Top Questions: Neudata’s San Francisco Data Summit

Neudata is hosting its latest data conference in San Francisco on September 26th. Here are the top questions for the panelists and presenters.

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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. 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 and creator of data, I have gained a unique perspective, which I am sharing through the newsletter.

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Three themes from my questions ahead of the conference

Neudata hosts its San Francisco Data Summit on September 26th. Here are the top 3 themes from the questions I provided for each panel and speaker below.

1. The Evolution and Impact of Alternative Data¹

2. Navigating Challenges in Data Sales, Delivery, and Licensing

3. The Intersection of AI and Data Monetization

Here is the link to the event: https://www.neudata.co/events/san-francisco-data-summit-2024

Opening remarks

8:50 - 9:10 Golden Gate Stage

Ian Webster Managing Director, Neudata

Question: Across the agenda today, alternative data is discussed across multiple types of institutional investors. What are the biggest opportunities for data company growth, and what obstacles must be overcome for success?

Keynote: Alpha extraction – Hedging equity portfolios efficiently

09:10 - 9:40 Golden Gate Stage

Morgan Stanley’s Quantitative Investment Strategies (QIS) Research team will compare different approaches to isolate the pure alpha² in stock selection portfolios. They will detail their hedging of systematic exposures by shorting market beta³, factor⁴ portfolios, or individual equities.

Aris Tentes Executive Director, QIS Research, Morgan Stanley

Question: Are systematic⁵ investors expanding alternative data inputs to drive demand, or are they replacing older inputs, driving market share shifts within the alternative data industry?

Addressing challenges in data sales and delivery: A practical workshop

8:50 - 9:30 Lombard Library

This exclusive, interactive, workshop for data providers brings together commercial practitioners to discuss and address the challenges in modern data sales and delivery. Together, we will learn first-hand how Dewey solves many of these common problems using technology, before we try our own hand at using Amplify.

Karthik Kumar Co-Founder & CEO, Amplify Data

Evan Barry Founder & CEO, Dewey

Question: Which aspect of a data buyer's “jobs to be done⁶” is most important yet causes the most dissatisfaction with data vendors? Some examples could be data discovery, assessing data use cases, understanding data integrity, accessing data, and assessing the cost.

The new data buyers on the block

9:40 - 10:10 Golden Gate Stage

Today, every major tech company is dedicating resources to breakthroughs in artificial intelligence. With the race to stand out firmly on, how are those at the forefront of the AI revolution looking to incorporate unique datasets to differentiate their offerings? This session will explore the most sought after data types for leading AI businesses and the potential for novel datasets to have a transformative impact in the field.

Daryl Smith Head of Research, Neudata (Moderator)

Margaret Quigley Head of Data Acquisition, Ex Cohere

Mikayel Khachatryan CEO, Wirestock

Aashmeet Kalra Principal Technologist, Google

Barry Dauber VP of GenAI GTM, Databricks

Question: How should data companies approach licensing terms in the Generative AI⁷ era? Do prior best practices still apply, or are new approaches needed for Gen AI use cases?

Keynote: How to make money with Generative AI

10:10 - 10:30 Golden Gate Stage

A clear AI strategy has become critical to success for businesses both selling and buying data. How can data providers leverage AI to increase revenue, through data generation and enrichment? What about concerns around anonymization and privacy? How can GenAI optimize spend for businesses buying data, through improved quality assurance, predictive analytics and combining synthetic with existing data?

Peter Danenberg Senior Software Engineer, Google

Question: What best practices ensure explainability and validation of generative AI solutions in production?

The future of private market investing

10:30 - 11:00 Golden Gate Stage

In this session, panelists will gaze into the future of private market investing, assessing the current state of the market and the outlook for 2024 and beyond. Join experts as they scan the horizon for new and exciting ways alternative data can play a role in advancing private market investing, as firms both invest in data companies and leverage data themselves.

Adam Nahari Partner, Head of Data Science, Pinegrove Capital Partners (Moderator)

Tony Ho Head of Data Infrastructure, General Catalyst

Hem Wadhar VP, Data Science, Sequoia Capital

Jerry Ye Partner, Ex Lightspeed Venture Partners

Question: What is the largest opportunity for alternative data in private markets: due diligence, performance monitoring, exit strategy assessment, or converting operational data into actionable insights?

Data monetization seminar & networking (pre-registration required)

10:30 - 11:30 Lombard Library

This exclusive workshop will bring together a dynamic group of data owners and data buyers to explore strategies and opportunities for data monetization. Through guided roundtable discussions, participants will have the opportunity to ask questions, gather invaluable intelligence and form connections within an closed networking environment.

Ian Webster Managing Director, Neudata

Henry Scherman Data Monetization Consultant, Neudata

Michael Hejtmanek Vice President, Corporate Solutions, Neudata

Question: What pricing strategies are most effective for data companies to scale beyond early adopters?

Navigating uncertainty: Intelligence-driven strategies for investment resilience

11:50 - 12:50 Lombard Library

Join Deloitte's Intelligence Services Centre with guests from Bloomberg to explore how leading organizations are leveraging intelligence to build resilience, moving beyond traditional, reactive methods to anticipate and navigate disruption. Discover how a proactive, intelligence-led approach can safeguard your business, your investments, and unlock new opportunities in an uncertain world.

Jonathan Daglish Senior Manager, Intelligence Lead, Deloitte

Guy Hewetson Senior Manager, Resilience Lead, Deloitte

Manish Motiani Managing Director, Strategy & Analysis Practice Lead, Deloitte

Shannon Pym Senior Manager, Technology Lead, Deloitte

Matthew Ekroth EMEA Corporates Supply Chain and Data Strategy Lead, Bloomberg

Question: While there are likely innovative use cases for intelligence data in investing, it could be useful to discuss the types of intelligence strategies that are not appropriate for investment use cases due to the risks of MNPI (material non-public information)⁸ or PII (personal identifable information)⁹ exposure. Could you share some ways to avoid those risks?

Lunch and learn: Sourcing and using data to track B2B businesses

1:00 - 1:40 Lombard Library

This session will explore alternative data approaches to tracking underserved equities, such as media, advertising and software.  Discover how Oxford DataPlan applies it’s proprietary data sources and algorithm to deliver daily KPI predictions on companies like Shopify, Monday.com, Meta, Alphabet, IPG and Reddit.

Nico Hoff Head of Revenue, Oxford DataPlan

Question: How do you think about ground truth to confirm the data insights are valid when dealing with smaller companies without sell-side companies to sanity check the output?

The data difference: Essential insights for running a DaaS company

2:00 - 2:20 Golden Gate Stage

Jonathan Chin Co-Founder, Head of Data & Growth Strategy, Facteus

Lorn Davis Head of Product & Corporate Strategy, Facteus

Essential for entrepreneurs and executives looking to build, run or invest in successful data companies, this session will explore how data-centric business models differs from traditional software models, in marketing, product development and other key areas.

Question: What were some of the early signs you had product market fit at Facteus? What pivots were needed along the way?

Showcase: What's new in alternative data?

2:20 - 2:50 Golden Gate Stage

This session will showcase some of the newest vendors, datasets and product launches taking the alternative data industry by storm. Ensure you keep on top of the latest updates and case studies to inform your future data acquisition strategy.

Pouya Taaghol CEO, Big Data Federation

Kyle Henderson CEO & Co-Founder, Vizion

Taylor Lowe Co-Founder and CEO, Metal

Question: In these types of new data source discovery sessions at conferences, the questions I value being answered are questions about the sourcing of data from a compliance point of view and then understand the potential wide range of use cases for the data product.

Alternative data trends and applications for private equity firms

2:00 - 2:20 Lombard Library

Hear about some of the hottest trends in the private equity sector and how alternative data applies in each instance. What current and future opportunities should private equity firms be on the lookout for when it comes to leveraging alternative data?

Keava Low Research Analyst, Neudata

Question: At which phase of the private equity lifecycle is alternative data being adopted most widely?

From monitoring to monetization - The data-driven secrets to value creation

2:20 - 2:50 Lombard Library

For ongoing portfolio management, alternative data proves invaluable. Which datasets are enabling private equity firms to monitor and make informed decisions about their current investments? How significant is the trend towards monetization of data assets within portfolio companies?

Michael Hejtmanek Vice President, Corporate Solutions, Neudata (Moderator)

Chad Gray Vice President, Data Science, Silver Lake

Cathy Tanimura Vice President, Analytics and Data Science, Summit Partners

Question: In addition to these great questions, how open are portfolio companies to monetizing their data, and what factors help them feel comfortable with this process?

New horizons in quantitative investing: The latest data trends

2:50 - 3:30 Golden Gate Stage

Quantitative investment funds have been faced with notable changes to the data market in the last 5-10 years. This session will explore the current trends in alternative data usage. What dataset types have grown in availability and popularity? What challenges do quantitative investors face in integrating new data? Does data supply meet demand?

Todd Schmucker Data Strategy Executive, ex Walleye Capital (Moderator)

Ben Cohen Director - Global Head, Data Strategy, WorldQuant

Stewart Stimson Head of Data Strategy, Jump Trading

Artemiza Woodgate CIO, Founding Partner, Integrated Quantitative Investments

Tony Berkman Managing Director, Two Sigma

Question: Great questions in the summary already. It should be a great panel as a highlight of the conference, given the experience and talent on the panel. Maybe one more for the panel: Thinking about the quant industry as a whole, are systematic investors finding more incremental alpha from new data sources or from refining models to better utilize existing data?

The vital role of Alternative Data in the Healthcare sector

3:30 - 3:50 Golden Gate Stage

Investment managers relying on US claims data have seen significant turbulence in 2024. What are the alternatives to Open Claims data and how can data buyers diversify their sources? What can and should both vendors and investors do to mitigate against future cyber attacks?

Keava Low Research Analyst, Neudata (Moderator)

Julia Fitzgerald Group Product Director, Earnest Analytics

Question: One of the common challenges with healthcare data is the lagged nature of anonymized, granular data, while aggregated industry-level healthcare data has less of a lag, but it’s aggregations limit the scope of use. Have there been developments to provide both fresh and granular healthcare data?

Is alternative data the route to your next successful deal?

2:50 - 3:30 Lombard Library

A growing number of private equity and venture capital firms are leveraging alternative data to identify and evaluate investment opportunities. How can new datasets better pinpoint undervalued assets and emerging market segments to support deal origination efforts? How can alternative approaches to data enhance the speed and accuracy of due diligence processes?

Amanda Block Head of Account Management, Neudata (Moderator)

Tiago Caruso Co-Founder and CEO, Neural Sourcing

Amanda Widjaja VP, VP, Data Science & Engineering, Norwest Venture Partners

Claire Saint-Donat Head of Data Science & AI, Bain Capital

Le Michael Song Vice President, Data Science, Advent International

Questions: more great questions already in the summary! Maybe one more would be how can data companies adjust their products to make the due diligence process easier and more cost-effective from a data point of view?

Case studies in private equity: What can alternative data deliver?

3:30 - 3:50 Lombard Library

What options are available to private equity firms looking to leverage non-traditional data to unlock a market edge? This session will explore instances where firms enlisted the services of consulting giants and consider whether similar results could have been more easily achieved through direct engagement with an alternative data provider?

Barney Bruce-Smythe Senior Associate, Neudata

Question: Are data companies exploring partnerships with the consulting giants as a way to offer the best of both worlds to private market due diligence?

Neudata Scout demonstration

4:20 - 4:40 Lombard Library

Data buyers

Ian Webster Managing Director, Neudata

Question: Beyond the newest features of Neudata Scout, which earlier features are overlooked but add significant value?

Neudata: Ask me anything

4:40 - 5:20 Lombard Library

Barney Bruce-Smythe Senior Associate, Neudata (Moderator)

Ian Webster Managing Director, Neudata

Daryl Smith Head of Research, Neudata

Michael Hejtmanek Vice President, Corporate Solutions, Neudata

Question: What synergies do expect Neudata customers to see from Neudata’s recent launch of the Neudata Ranger, which now provides coverage of market data sources?

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What questions would you ask? Leave a comment below.

If you think this is useful for someone attending the conference, please feel free to forward it on.

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- Jason DeRise, CFA

1. 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.

2. Alpha: A term used in finance to describe an investment strategy's ability to beat the market or generate excess returns. A simple way to think about alpha is that it’s a measure of the outperformance of a portfolio compared to a pre-defined benchmark for performance. Investopedia has a lot more detail: https://www.investopedia.com/terms/a/alpha.asp

3. 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.

4. Factor investing: an investment approach that involves targeting quantifiable firm characteristics or “factors” that can explain differences in stock returns. Security characteristics that may be included in a factor-based approach include size, low-volatility, value, momentum, asset growth, profitability, leverage, term and carry. https://en.wikipedia.org/wiki/Factor_investing

5. Systematic Fund: 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).

6. Jobs To Be Done: A theory and methodology for understanding customer motivations and needs in business and product development, based on the idea that customers "hire" products or services to fulfill specific jobs.

7. 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.

8. Material, Non-Public Information (MNPI): Information about a company that is not publicly available and could have a significant impact on the company's stock price if it were made public.

9. Personal Identifiable Information (PII): Any information that can be used to identify an individual, such as a name, social security number, address, or phone number.