Top Questions: BattleFin Discovery Day Miami, 2025
Grab your sunglasses and laptops to head to Miami for BattleFin’s Discovery Day 2025. Here are my top questions for the panels.
The Big Alt Data Party kicks off 2025 with a great lineup of speakers and topics. Here are my top questions for the speakers. https://www.battlefin.com/events/miami-2025
There’s 3 key themes to the top questions on my mind
The Integration of AI and Alternative Data1
Practical Applications and Insights for Industry Challenges
Methodological Transparency in Data and AI Use
Welcome to the Data Score newsletter, composed by DataChorus LLC. The newsletter is your source for insights into the world of data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. As one of the first 10 members of UBS Evidence Lab, I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. After moving on from UBS Evidence Lab, I’ve remained active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser, user, and creator of data, I have gained a unique perspective, which I am sharing through the newsletter.
BattleFin Discovery Day Miami, 2025 Conference Agenda
Note: Agenda as of January 12th, 2025
Day 1: January 22, 2025
5:00 PM – Panel: The Future of Decision-Making: AI Meets Alternative Data
How are asset management firms leveraging AI to extract more value out of alternative data? Where does the AI need to go to be part of the investment process? What are the obstacles? What attributes make data a good fit for AI?
Speakers:
Tim Harrington (Moderator), CEO & Co-Founder, BattleFin
Armando Gonzalez, CEO & Founder, Bigdata.com
Carson Boneck, Chief Data Officer, Balyasny Asset Management
Question: Thinking about current AI capabilities, is there a bigger near-term opportunity in alternative data use cases than more accurately and efficiently matching and joining multiple types of datasets? If so, what is it?
5:30 PM – Panel: Top 3 Macro Ideas for 2025 Driven by Alt Data Research
How can portfolio managers leverage alternative data in their investment process? What categories of data products are relevant for macro strategies? Are there leading and lagging indicators for macro investing2 and how can Alt Data be used to predict these indicators? How are quants3 approaching 2025 trends?
Speakers:
Nicole McQueen, Head of Global Technology Partnerships, AWS
Paul White, CEO & Co-Founder, Quantbot
Alvin Mok, Head of Data and Analytics, Select Equity
Question: How can quant models effectively adapt to the rapid impact of social media on markets? Do we have sufficient historical data to refine these signals?
6:00 PM – Panel: Leveraging Alt Data for KPI Analysis
Speakers:
Dan Entrup, Co-Founder, AggKnowledge
Andreas Aglen, President, Exabel
Tim Harrington, CEO & Co-Founder, BattleFin
Zac Yang, Head of Product, Exabel
Question: Many investors have been tracking revenue-based KPIs4 with alternative data for the past decade. Is the market ready to move on from these KPIs to additional factors that explain share price movements?
6:20 PM – Panel: From Wall Street to Main: Is 2025 the Year of Acceleration for Data & AI in the Corporate World?
Financial Services focused LLMs5—efficiency to research,
How CPG uses AI to help with pricing
Speakers:
Tim Harrington, CEO, BattleFin
Sigal Mendelevitch, Director, Global Emerging Technologies, PNG / Nimble
Uriel Knorovich, CEO & Co-Founder, Nimble
Rasool Aghdam, Head of Commercial Data Analysis, Coca-Cola
Question: Who is leading in leveraging LLMs for business performance: corporations or Wall Street, and what lessons can be learned from each?
Day 2: January 23, 2025
9:00 AM – Panel: The Evolution of Alt Data & AI Markets: What's Next for Alternative Data & AI in 2025?
Trends in data monetization, marketplaces, and ecosystem growth. LLMs as the Next Frontier in Alternative Data. Key challenges in scaling AI-driven solutions across industries.
Speakers:
Mark Peng, CTO, Tiny Fish
Natalya Dmitriyeva, Global Head of Enterprise Data, Schonfeld
Uriel Knorovich, CEO & Co-Founder, Nimble
Question: How will LLMs impact web mining6? Will they reduce existing pain points, increase accessibility, or create new challenges compared to traditional methods?
9:30 AM – Panel: Data Mosaic: Consumer/Retail Trends
Exploring consumer trends through transactional and behavioral data. Enhancing demand forecasting using social media and e-commerce analytics. Monitoring retail foot traffic and online sales with Alt Data.
Speakers:
Tim Harrington (Moderator), CEO & Co-Founder, BattleFin
Casey Webb, Head of Equity Research, Bridgewater
Michael Gunther, Head of Insights, Consumer Edge
Matt Nagle, SVP Sales and Business Development, Datos
Andrew Sprague, Head of Investor Vertical, Sensor Tower
Gene Gallagher, Director of Research, Exabel
Question: When different data sources (e.g., transactions, web traffic, app usage, foot traffic) suggest conflicting conclusions, what best practices should alternative data users follow?
10:10 AM – Panel: Data Mosaic: Earnings
Understanding how alternative data refines earnings expectations
Analyzing a diverse set of firms with upcoming earnings.
Speakers:
Liam Hynes, Global Head of New Product Development, S&P Global
Garrett DeSimone, Head Quant, OptionMetrics
Question: Which alternative data sets act as a market beta7 signal by moving the markets immediately when the metrics are released, informing the buyside8 consensus9 estimates of the likely outcome and the market reacting at the same time?
11:00 AM – Data Spotlight: Surfacing Insights with Kayrros & Exabel
Predicting performance with insights from alternative data
Operational improvement in portfolio companies using alternative data.
Speaker:
Niklas Huppmann, Head of Business Development and Product, Kayrros
Zac Yang, Product, Exabel
Question: How do portfolio companies leverage Kayrros’ environmental data to enhance performance and achieve operational goals?
11:20 AM – New Data Release: Sensor Tower Web Data
Speaker:
Anthony Bartolacci, Chief Strategy Officer, Sensor Tower
Question: How does web data enhance Sensor Tower’s app usage data, and what use cases demonstrate improved accuracy in company insights?
11:30 AM – Panel: Data Mosaic: Industrials
Supply chain disruptions and recovery trends. Macro trends impacting industries through alternative data lenses. Unveiling operational inefficiencies through IoT and sensor data.
Speakers:
Chris Petrescu, CEO, CP Capital
Ed Lavery, VP of Investor Intelligence, Placeri.AI
Andreas Aglen, CEO, Exabel
Phil DeFrancesco, Head of Product, Inrix
Question: How do investors leverage IoT data10 to gain an edge in analyzing industrial sector trends and identifying inefficiencies?
11:50 AM – Panel: State of Institutional Adoption for Trade Digital Assets
Tokenized assets, Crypto
Speakers:
Tim Kwok, Head of Sales, Americas, Talos Trading
Alex Kallelis, Sales Business Development, Hidden Road
Question: What analytics could help fundamentally driven investors determine intrinsic value in cryptocurrencies, if achievable?
Day 3: January 24, 2025
9:00 AM – Using Alt Data to Determine Risk and Hot Spots
Identifying early warning signals for geopolitical instability.
Using alt data to analyze trade flows, sanctions, and economic disruptions.
Challenge in interpreting data and second order effects.
Speakers:
Rich Brown, Global head, Market Data, Jain Global
Rich Falk-Wallace, Co-Founder & CEO, Arcana
Dr. Richard Peterson, Founder & CEO, MarketPsych
Stas Melnikov, Head of Quantitative Research & Risk Data Solutions, SAS
Question: Does alternative data show early signs of changing trade behavior in recent weeks and months in response to the changing political landscape?
9:30 AM – Compensation and Sales Workforce indicators of company Performance with RepVue - Sales Compensation Report
With Revelio Labs.
Speakers:
Adam Little, CEO VP of Data Operations, RepVue
Jin Yan, Senior Economist, Revelio Labs
Question: What does RepVue and Revelio Labs data show for the health of the B2B sales profession? Is there an excess supply of talent or more demand than can be filled?
10:00 AM – Product Showcase: Sourcing Proprietary Insights with Survey Data
Building a scalable data strategy for hedge funds and asset managers.
Challenges in vendor selection, prioritization and vendor negotiation
Speakers:
Aarti Desai, RVP, Financial Services, NewtonX
Question: How have advancements in AI/ML transformed survey data collection? Beyond cost reduction, what new insights are now possible?
10:10 AM – Buyside: Data Strategy and Sourcing
Speakers:
David Chen, Senior Economist, Schonefeld
Danielle Castelli, Baupost Group
Question: What challenges in data sourcing do buyside firms face that data vendors often overlook, and how can vendors address these gaps?
10:40 AM – Data Mosaic - Healthcare
Tracking trends in biotech, pharmaceuticals and healthcare services. Impact of efficiencies associated with telehealth and digital health adoption
Speakers:
Tim Harrington, CEO & Co-Founder, BattleFin
Mark Holmquist, Vice President, Head of MarketPulse, SG2
Question: In the telehealth sector, how can generative AI11 realistically impact medical advice? What are the near-term opportunities, and how should the industry address associated risks and ethical concerns?
11:10 AM – New Data Provider Showcase
Introducing innovative data products and firms.
Speakers:
Ish Pandher, Chief Strategy Officer, BattleFin
Matt Ober, General Partner, Social Leverage
Ethan Berkman, Principal, Social Leverage
Tony Berkman, Managing Director, Two Sigma
Stewart Stimson, Head of Data Strategy, Jump Trading
Question: Good luck to the new data providers. No question here. Instead, just advice to make sure presentations are aligned with investment debates that matter to investors and to be sure to be as transparent as possible about the data product’s methodology.
What questions do you have for the panelists? Leave a comment below
Would this content help someone attending or presenting at the conference? Feel free to forward it on.
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- Jason DeRise, CFA
Lot’s of jargon today… By the way, you can always bookmark the Jargonator article if you need a reference for tech, data, investing or business jargon. I constantly add new terms, including one from today’s article (IoT):
Alternative data: Alternative data refers to data that is not traditional or conventional in the context of the finance and investing industries. Traditional data often includes factors like share prices, a company's earnings, valuation ratios, and other widely available financial data. Alternative data can include anything from transaction data, social media data, web traffic data, web mined data, satellite images, and more. This data is typically unstructured and requires more advanced data engineering and science skills to generate insights.
Macro Funds: as a simplified summary, they follow an investing strategy based on global macroeconomic views and are typically executed in a portfolio by investing across entire asset classes like fixed income, currencies, derivatives, and equities. They are not focused on company-specific, bottom-up investing choices.
Quant funds: Short for "quantitative funds," also referred to as systematic funds. Systematic refers to a quantitative (quant) approach to portfolio allocation based on advanced statistical models and machine learning (with varying degrees of human involvement “in the loop” or “on the loop” managing the programmatic decision-making).
Key Performance Indicators (KPIs): These are quantifiable measures used to evaluate the success of an organization, employee, etc. in meeting objectives for performance.
Large Language Models (LLMs): These are machine learning models trained on a large volume of text data. LLMs, such as GPT-4 or ChatGPT, are designed to understand context, generate human-like text, and respond to prompts based on the input they're given. It is designed to simulate human-like conversation and can be used in a range of applications, from drafting emails to writing Python code and more. It analyzes the input it receives and then generates an appropriate response, all based on the vast amount of text data it was trained on.
Web Mining (or Web Scraping): The process of using automated software to extract large amounts of data from websites.
Beta: In finance, beta is a measure of investment portfolio risk. It represents the sensitivity of a portfolio's returns to changes in the market's returns. A beta of 1 means the investment's price will move with the market, while a beta less than 1 means the investment will be less volatile than the market. In the context of data, beta refers to the data’s ability to explain the market’s movements because the data is widely available and therefore fully digested into the share price almost immediately. This level of market pricing efficiency means there’s not much alpha to be generated, but the data is still needed to understand why the market is moving.
Buyside typically refers to institutional investors (Hedge funds, mutual funds, etc.) who invest large amounts of capital, and Sellside typically refers to investment banking and research firms that provide execution and advisory services (research reports, investment recommendations, and financial analyses) to institutional investors.
Consensus: “The consensus” is the average view of the sell-side for a specific financial measure. Typically, it refers to revenue or earnings per share (EPS), but it can be any financial measure. It is used as a benchmark for what is currently factored into the share price and for assessing if new results or news are better or worse than expected. However, it is important to know that sometimes there’s an unstated buyside consensus that would be the better benchmark for expectations.
Internet of Things (IoT) data: A network of interconnected devices and sensors that communicate and exchange data in real time, often used to gather insights from industrial operations. The exhaust data can be analyzed for investment use cases as a type of alternative data.
Generative AI: AI models that can generate data like text, images, etc. For example, a generative AI model can write an article, paint a picture, or even compose music.