Top Questions ahead of Eagle Alpha London Alternative Data Conference
Eagle Alpha’s Alternative Data Conference in London is on the 16th of May 2024. Here are the questions on my mind for the speakers.
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.
On 16 May, 2024, Eagle Alpha hosted their latest Alternative Data conference in London.
https://www.eaglealpha.com/2023/01/30/alternative-data-conference-london-2024/
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Reach out to Jordan Hauer at jordan@amassinsights.com with any additional questions (LinkedIn: https://www.linkedin.com/in/jordanhauer/).
There are three themes in the questions I would ask at the Eagle Alpha’s London Alternative Data Conference:
Integration of AI and machine learning1.
Data Usage.
Regulatory and compliance issues.
Vendor Best Practices Workshop (Vendors only): 08:00–08:30 (BST)
Mark Fleming-Williams, Head of Data Sourcing – Capital Fund Management
Connor Emmel, Director Financial Institutions – Apptopia
Questions: A key part of selling any product is understanding the jobs to be done with the product by the purchaser. However, the financial community is often unwilling to share their use of alternative data2 to avoid sharing any trade secrets. How can data companies more effectively discern how institutional investors3 are utilizing data to enhance their job performance?
Opening Remarks 08:45 - 09:00(BST)
Niall Hurley, CEO- Eagle Alpha
Questions: At the event in New York in January 2024, an interesting presentation was made about how AI agents could be orchestrated to enable analytics at scale. If you’re willing to share, how has Eagle Alpha incorporated generative AI4 into its products and internal processes?
Fireside Chat - Fueling the Future of Alt Data: Insights, Technical Challenges and the Power of Partnerships 09:10 - 09:30(BST)
Amy Young, Director Capital Markets Industry Advisor – Microsoft
Thomas Oliver, Director – Aladdin by BlackRock
Questions: In a world where AI agents assist knowledge professionals in obtaining quick answers, collaboration between technical and domain experts is crucial. How do you ensure that proprietary insights remain confidential within the domain experts' company while still leveraging technical expertise?
How To Build Your Own Database Technology - Perspectives from Man Group 09:30 - 09:45(BST)
James Munro, Managing Director – Man Group
Questions: Have recent technological advances shifted the balance between building in-house versus buying external solutions?
Macro & Inflation Insights - What Can Alt Data Do to Help Close the Gap with Central Banks 09:45 - 10:15(BST)
Dr. Lassen Simonsen, Director of Systems & Analytics – Macrosynergy
Saaed Amen, Co-founder – Turnleaf Analytics
Meghna Shah, Macro Strategist & Chief Economist – Macrobond
Questions: Which government-collected and modeled inflation data points are now considered flawed in light of new methodologies enabled by recent data and technological advances?
Consumer Transaction Data - Applications of AI/ML to Data Models & Implementing Synthetic Data5 Optimization to Transaction Data 01:15 - 01:45(BST)
John Roa, CEO – Anthology.ai
Fabio Manola, Co-Founder and Head of Operations – Fantix
Questions: How does integrating synthetic data with anonymized transaction data enhance model accuracy in predicting consumer behavior? How do you prevent models from overfitting6 with synthetic data, which might not fully represent real-world scenarios?
Note: I wish I were able to go to the event. This seems like fascinating discussion that leads to a deeper understanding of the possibilities and probably more questions.
Addressing the Blind Spots in Data Discovery - Where Next? 01:45 - 02:15(BST)
Mark Fleming-Williams, Head of Data Sourcing – Capital Fund Management
Olivier Beau de Loménie, Partner – Mergers & Acquisitions – West Monroe
Brendan Furlong, Head of Data Sourcing & Advisory – Eagle Alpha
Questions: Beyond immediate challenges in data discovery, what are the long-term challenges we should anticipate over the next 5 to 10 years?
Regulating the Digital Future: AI and Data Compliance Considerations in Europe 04:15 - 04:45(BST)
Prof. Patrick Van Eecke, Partner and Head of European Cyber, Data, and Privacy practice – Cooley LLP
Christophe Fichet, Partner – Bignon Lebray
Sanaea Daruwalla, Chief People and Legal Officer – Zyte
Questions: How might varying AI regulations across different regions impact the efficacy of European policies?
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
Machine Learning (ML): An application of AI that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.
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.
Institutional investors: professional investors, like mutual funds, pensions, and endowments (aka the Buyside), who invest the money of others on their behalf. This is different from a retail investor, who is an individual or nonprofessional investor who buys and sells securities through brokerage firms or retirement accounts like 401(k)s.
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.
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.
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.