Knowledge Graph Architecture & Agentic Workflows - Knowledge graphs can enhance agentic workflows by enabling reasoning and question decomposition, moving beyond simple vector searches [4] - Knowledge graphs facilitate the expression of simple data models to agents, aiding in accurate information retrieval and expansion with more data [5] - The integration of knowledge graphs allows for more precise question answering through a more expressive data model [22] Data Modeling & Entity Extraction - Data modeling should focus on defining key entities and their relationships, such as people, skills, and activities [17] - Entity extraction from unstructured documents, like resumes, can be used to create a graph database representing these relationships [18] - Pydantic classes and Langchain can be used for entity extraction workflows to decompose documents and extract JSON data containing skills and accomplishments [19][20] Benefits of Graph Databases - Graph databases enable flexible queries and high performance for complex traversals across skills, systems, domains, and accomplishments [30] - Graph databases allow for easy addition of new data and relationships, which is crucial for rapid iteration and adaptation in agentic systems [37] - Graph databases facilitate the creation of tools to find collaborators based on shared projects and domains [39] Practical Application: Employee Skills Analysis - The presentation uses an employee graph example to demonstrate skills analysis, similarity searches, and identification of skill gaps [5] - Initial attempts to answer questions using only document embeddings are inaccurate, highlighting the need for entity extraction and metadata [9] - By leveraging a knowledge graph, the system can accurately answer questions about the number of developers with specific skills, such as Python, and identify similar employees based on skill sets [24][25]

Problem Statement - Data readiness is a myth, and achieving perfect data for AI is an unattainable pipe dream [1][2][3] - Fortune 500 companies lose an average of $250 million due to poor data quality [7] - Traditional semantic layers and knowledge graphs are insufficient for capturing the nuances of business language and tribal knowledge [8][9][10][11][12][13][14] Solution: Agentic Semantic Layer (PromQL) - PromQL is presented as a "day zero smart analyst" AI system that learns and improves over time through course correction and steering [17][18][19][20] - It uses a domain-specific language (DSL) for data retrieval, computation, aggregation, and semantics, decoupling LLM plan generation from execution [21][22] - The system allows for editing the AI's "brain" to correct its understanding and guide its learning [28] - It incorporates a prompt learning layer to improve the semantic graph and create a company-specific business language [31] - The semantic layer is version controlled, allowing for fallback to previous builds [33] Key Features and Benefits - Correctable, explainable, and steerable AI that improves with use [19] - Ability to handle messy data and understand business context [24][25] - Reduces months of work into immediate start, enabling faster AI deployments [37] - Self-improving and achieves 100% accuracy on complex tasks [37] Demonstrated Capabilities - The system can understand what revenue means and perform calculations [23] - It can identify and correct errors in data, such as incorrect status values [24] - It can integrate data from multiple databases and SAS applications [25][27] - It can summarize support tickets and extract sentiment [26][29] - It can learn the meaning of custom terms and relationships between tables [35][36] Customer Validation - A Fortune 500 food chain company and a high-growth fintech company achieved 100% accurate AI using PromQL [38]

Snowflake (SNOW) Update Summary Company Overview - **Company**: Snowflake Inc. (SNOW) - **Event**: Update/Briefing on June 11, 2025 - **Key Speakers**: Ruby (Head of Partner Marketing for APJ), Mike Garnan (CRO), Ash Willis (VP of Partner Alliance for APJ) Key Points Industry and Market Position - Snowflake is experiencing significant growth, with over 20,000 attendees at their recent summit, doubling their growth from the previous year [4][5] - The company is a sponsor for the LA 2028 Olympics, indicating strong brand visibility and market engagement [7] Financial Performance - Snowflake reported a billion-dollar revenue quarter, representing a **26% year-on-year growth** [18] - The company's **net revenue retention rate** is at **124%**, indicating that existing customers are expanding their contracts [18] - Remaining revenue obligation (RPO) stands at **$6.7 billion**, a **34% year-on-year increase**, suggesting strong future revenue potential [18][24] Customer Engagement and Product Adoption - Snowflake has a total of **11,200 customers**, with **451 new customers** added in Q1 [19] - Approximately **50% of customers** are actively using Snowflake's AI and ML products, showcasing strong adoption of advanced technologies [19] - The company emphasizes the importance of simplifying AI initiatives for customers, which is a key selling point [20] Strategic Focus and Partnerships - Snowflake is focusing on building a robust partner ecosystem to drive consumption and accelerate migrations from legacy systems [25][27] - The company is targeting traditional warehousing technologies like Teradata and Oracle Exadata for migration opportunities [26] - A unique compensation structure is in place where sales teams are incentivized based on consumption rather than contract bookings, aligning interests with customer success [25] AI and Innovation - Snowflake is leveraging AI to enhance productivity and drive business outcomes, with examples of AI applications improving operational efficiency [35][36] - The partnership with Spark New Zealand and Relational AI is highlighted as a strategic move to enhance decision-making capabilities through AI [75][90] Summit Insights - The recent summit showcased a strong network effect, with **70% of content delivered by customers**, emphasizing real-world applications of Snowflake's technology [40] - The event attracted a diverse audience, including business leaders and technical experts, indicating a shift towards business impact rather than just technology [39] Future Outlook - Snowflake plans to invest significantly in its partner ecosystem, including traditional resellers and systems integrators, to scale its business efficiently [48][50] - The company aims to activate its channel to potentially exceed **35% growth** in the future [52] Customer Case Studies - Spark New Zealand is leveraging AI to streamline processes, such as call summarization, which enhances data quality and operational efficiency [84][89] - Relational AI is working with Snowflake to create a relational knowledge graph, addressing knowledge silos within organizations [97][100] Additional Insights - The emphasis on AI is not about job replacement but enhancing productivity and enabling existing employees to work more efficiently [35][36] - The partnership approach is seen as crucial for future innovation, with a focus on collaborative growth and shared success [109][110] This summary encapsulates the key insights and strategic directions discussed during the Snowflake update, highlighting the company's robust growth, innovative use of AI, and commitment to building a strong partner ecosystem.

在金融营销进入智能化 3.0 时代的当下，AI 与 Agent 已不再是锦上添花的"选配"，而是重塑 客户洞察、决策效率和服务体验的核心驱动力。本文将结合行业演进、现实痛点与前沿实 践，探讨 AI 技术如何为金融机构打造差异化竞争力，开启以"智能密度"为核心的新一轮营 销升级。 很高兴在今天这样一个充满变革的时刻，能和大家一起探讨一个金融营销人都高度关注的话题：AI 和 Agent 如何深刻改变我们的工作，以及我们如何抓住这波浪潮，为企业建立真正的竞争壁垒。 1 回望与前瞻：金融营销的进化之路与 AI 的价值定位 在我们这个行业摸爬滚打十几二十年，大家都亲身经历了金融营销的巨大变迁。从最早依赖网点、靠客 户经理"跑断腿"的传统 1.0 时代，那时候效率低、覆盖窄，效果基本靠经验；到后来互联网兴起，我们 进入了数字化 2.0 时代，有了 CRM，有了线上渠道，开始讲数据、讲精准，银行 APP、网银成了主战 场，交易线上化率也确实上来了。但说实话，数据孤岛、体验割裂的问题一直没彻底解决，"千人一 面"的推送还是主流，转化率提升也遇到了瓶颈。 而现在，我们正站在智能化 3.0 时代的门槛上，甚至可以说，一只脚已经迈 ...

Market Overview - The Graph Database Market is projected to grow from USD 507.6 million in 2024 to USD 2,143.0 million by 2030, reflecting a Compound Annual Growth Rate (CAGR) of 27.1% during the forecast period [1] - Graph databases facilitate enterprise knowledge management by reconstructing complex data with interconnected nodes and relationships, enhancing information retrieval and navigation [1] Market Dynamics Drivers - Rising demand for AI and generative AI solutions is driving the growth of graph databases [3] - The rapid increase in data volume and complexity necessitates advanced data management solutions [3] - There is a growing demand for semantic search capabilities [3] Restraints - Challenges related to data quality and integration are hindering market growth [3] - The navigation of a saturated data management tool landscape poses difficulties for organizations [3] - Scalability issues are a concern for businesses looking to implement graph databases [3] Opportunities - Leveraging large language models (LLMs) can reduce the costs associated with knowledge graph construction [3] - The proliferation of knowledge graphs presents opportunities for data unification [3] - Increasing adoption in healthcare and life sciences is expected to revolutionize data management and enhance patient outcomes [3] Market Segmentation - The property graph segment is anticipated to hold the largest market size during the forecast period, representing data as nodes, edges, and properties [3] - The services segment is expected to experience the highest growth, encompassing managed services and professional services to support graph database implementation and operation [5] Regional Insights - The Asia-Pacific region is projected to have the highest market growth rate, driven by digital transformation and demand for sophisticated data management solutions [6] - In China, businesses are adopting graph database technology to enhance innovation and operational efficiency across various industries [6] - Australia is leveraging Neo4j's technology to develop a national-scale graph database aimed at improving research collaboration and sustainability [6] Key Players - Major vendors in the Graph Database market include IBM Corporation, Oracle, Microsoft Corporation, AWS, Neo4j, and others [7] - These companies are employing various growth strategies such as partnerships, new product launches, and acquisitions to expand their market presence [7]