Top Questions: Neudata London Data Summit 2025
Previewing Neudata's London Data Summit on 27 March, including questions for each speaker and panel.
Neudata will host its London Data Summit on 27 March 2025 in Bishopsgate, London. https://www.neudata.co/events/london-data-summit-2025.
To help the data community prepare, I've created targeted questions for each speaker and panel. There are 3 key themes to my questions:
The path from data to Insight to Alpha
Human and Machine Collaboration in the AI Era
Operational and strategic dimensions of data acquisition
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Welcome to the Data Score newsletter, composed by DataChorus LLC. The newsletter is your go-to 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. 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.
Note: Agenda as of 22 March, 2025
Opening remarks
8:50 - 9:05 | Leadenhall Hub | Keynote
Ian Webster, Chief Revenue Officer, Neudata
Question: How do you see the data advisory industry evolving over the next 3 to 5 years, given the rapid expansion of data availability and AI capabilities?
DDQs and data listing optimisation: Best practices for data providers
8:50 - 9:40 | Lothbury Library | Data providers
In this interactive workshop and Q&A, join Neudata’s Vendor Engagement and Regulatory Leads as they share best practices for data providers to maximise success. Hear hints and tips on presenting a dataset as an attractive product and learn strategies for overcoming common compliance and DDQ1 pitfalls, to build confidence with potential buyers.
Brittany Thomas Senior Regulatory Analyst, Neudata
Saima Jannath Vendor Engagement Associate, Neudata
Question: What are the most common misconceptions data providers have about key elements of the due diligence process?
Future-proofing: Competing in an AI-driven, constantly changing, quant landscape
9:05 - 9:30 | Leadenhall Hub | Keynote
Ian Webster Chief Revenue Officer, Neudata
Paul White, Co-Founder and CEO, Quantbot Technologies
Question: As more data feeds into AI-driven models, is GPU2 demand rising on the buy-side3? Could short-term GPU constraints impact the viability of increasingly compute-intensive strategies? Are users actively optimizing its use of AI or just assuming more compute power will be unlocked in the future?
The ABCs of research management: Automation, big data and content
9:30 - 10:00 | Leadenhall Hub | Keynote
Nicole Bauthier Executive Director, Head of AlphaWise Data for Europe, Morgan Stanley
Paul Walsh Associate Director of Research, Morgan Stanley
Question: How might sell-side4 research evolve as GenAI5 becomes more capable of replicating quality research report structure and insights?
From engagement to execution: Selling to data buyers
9:40 - 10:30 | Lothbury Library | Data providers
This exclusive session will offer invaluable guidance and insights from those on the frontlines of data acquisition. Whether at the point of sourcing, onboarding, testing or governance, what are the biggest challenges faced by data buyers and providers? How can vendors streamline their strategies, to ensure best results from initial engagement through to execution?
Mark Fleming-Williams Head of Data Sourcing, Capital Fund Management
Leo Murison Lead Data Scientist, Jupiter Asset Management
Amy Dafnis Data Sourcing Lead, Rokos Capital Management
Henry Scherman Consultant, Neudata (Moderator)
Question: Since buy-side firms rarely share direct feedback during the data assessment process, how can vendors “read between the lines” to extract useful signals from limited responses?
The European investment market: Volatility, growth and opportunity
10:00 - 10:30 | Leadenhall Hub | Keynote
In an ever-evolving European market, businesses face currency fluctuations, disrupted supply chains and unpredictable consumer behaviour. The risks are clear, but uncertainties also open the door to growth and innovation. In this session, panelists will discuss the alternative data6 sources delivering actionable insights into geopolitical trends, empowering investors to leverage volatility for strategic advantage.
Alba Seoane Head of Data Research, Europe, Point72 Asset Management
Ian Morley Chairman, Wentworth Hall
Savvas Savouri Head of Macro, Chief Economist & Strategist, Quantmetriks Research
Nick Greenstock CEO, Gatehouse Advisory Partners
Question: With anticipated increases in European aerospace and defense spending, how can investors monitor government allocations and assess localized economic impact (e.g., jobs created near manufacturing sites)? Can this be overlaid with consumer brand and retailer exposure to identify companies best positioned to benefit? Conversely, is it possible to gauge public sentiment and taxpayer willingness to support this spending trend?
Shark Tank: New vendor showcase
11:00 - 11:40 | Leadenhall Hub | Data buyers
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.
Paul Mann Director of Business Development, Zeki Data
Carolyn Ryan Chief Strategy Officer, Trustpilot
Ariel Duarte López Director of Data Science, Acuity Trading
Question: Good luck to the showcase participants. Can you highlight the strongest investment use cases, your methodology (including compliance considerations), and how your offering differs from competing datasets?
Quantext: Invest trends in textual and alternative quant data
11:00 - 11:20 | Threadneedle Hub | Data buyers
Aditya Sharma Director, Alpha Signals, S&P Global Market Intelligence
Question: Since the release of GPT-3, a plethora of services using LLMs to explain earnings calls and earnings release statements have come to market. Have investor sentiment signals from earnings calls and earnings releases reached the point of being a coincident signal in quant models, explaining bets more so than alpha? Or does the post-earnings share price drift still happen?
A real China: From space to insight
11:20 - 11:40 | Threadneedle Hub | Data buyers
This session will examine how alternative data sources can address the global research gap in Chinese markets, alleviating institutional investors' data pain points. Skysight Technologies will showcase how use of dynamic satellite imagery technology to conduct real-time tracking and monitoring, can provide decision-making support for financial investment institutions.
Julie Liu Chief Marketing Officer, Skysight Technology
Question: Monitoring large construction projects has been a proven use case for satellite imagery, while efforts like tracking fast-moving events (e.g., car counts) have been less reliable. What are some underappreciated but high-value applications of satellite-derived data?
Unlocking holistic digital trends across app and web
11:40 - 12:00 | Leadenhall Hub | Data buyers
This session will explore the advantages of leveraging both app and web data in tandem, when doing investment due diligence. Andrew will touch upon analysis that utilises Sensor Tower's True Audience Estimate, which deduplicates users across web and app to uncover one true digital user figure.
Andrew Sprague Head of Investor Vertical, Sensor Tower
Question: Could you walk us through your deduplication methodology for unifying app and web data across devices? What caveats should investors keep in mind when merging these datasets?
Alternative perspectives for private equity
11:40 - 12:20 | Threadneedle Hub | Keynote
2024 saw major activity in the private markets space. With rebounding valuation levels, the continued expansion of AI and an expected M&A surge, 2025 brings no signs of slowing down. This session will explore how private equity firms are using alternative data sources to better inform new strategies, from deal sourcing to diligence and value creation.
Jon Steinberg Founding Partner, Mountside Ventures
Aman Aneja Director of Analytics, Fairview Equity Partners
Steven Millar Senior Manager, PWC Deals Analytics UK
Michael Hejtmanek Vice President, Corporate Solutions, Neudata (Moderator)
Question: What changes have successful data companies made to serve the distinct needs of private market buyers versus public market buyers?
Transaction data: Tracking grocery and general merchandising retailers
12:00 - 12:20 | Leadenhall Hub | Keynote
Finn Cousins Research Analyst, Neudata
Question: How should data be combined to better understand revenue growth drivers in grocery and general merchandise retail? For example, some use transaction size as an inflation proxy, which requires a lot of assumptions (e.g., constant basket size, price mix, and geography mix). How would you approach this challenge using multiple datasets?
Patterns and predictions: Unlocking the power of consumer data
12:20 - 12:50 | Leadenhall Hub | Keynote
Consumer transactional and behavioral data remains one of the most popular and widely deployed categories of alternative data, providing indicators for both short-term trading decisions and long-term valuations. This panel will explore the key dataset types helping investors to understand and react to current market trends, whilst predicting future performance and economic shifts.
Aditi Sawhney Specialist Data Science Lead, Man Group
Suraj Gohil Co-Founder and Chief Commercial Officer, Fable Data
Nicholas Neary Senior Data Scouting Analyst, Neudata (Moderator)
Question: Can you share some best practices for sophisticated asset managers to follow when playing the prediction game within the prediction game? By this I mean some data points move the market when released, affecting perceptions of future results, so some investors try to predict the data released by the data vendor with other data sources. And then there are times when the released data point is not the right read versus the real future company results, so market participants also attempt to predict the real result and arbitrage the difference.
From data to alpha: Streamlining the data management lifecycle
12:20 - 12:50 | Lothbury Library | Data buyers
This session will delve into the challenges funds face in managing the data lifecycle, from cataloging, to trialing, budgeting, governing and migrating data. Panelists will consider ways to streamline workflows, adapting to evolving regulatory demands whilst maintaining a competitive edge. Featuring an introduction to Neudata's new SaaS platform, Navigator.
Rado Lipuš Founder and CEO, Neudata
Ruairi Powers Senior Vice President, Product Manager, Neudata
Dean Gray Head of Design, Neudata
Question: Based on your work with the buy-side, how would you benchmark the range of capabilities in managing the data lifecycle from lagging to leading? What are the typical signs of lagging and leading capabilities?
Lunch and learn: How investors use gig mobility data to understand consumer behaviour
1:00 - 1:40 | Exhibition Area | Data buyers
The gig economy is set to surpass $873 billion by 2027, marking it as one of the fastest-growing sectors globally. Yet insights on gig mobility remain largely inaccessible and fragmented. In this session, we’ll explore how investors are harnessing gig mobility data to gain a differentiated view of the market and uncover insights into the performance of hundreds of publicly traded and thousands of privately held companies worldwide.
Ryan Green CEO, Gridwise
Question: How does Gridwise address the challenge of fragmented gig mobility data, especially given the limited transparency from major ride-hailing and delivery platforms?
The multimodal economist - A worldwide case study
1:50 - 2:10 | Leadenhall Hub | Keynote
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?
How investors are using alternative data to track industrials companies
2:10 - 2:30 | Leadenhall Hub | Keynote
This session will outline global trends impacting the industrials sector, including trade policies, geopolitics and supply chains. What are the common challenges faced by investors looking to track this industry and which dataset types are growing in popularity and impact?
Matt Yome Research Analyst, Neudata
Question: Beyond tracking supply chain shifts, how can industrial data be used to assess whether productivity is improving—potentially offsetting other pressures on output?
ESG data in 2025: Declining demand or more relevant than ever?
2:30 - 2:50 | Leadenhall Hub | Keynote
The continued rise of ESG investing has fueled a new branch of alternative data, but despite growing popularity, ESG policies and practices continue to attract criticism from many. This session will consider some of the problems with ESG data, look at the current regulatory landscape and highlight the potential advantages and use cases for risk mitigation and alpha generation.
Antti Savilaakso CRO, Impact Cubed
Question: ESG data, in my view, is just investment data. It’s not a box-ticking exercise. Could you share examples where ESG indicators flagged early risk or identified outperformers in a market?
The future of data through a quant lens
2:50 - 3:30 | Leadenhall Hub | Keynote
As technological advancement continues to have a profound impact on the evolution of quantitative investing, how are funds leveraging alternative data to deliver alpha and manage risks? What challenges do quants face in sourcing and integrating new datasets? How can artificial intelligence optimise investment strategies and where does human oversight remain essential?
Kristina Usaite Senior Quantitative Researcher, Robeco Investment Management
Florian Koch Quantitative Researcher and Portfolio Manager, Lynx Asset Management
Vik Bansal Systematic Portfolio Manager, Centiva Capital
Sophie Beland Head of Systematic Advisory Sales - EMEA, Morgan Stanley (Moderator)
Question: Building on the panel's strong framing—how might synthetic data7 generated by AI be used to stress-test models more effectively? Conversely, could it amplify the flaws in existing training datasets?
Why macro-quantamental data matters in an age of uncertainty: A back-to-basics approach
4:00 - 4:20 | Leadenhall Hub | Keynote
Lasse de la Porte Simonsen Director of Systems and Advanced Analytics, Macrosynergy
Question: I wish I could be in London to hear the presentation. This is a critical moment to revisit how we apply historical frameworks to evolving macro conditions. In that context, how do you approach the first principles of macro-quantitative8 investing, especially when the underlying causal forces of globalization (post–Berlin Wall) may no longer hold in an era of national protectionism? How might this shift challenge the validity of leading indicators9 that worked reliably over the past 30 years? Or does a first-principles approach suggest these frameworks can still hold, regardless of structural regime change?
AI-Washing in focus: Reviewing the impact of artificial intelligence
4:20 - 5:00 | Leadenhall Hub | Keynote
Amid the AI-boom, some companies have faced accusations of ‘AI-Washing’, or falsely overstating the use of Artificial Intelligence to capitalize on the attention this trending technology attracts. How can alternative data help to expose AI innovation vs hype? Where is AI having a tangible impact on data optimisation in the investment sector? What shifts are we seeing in the regulatory landscape?
Sam Livingstone Head of Quantitative Strategies & Risk, Ambienta Public Markets
Sanne de Boer Head of Quantitative Equities, Voya Investment Management
Timothée Consigny Chief Technology Officer, H20 Asset Management
Bhavin Kapadia Senior Advisor, Financial Services (Moderator)
Question: Great questions in the summary! As a follow-up, how can due diligence questionnaires help uncover “AI-washing”? What are the telltale signs that AI isn’t meaningfully embedded in a product? Or more importantly, how is AI actually embedded and does it meet responsible AI standards in order for an asset manager to have trust in purchasing the product?
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.
- Jason DeRise, CFA
DDQ (Due Diligence Questionnaire): A standardized document used by data buyers (especially institutional investors) to assess a vendor’s compliance, privacy practices, methodology, and business model.
GPUs: An acronym for "Graphics Processing Units." These are specialized electronic circuits designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device.
Buy-side typically refers to institutional investors (Hedge funds, mutual funds, etc.) who invest large amounts of capital.
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.
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.
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.
Synthetic Data: Artificially generated data that is created rather than obtained by direct measurement, used primarily to train machine learning models where real data may be incomplete or sensitive.
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).
Leading Indicator: A leading indicator is a measurable set of data that may help to forecast future economic activity. Leading economic indicators can be used to predict changes in the economy before the economy begins to shift in a particular direction. They have the potential to be useful for businesses, investors, and policymakers. https://www.investopedia.com/terms/l/leadingindicator.asp
Jason, thank you - these posts always make me stop my D2D work and see what I am missing or need to reinforce.