What does AI need to do to deliver real economic value. Let's talk about it with Charles Sansbury, the CEO of Cloudera, who is here with us in studio for a video brought to you by Cloudera. Charles, great to see you.How are you. >> Great to see you and thanks for having me. >> Thanks for being here.We've been talking on the show so much about the economic value of artificial intelligence. Um whether or not there there will be an ROI on this technology. >> I'm so happy to be speaking with you today because y ...

Summary of Braze FY Conference Call - September 10, 2025 Company Overview - **Company**: Braze (NasdaqGS:BRZE) - **Industry**: Customer Engagement and Marketing Technology Key Points Data Architecture and Competitive Advantage - Braze emphasizes the importance of data architecture as a competitive advantage in the AI era, focusing on real-time data processing rather than traditional data warehousing [7][12][11] - The company has built a stream processing engine similar to high-frequency trading systems, allowing for real-time updates and actions based on customer engagement data [8][9][10] - Braze processes over 10 trillion data points annually, highlighting its capability to handle massive data flows efficiently [29] Business Momentum and Growth Outlook - Braze has shown an improving growth outlook for margins and profits, with increased productivity in its sales force over the last two quarters [34][36] - The company has improved its renewal processes, leading to lower downsell risks and better customer retention [38][40] - The overall effectiveness of the sales team has increased, contributing to a positive forecast for future performance [41] Replacement Cycle and Market Position - Braze is four years post-IPO and is analyzing changes in enterprise replacement cycles, indicating a shift in customer engagement strategies [7] - The company is focusing on enhancing its international strategy and verticalization to improve efficiency [38] AI and Composable Intelligence - Braze is transitioning to a new framework of context, intelligence, and interaction, leveraging advancements in AI and machine learning [20][21] - The concept of "composable intelligence" is introduced, where models are imbued with brand knowledge and can operate autonomously, enhancing marketing strategies [24][23] - The integration of AI tools aims to improve marketer productivity and customer engagement by automating decision-making processes [22][28] OfferFit and Unit Economics - OfferFit, a new product line, has two SKU types priced between $100,000 and $300,000, targeting enterprises with high-leverage use cases [49][50] - The potential for cross-selling between OfferFit and Braze's existing customer base is significant, with many OfferFit customers not currently using Braze's customer engagement solutions [51] - The decisioning products have higher gross margin potential compared to traditional messaging services, positioning Braze favorably in the market [52] Future Outlook - Braze is excited about upcoming developments in AI-centric customer engagement and plans to share more at the Forge event [53] - The company is focused on enhancing its product portfolio and leveraging AI to drive customer engagement strategies [52][53] Additional Important Insights - The company has made significant investments in first-party data and real-time context understanding, which are crucial for effective customer engagement [29][30] - Braze continues to expand its channel offerings, including new functionalities for messaging platforms like WhatsApp and Kakao [30]

Market Volatility and Systemic Risk - Financial institutions face systemic risks amplified by geopolitical shocks, tariffs (spiking costs by 10-20%), and regulatory changes, necessitating proactive risk management [1] - The interconnectedness of global economies means events in one region (e g, Japan, London) can impact others, highlighting the importance of mitigating systemic risk [1] - Traditional risk models often struggle to keep pace with intraday market volatility, requiring faster data processing and analysis [7] Data and Technology Challenges - Many financial institutions still rely on legacy systems for risk management, which are inadequate for today's volatile environment [1] - Institutions face a data explosion when performing risk calculations like Monte Carlo simulations, generating 15+ terabytes of data daily for a 10-day VaR calculation [5] - Moving data to and from the public cloud for calculations can be slow (e g, taking 3 hours to retrieve 15 terabytes), hindering timely risk analysis [8] Hybrid Cloud and Data Architecture - Hybrid cloud environments are becoming essential, allowing institutions to leverage the scalability of the public cloud while keeping sensitive data on-premises due to regulatory requirements [10][39] - A well-designed data architecture is critical for CIOs, enabling them to leverage different environments for their respective strengths [44][45] - DDN enables a hybrid multi-protocol environment, allowing institutions to keep sensitive data on-premises while running analytics and making the results available quickly [44] DDN's Solutions and Capabilities - DDN offers data intelligence solutions, focusing on providing relevant information up the stack rather than just storage [1] - DDN's Infinia platform can achieve up to 8x faster performance compared to legacy NAS architectures in grid runs with Murex and scenario reloads [28][29] - DDN's data ocean concept allows institutions to leverage data where it sits, avoiding slow and difficult data movement, and provides thousands of tags for finding relevant data [36][37]