Top Questions: Neudata New York Summer Data Summit 2025

As a preview, I’m sharing my questions for each speaker and panel—except for one, where you'll need to attend to hear what I ask live.

Data conference season continues in New York with Neudata’s event on May 8.

To help the data community prepare, I’ve crafted targeted questions for each speaker and panel. There are 3 key themes to my questions:

1. Strategic Trust in AI and Data-Driven Narratives

2. Operational Bottlenecks in Data Adoption

3. Macro Uncertainty as a Catalyst for Data Innovation

Link to agenda: https://www.neudata.co/agenda/new-york-summer-data-summit-2025-agenda?_gl=1*1m1pl3q*_gcl_au*OTg3MjM2MTkwLjE3NDIzMzYwNDc.

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Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into 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. 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. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.

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Note: Agenda as of May 4th, 2025

The Empire Stage

Welcome remarks

8:50 – 9:05 AM · Empire Stage

• Ian Webster, Chief Revenue Officer, Neudata

Question: How do you see the balance between proprietary data development and partnering with market data providers evolving over the next three to five years to sustain a competitive edge?

In conversation: Data, disruption and the future of finance

9:05 – 9:30 AM · Empire Stage · Tracks: Quantitative; Private markets; Data providers; Fundamental

• Mark Fleming-Williams, Head of Data Sourcing, Capital Fund Management (Moderator)

• Abraham Thomas, Previously Founder and Chief Data Officer, Quandl; Angel Investor and Independent Board Director

Question: Which emerging AI capabilities pose the greatest threat to existing data moats, and how should incumbents respond? I’d encourage the audience to check out Abraham’s Substack to get even more context and insights ahead of the discussion with Mark (who I hope you all already know from The Alternative Data Podcast).

Extracting insights embedded in earnings call transcripts using LLMs¹

9:30 – 10:00 AM · Empire Stage · Tracks: Quantitative; Fundamental

• Qingyi Huang, Head of AlphaWise Quant Research, Morgan Stanley

Question: What advice do you have for AI product creators who present AI-generated narrative summaries to senior stakeholders so they trust the nuance without second-guessing every edit?

The analyst of the future

10:00 – 10:30 · Empire Stage · Tracks: Quantitative; Private markets; Data providers; Fundamental

This session will explore how technology and data are reshaping the process of investment research, from the perspectives of sellside and buyside analysts, and data providers. Is AI a match for human-level analysis or more of a tool than a replacement? What are the benchmarks for funds on the long-term journey to insight automation?

• Jason DeRise, Head of Public Markets & Research Investment Products, Liberty Mutual Investments (Moderator)

• Chris Andrews, Global COO of Research, Morgan Stanley

• Siddhant Jayakumar, Founder & CEO, Finster AI

• Nicole Königstein, Chief AI Officer, quantmate

Question: You’ll need to attend the session to hear the questions I’ll ask while moderating. I’m thrilled to have such an amazing collection of talent on stage with me.

New vendor showcase

10:50 – 11:20 · Empire Stage · Tracks: Quantitative; Private markets; Fundamental

In this showcase session hear from some of the industry’s newest providers, as they share the datasets and product launches set to take the world of alternative data by storm. Ensure you’re keeping on top of all the latest updates and case studies to inform your future data acquisition strategy.

• Jenna Menking, Founder, Crosswalk

• Ben Darr, Founder, CredoIQ

• Joseph Berger, Managing Director, Capital Markets Division – Guideline

Question: Good luck to the showcase participants. Can you highlight your strongest investment use cases, your methodology (including compliance considerations), and how your offering differs from competing datasets?

Industrials neglected no more: Beating the market through nowcasting² with alternative macro data

11:20 – 11:40 · Empire Stage · Tracks: Quantitative; Fundamental

While consumers leave a footprint of their activities, there is less of that in the business-to-business world. As such, the industrials sector has remained out-of-grasp for PMs trying to get an informational edge. This talk will demonstrate how novel techniques of Nowcasting allow us to turn disparate macro data into structured signals, highlighting which companies are likely to beat or miss consensus revenues.

• Ajit Agrawal, Founder, AKAnomics

Question: Which non-traditional indicators have you found most predictive of industrial revenue surprises in your nowcasts?

Exploring holistic digital behavior across app and web

11:40 – 12:00 · Empire Stage · Tracks: Quantitative; Private markets; Fundamental

This presentation will explore how combining app and web data can offer a more comprehensive view during investment due diligence. Vlad will highlight how Sensor Tower’s True Audience Estimate helps cut through duplication across platforms to reveal a single, unified digital user count.

• Vlad Prelevic, Account Director, Sensor Tower

Question: How do you reconcile discrepancies between in-app engagement and web user journeys when constructing a unified True Audience Estimate?

Consolidating competition: M&A in the data market

12:00 – 12:20 · Empire Stage · Tracks: Quantitative; Private markets; Data providers; Fundamental

2025 has already brought a number of significant acquisitions in the data market. With consolidation seemingly on the rise, this session will draw on Neudata’s industry intelligence to assess what is driving this trend and what the impact is expected to be on the market.

• Barney Bruce-Smythe, Vice President, Neudata

Question: Given the long selling cycle associated with asset managers, how is the industry managing liquidity while building toward confirmed market fit? How do mergers, auditions and partnerships help shorten the selling cycle or slow cash burn rates?

What does Trump 2.0 mean for the industrials sector?

1:20 – 1:40 · Empire Stage · Track: Fundamental

Tax reform, supply chain disruptions and potential incoming trade policies have all put the US industrials sector in the spotlight for 2025. This session will explore different types of alternative data that can monitor subsectors such as defence, aviation, construction and manufacturing.

• Matt Yome, Research Analyst, Neudata

Question: Because markets are forward-looking, prices are reacting to policy talk before the actual impact is visible. Which datasets can narrow the gap between what’s being said and what’s actually happening such that it’s easier to see what could happen next?

The multimodal economist: A worldwide case study

1:40 – 2:00 · Empire Stage · Tracks: Quantitative; Fundamental

With government data quality declining due to low survey response rates and budget cuts, understanding the effects of growth, inflation, geopolitical shifts, and policy shocks on the worldwide economy is increasingly difficult for asset managers. This session will discuss an innovative multimodal AI approach, designed to increase the depth and breadth of domain expertise, while maintaining explainability. The demonstration will track global labour markets – generating JOLTS+ data and the effect of Trump administration policies – in real time.

• Apurv Jain, Founder and CEO, MacroXStudio

Question: If government data becomes less reliable due to budget cuts or reduced transparency, how can alternative data fill the gap? Are there cases where it already offers a more accurate view of real-world economies than official statistics? Note: I used this question in my preview for Neudata’s London conference in March, where Apurv spoke on this topic, but I’ll be attending this session in person, so I’m asking it again.

Alternative data use cases: Competitive intelligence in private investing

2:00 – 2:40 · Empire Stage · Track: Private markets

While the use cases may differ from public markets, alternative data can be a powerful tool for private market investors. Panelists will explore how venture capital and private equity funds are leveraging emerging data sources, together with new tools for processing data to move faster, benchmark better and gain a competitive edge.

• Keava Low, Senior Analyst, Neudata (Moderator)

• Brian Murphy, Head of Data Science, Salesforce Ventures

• David Teten, Venture Partner, Coolwater Capital; Founder and Managing Partner, Versatile Venture Capital

• Claire Saint-Donat, ex Bain Capital; ex Blackstone Group

• Warren Valdmanis, Managing Partner, FoW Partners

Question: In the due diligence process, private market investors need to ramp up quickly on specific opportunities before the investment window closes. What can data vendors do to better meet the needs of investors in this process?

Navigating market turmoil: Leveraging insights from historical scenario analysis using alternative data

3:00 – 3:20 · Empire Stage · Track: Quantitative

This presentation will explore two compelling case studies that highlight the transformative power of alternative data in decision-making. The first demonstrates how advanced data capabilities can help clients navigate the market environment and make informed allocation decisions. The second showcases how historical scenario analysis and alternative data can impact portfolio-specific strategies.

• Duncan Robinson, Director of Quantitative Insights & Data Science, Allspring Global Investments

Question: Using historical analogies can be powerful for establishing a framework for analyzing the current environment, but it can also be dangerous if there are structural differences between the present problem and the analogous situation. What advice do you have for assessing whether a historical scenario is the right one to apply to a current challenge?

From opportunity to edge: Quant data sourcing and strategy

3:20 – 4:00 · Empire Stage · Track: Quantitative

Quantitative funds have long been a driving force in alternative data adoption. How can quants source and onboard new datasets seamlessly? What dataset types have grown in availability and popularity? Does supply meet demand? What considerations must be made around data quality, cleaning and mapping to ensure funds maximise a dataset’s value?

• Amrita Tiwari, Investment Analytics, New York Life Investment Management (Moderator)

• Ben Cilia, Chief Data Officer, Quantbot

• Alexios Bevratos, Managing Director, Deputy Head of Alpha Research, Capital Fund Management

• Duncan Robinson, Director of Quantitative Insights & Data Science, Allspring Global Investments

Question: Can you share for the data providers what due diligence frameworks quants apply to data to detect hidden overfitting³ risks before onboarding?

The Chrysler Stage

Streamlining data buyer and provider relationships

9:40 – 10:30 · Chrysler Stage · Track: Data providers

This session will explore the evaluation of relationships with data providers, and zero in on the question of when to source a new provider and when to remain in an existing relationship. How can vendors best communicate to existing subscribers about product changes and updates? How willing are buyers to act as an early adopter of a new data product? How can pricing structures best reflect the value a data product can bring?

• Henry Scherman, Data Monetization Consultant, Neudata (Moderator)

• Gregory Kurzman, Senior Associate - Data Sourcing and Strategy, Balyasny Asset Management

• Eliza Raphael, Head of Data Services, Jump Trading

• Philip DiLemme, Data Science, King Street

Question: What should data sellers know about the different buyer personas across the asset management landscape, and how should they adapt the sales cycle for each? What can the data buyers do to help the data vendors meet their needs without sharing proprietary info? Shameless plug for There’s No Such Thing as a Data Buyer

Banking on industry data: Navigating the ebbs and flows of the banking sector

10:50 – 11:10 · Chrysler Stage · Tracks: Quantitative; Fundamental

Over the past two decades, the U.S. banking sector has experienced periods of both exuberance and turbulence. This session will explore the findings of a recent research paper from S&P Global Market Intelligence, aimed at helping investors navigate the sector’s ebbs and flows, offering critical insights into banking characteristics that distinguish winners from losers.

• Daniel Sandberg, Managing Director, Head of Quantitative Research & Solutions, S&P Global Market Intelligence

Question: The research paper leverages SNL financial metrics to break down bank performance into five factors and includes the source code for replication of the methodology (that’s a best practice other companies should follow). Here’s the link: https://www.spglobal.com/market-intelligence/en/news-insights/research/banking-on-industry-data-navigating-the-ebbs-and-flows-of-the-banking-sector. As a follow-up question to the report, what additional data sources or emerging metrics would you recommend investors integrate alongside the traditional, SNL-based factors to ensure their quantitative analyses remain both robust and forward‐looking?

Adopting cloud technology: Where do we start?

11:10 – 11:30 · Chrysler Stage · Tracks: Quantitative; Fundamental

As cloud adoption accelerates across the financial industry, many data providers and buy-side⁴ participants are left asking: What are the benefits of receiving market data through the cloud, and what are the steps to get started? In this session, we break down the cloud architecture that leading hedge funds use and outline how real-time data can be distributed through the cloud.

• Ethan Han, Research Analyst, Neudata

Question: How do you frame the cloud migration narrative to secure buy-in from IT, compliance and front office stakeholders?

Market vs alternative data: Two sides of the same coin?

11:30 – 11:55 · Chrysler Stage · Tracks: Quantitative; Fundamental

Alternative data is often defined by what it is not, but where do we draw the line between market and alternative data? Panelists will discuss how market, or traditional, data sources can identify trends and inform investment decisions across a range of trading strategies. How can traditional and alternative data sources form part of a cohesive investment approach?

• Farah Ruthnam-Sandys, Business Development Director, Neudata (Moderator)

• Steve Hansen, Global Head of Market Data, Davidson Kempner Capital

• Evan Reich, Global Head of Data Strategy and Sourcing, Verition

Question: Considering the benefits and constraints of traditional market data and alternative data, which tends to lead in signaling regime shifts when market dynamics are uncertain and chaotic? And in stable periods, does the signal hierarchy reverse?

From data to alpha: Streamlining the data management lifecycle

11:55 – 12:20 · Chrysler Stage · Tracks: Quantitative; Private markets; Fundamental

This session will delve into the challenges funds face in managing the data lifecycle, from cataloging, to trialing, budgeting, governing and migrating data. An exclusive update on Neudata’s new SaaS platform, Navigator, will highlight ways to streamline workflows, adapting to evolving regulatory demands whilst maintaining a competitive edge.

• Ruairi Powers, Senior Vice President, Product Manager, Neudata

• Dean Gray, Head of Design, Neudata

Question: How do you prevent siloed ownership from fragmenting a unified data strategy while also enabling innovation and speed across decentralized teams?

Rockefeller Room

Lunch & learn – Small data, big insights: A new playbook for quant signals

12:30 – 1:10 · Rockefeller Room · Track: Quantitative

At Hatched Analytics, we’ve developed a proprietary global dataset grounded in direct, observable measurements, never panel-based assumptions. With growing adoption among pioneering quant funds, momentum is building fast. Join our founders to explore how hidden digital signals deliver precision over assumptions, and how our latest quant product is delivering robust alpha. Plus, hear from Maiden Century on the real-world returns this approach is driving.

• Charmaine Kenny, Co-Founder, Hatched Analytics

• Donal Byrne, Co-Founder & CEO, Hatched Analytics

• Qaisar Hasan, Founder & CEO, Maiden Century

Question: How do you tell the success stories that persuade large quant shops⁵ to adopt “small data” solutions?

Unlocking AI potential: The critical role of licensed data feeds in business intelligence

1:20 – 2:10 · Rockefeller Room · Tracks: Quantitative; Private markets; Data providers; Fundamental

As Generative AI transforms business intelligence, the role of high-quality, fit-for-purpose, licensed data feeds has never been more critical. This panel brings together leaders from Dow Jones to discuss how real-time news and data power AI applications, the challenges of delivering AI-ready licensed content, and the evolving needs of enterprises integrating AI into their workflows.

• Dan Shar, Executive Vice President and General Manager, Dow Jones Wealth & Investing (Moderator)

• Joe Cappitelli, General Manager, Dow Jones Newswires

• Nicole Spell, Director of Business Management and Commercial Strategy, Dow Jones

• Brian Gelinas, Senior Vice President, B2B Product, Dow Jones

• Jason Malatesta, Head of Partnership and Licensing Americas, Dow Jones

Question: Are data buyers at asset managers and data sellers on the same page about derivative data creation and usage in the age of generative AI? Do data buyers and sellers view generative AI as just another data-processing tool, no different from previous machine learning techniques?

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What questions do you have for the panelists? Leave a comment below.

Know someone attending or presenting at the conference? Feel free to forward this to them.

- Jason DeRise, CFA

1. 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.

2. Nowcasting: In order to systematically forecast the next reported economic or company-specific financial result, multiple sources of high-frequency data are combined. The model continuously updates the forecast with increasing accuracy as the volume of data covering the unknown period increases.

3. Overfitting: When a model matches the training data very well when back-tested but fails in real-world use cases when the model is applied to new data.

4. Buy-side typically refers to institutional investors (hedge funds, mutual funds, etc.) who invest large amounts of capital, and sell-side typically refers to investment banking and research firms that provide execution and advisory services (research reports, investment recommendations, and financial analyses) to institutional investors.

5. 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).