[Music] So, as you come in, we have here a server set up with everything you'll need. If you want to follow along, you should have gotten a post-it note. If you don't, just raise your hand and my colleague Alex over here will come find you and we'll provide you with one.Uh, basically what you're going to do is you're just going to go, if you have a number 160 or below, you go to this link here, the QR code on top as well. Um, and if you have a number that's 2011 or above, you go to the second link or the QR ...

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.

Core Insights - The article emphasizes that AI and Agents are no longer optional tools but are essential drivers for transforming customer insights, decision-making efficiency, and service experience in financial marketing [1][2][3] Evolution of Financial Marketing - Financial marketing has evolved from a traditional model reliant on physical branches and customer managers (1.0) to a digital model utilizing CRM and online channels (2.0), but issues like data silos and fragmented experiences persist [2] - The industry is now entering the intelligent 3.0 era, where AI technologies, particularly large language models and Agents, are becoming the core engines driving marketing transformation [2][3] AI's Value Proposition - AI provides unprecedented customer insights by analyzing both structured and unstructured data, enabling the identification of deep, often unrecognized customer needs [2] - AI facilitates real-time and precise decision-making by integrating various data points to generate optimal marketing strategies tailored to individual customers [3] - AI-driven Agents enhance service execution by automating repetitive tasks, improving efficiency, and allowing human staff to focus on more complex, value-added services [4] Current Challenges in Financial Marketing - High customer acquisition costs and low conversion rates are significant challenges, with customer acquisition costs (CAC) often exceeding thousands of dollars [5][6] - Personalization remains a challenge, as many financial institutions struggle to provide truly individualized experiences due to data fragmentation [7] - Complex products lead to customer confusion, making it difficult for them to make informed purchasing decisions [8] - Regulatory compliance poses challenges to innovation, requiring a balance between compliance and efficiency [8] - Measuring marketing effectiveness is complicated, with traditional attribution models failing to provide clear insights into ROI [9] AI and Agent Solutions - A robust "intelligent marketing platform" is proposed as a solution, consisting of a data foundation that integrates internal and external data to create a comprehensive customer view [10] - The platform includes an "intelligent engine" for AI algorithms that support customer understanding, predictive analytics, and decision-making [11] - Successful case studies demonstrate the tangible benefits of AI and Agents in enhancing customer insights, improving conversion rates, and increasing marketing efficiency [12] Future Outlook - The future of financial marketing will focus on "intelligent density," where the effective use of smart technologies will create competitive advantages in understanding customers and optimizing experiences [14]

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]