Greg Holcombe

❄ Financial services organizations handle a lot of data, making it crucial that the data is managed efficiently and securely. Jill Grozalsky Roberson explains why we trust Snowflake Cloud Data Platform to meet the needs of our FinServ clients. ❄ Dive into the top four reasons here: https://lnkd.in/e_npssRf

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FIS has teamed up with Snowflake to bring greater agility and innovation to its capital markets products. With a strong data foundation and better access to insights, FIS is discovering ways to layer AI and ML technologies on top of its data in Snowflake to deliver additional value to customers. Learn how FIS uses 20% less compute and has a 33% cost savings with Snowflake: https://okt.to/Q56t0B

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Financial services regulations can be complex, tricky to navigate, and sometimes vague, leaving companies even more challenges to solve for. Allen Rohner's advice for fellow leaders? Simplify as many processes as you can. Hear more relatable opinions and sage advice on the newest episode of The Bank Tellers, with Griffin's Founder and CTO. YouTube: https://lnkd.in/edPS88kv Spotify: https://lnkd.in/gjhP27SM #thebanktellers #money2020 #fintech

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John Steinmetz (VP of Analytics at Gallo Mechanical) and our CEO Ethan Aaron joined John Wessel + Eric Dodds on The Data Stack Show (sponsored by RudderStack) to dig into building High-Impact Data Teams. Full episode at the link below 👇 Here are the highlights: Ethan’s Background (0:47) John’s Background (1:16) Data Teams vs. Engineering Teams (2:04) Career Paths in Data (3:40) Pressure of Large Companies (6:10) Contrasting Industries: Expedia vs. Gallo (9:02) Establishing Trust in Data (11:30) From Sales to Data (16:30) Understanding Success Metrics (18:58) Creating Daily Business Value (23:03) Aligning Data Work with Leadership Goals (29:30) Differences Between Data and Software Engineering (31:25) The Role of Data in Business (35:56) Understanding Data Contracts (39:35) Accuracy vs. Usability in Data (41:51) Observational Skills in Data Roles (44:03) Defining Product in Data vs. Software (47:07) Final Thoughts and Takeaways (52:42) https://lnkd.in/ebyygTfZ

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Shared Nearest Neighbors (SNN) may offer advantages over traditional metrics like Manhattan and Euclidean, especially with high-dimensional data. Learn more about SNN 🔍 🖊️ by Brett Kennedy | #DataScience #AnomalyDetection

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Chris Hronek, Linqto’s Director of Data Engineering, experienced massive growth of his data warehouse, his team, and the complexity of requests he received -- a common occurrence for cutting edge data leaders. Chris found himself at a crossroads: pursue the conventional data stack to address the increasing demands on financial reporting analytics or find an alternative that met his data development goals. That’s when Chris turned to SDF to gain a complete understanding of his data warehouse and improve Linqto’s transformation layer. Learn how Chris and the Linqto team increased confidence in their decisions and the quality of their data warehouse. https://lnkd.in/gTH-3ABz

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In his latest TDAN.com - The Data Administration Newsletter column, John Wills, Founder & Principal of Prentice Gate Advisors, highlights why data mapping isn’t just technical—it’s a strategic necessity for data leaders. Poor mapping leads to quality risks and hidden people costs that drain resources. Key Tips: 1. Define & align on the value of data mapping. 2. Create standards to guide its use. 3. Enforce validation to avoid rework. Boost data quality, reduce confusion, and free your team for AI and analytics. Are you making the most of data mapping? Read now: https://lnkd.in/eamy9hRD #DataLeadership #DataMapping #AI #Analytics #DataStructures #Data

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Get better visibility into your BigQuery spend! With Costimize's BigQuery Jobs Report, you can see which service accounts or users drive your expenses. Pair it with our "Most Expensive BigQueries" analyzer to fine-tune your queries. Keep your BigQuery costs under control with Costimize optimizers. #BigQuery #CostManagement #Optimization #Cloud

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🥊 Stop fighting data, start making strategic decisions 💡 Our clients feel the benefits of consolidated data. Watch how easy it is to get started today with only 30 minutes of your time! https://hubs.li/Q02tkhdm0 #financialreporting #dataconsolidation #cfocommunity #privateequity #middlemarket

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Read about the Clearwater Snowflake integration in FundFire! Clearwater Analytics VP of Investment Data Management Subbiah Subramanian weighs in on this timely integration and how Insurance firms, asset managers and asset owners can now seamlessly access all their investment data together – think equities, fixed income, real estate, private equity, loans and private debt -- in order to capitalize on new opportunities to grow their business. Read the story here: https://cw-an.co/4eZtgkN #InvestmentDataManagement #DataIntegration

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As you leverage automatic indexing, collaborative insights, and self-service capabilities, consider how these elements merge to create a culture of data-driven decision-making within your organization. Read 👉 https://lttr.ai/AWz5y #DataGovernance #DataGovernanceDay

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Struggling to understand the who's, the what's, the why's and beyond of data modernization? Julian Trostinsky, Global Director of Solutions Engineering at Gresham, has all the insights you need to transform your financial operations. He also highlights some real-world success stories that prove data modernization is not just a theoretical concept. Learn the 5 steps to achieve data modernization in our latest blog 👇: https://lnkd.in/eJ9bv4j4 #DataModernization, made simple.

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📊Data analytics is transforming how #FinancialInstitutions operate and serve their account holders. In Marla Pieton, M.A.'s latest blog https://bit.ly/3YNlHbt, she dives into the 2024 Alkami Telemetry Data Report and highlights how data analytics can elevate banking strategies. Key takeaways include: 🔹Enhanced Customer Insights: Leverage data to better understand and meet customer needs. 🔹Operational Efficiency: Use analytics to streamline operations and reduce costs. 🔹Risk Management: Improve decision-making with predictive analytics and risk assessment tools. 🔹Innovation and Growth: Drive business strategy and growth through actionable data insights. 👉 The 2024 Alkami Telemetry Data Report provides an in-depth look at how data-driven insights are shaping the future of banking. Download the full report here: [https://bit.ly/4biS30n] #DataAnalytics #BankingInnovation #FinancialServices #CustomerInsights #RiskManagement

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Organizations are moving quickly to maximize generative AI’s potential. In The New Stack, Clearwater Analytics Chief Product and Technology Officer Souvik Das shares key lessons for leaders to effectively leverage their generative AI investments, covering the advantages of small language models, the strategic use of synthetic data, and the importance of domain expertise. Read the full story here: https://cw-an.co/3OBuO8V

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🔶 This interview provides an overview of how AI, particularly LLMs, is transforming the financial sector, highlighting the critical role of data strategy and the potential for AI to enhance decision-making processes. 🔷 Jonathan Regenstein, head of financial services AI at Snowflake focuses on the evolving landscape of artificial intelligence in the financial services industry, particularly the role of large language models (LLMs) in managing and analyzing data. Here are the key themes and insights from the interview: 🌟 AI and Data Organization: Regenstein compares business data to sticky notes, indicating its disorganized nature. LLMs can help transform this chaotic data into a more structured format, akin to organized index cards. 🔄 Shift to Unstructured Data: The financial industry has historically relied on structured data, but LLMs are driving a shift towards recognizing the value of unstructured data. This shift involves exploring effective methods for ingesting, tagging, and utilizing unstructured data to extract insights. 🔍 Importance of Data Strategy: Successful AI implementation requires a robust data strategy. Organizations must focus on the quality, accessibility, and usability of data before they can fully leverage AI technologies. 🗣️ Explanatory Capabilities of LLMs: Unlike traditional natural language processing, LLMs can explain their decisions. This capability is vital for applications like fraud detection and risk assessment, helping users understand the reasoning behind flagged transactions or risk scores. 📊 Key Use Cases for LLMs: LLMs can analyze changes in textual data over time, such as assessing the tone of financial reports (like ten-Ks and earnings transcripts) to gauge sentiment and changes in optimism or pessimism. 📈 Value for Quants: Quantitative analysts (quants) are finding significant value in using LLMs to convert textual data into numeric features, which can then be utilized in quant models. This process enables back-testing and enhances model performance. 💻 Hands-On Coding Approach: Regenstein advocates for a practical approach to coding and data analysis, suggesting that personally coding solutions helps in understanding data nuances and the limitations of models. 📉 Evolving AI Conversations: Discussions around AI are shifting from being technology-driven to focusing on solving specific business problems and determining how AI can assist in that process. 🏦 Performance Disparities: Organizations with a strong data foundation are likely to excel in adopting AI, leading to disparities in performance among funds. Effectively utilizing AI will depend more on data strategy than the size of the organization. 🔮 Future of AI in Decision-Making: The future of AI in finance may involve multi-agent systems acting as decision-making aids. Although still a work in progress, these systems could significantly alter how financial decisions are made, enhancing data-driven insights and potentially reducing biases.

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While Snowflake is powerful at ingesting data and pulling information from various sources, the data that is immediately available is not always ready for reliable reporting and analysis. The data often requires mastering, mapping, and governance to achieve consistency and address quality issues before a firm can effectively use it. Our latest article explains how Private Equity firms can combine Snowflake — or any data warehouse — with Holland Mountain's ATLAS MDM to optimise their data quality and reporting. Read now 👉 https://lnkd.in/d-_YvPTZ Barnaby Piggott | Tim Friedman | Katie Brown | Harpreet Lakhan #privateequity #data #ai

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🎥 Get it on demand: The Alation DCMM #alationbrief is now available for you to tune in! Watch now as Nick Duce shares how you can see if your organization is data mature using The Alation Data Culture Maturity Model. 📏 If not, grow up and watch this brief 🌱 https://lnkd.in/ek-A_qdz #dataculture #dcmm #alationbrief

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Snowflake’s Arctic-TILT model, powering our Document Al, beats GPT-4 with just 0.8B parameters, securing a top spot in the standard benchmark for document understanding DocQVA. This efficiency not only reduces operational costs but also enhances the scalability of solutions built on Arctic-TILT. This also means that it does not require large GPU instances or resources to leverage the Arctic-TILT model in Document AI, making it more accessible and adaptable to different customer sizes and needs.

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Register for the Financial Services AI Demo Day virtual event to see what financial organizations are building with Snowflake's AI Data Cloud as they pivot from enterprise AI proof of concepts to implementation. Learn how to tap into and fully utilize your entire data estate – from structured data to unstructured data – to power AI use cases.

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