-tuning are emerging as essential skills in the D&A landscape.

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