Looking Back and Looking Forward
It’s been one year since The Data Score, composed by DataChorus, became available. Let’s look back at the past 12 months and preview what’s ahead.
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.
For the first part of my career, I was a sellside1 analyst who wrote for a living, helping institutional investors2 make better decisions in the consumer sector. I really enjoy writing to share insights and help others. There was a moment at Syracuse University where I thought I would pivot between degrees in finance and accounting, and my minor in economics. I thought I would switch to teaching, which my parents talked me out of. Yet, it seems I have found a way to both teach and be a financial professional.
Then, as the second part of my career, I became a data person. With the benefit of hindsight, I think I was always a data person. I took pride in my ability to create data products and platforms that enabled many other investors to generate success. The medium for helping others was insight-ready metrics, SQL, dashboards, and, of course, Excel. Sure, there were regular webinars and e-mail content highlighting insights possible with the data products, but I missed the act of writing.
So when I first decided to create DataChorus and its flagship newsletter, The Data Score, I began with an audience of one. I wrote what I would find interesting at the intersection of business, finance, data, and technology (and sprinkled in some music analogies). Since I was just writing for myself, I set an “impossible” subscriber count target for when I would begin to think about this newsletter as something that could be monetized. How many people would really care about the intersection of data-driven decision-making, data, and technology… and especially be willing to consume it in a long-form article format?
I never thought so many people would also be interested in my writing. I’m happy to share that I got to that “impossible” subscriber count a few months ago and am still seeing new subscribers. The subscriber base now includes key figures in the investment and data sectors, actively shaping the industry's trajectory.
Less amusingly, an esteemed figure in the data community dubbed me a 'Data Influencer"—a label that made me wince. We settled on 'Data Expert' instead. Yet, this sparked a thought: what if there was a TikTok rendition of The Data Score?
Here are some additional questions that are on my mind (which I’m also asking you):
The best of the last 12 months of The Data Score
There are a few key audiences that have shared positive feedback over the past year:
Data Product Creators
Data-Driven Decision-Makers
AI Explorers
Since the audiences are slightly different in their interests, I’ve shared the most popular articles aligned with outcomes needed by each of these groups.
Top 3 Data Score articles for Data Product Creators
Title: Why Some Data Companies Struggle to Sell to the Financial Markets
Headline: The data deluge: Navigating the challenges of selling valuable datasets to the financial industry
Date: 18 April, 2023
ChatGPT summary of the article: In this edition of The Data Score newsletter, Jason DeRise discusses the complex landscape of selling data to financial markets, highlighting the challenge of matching the intricate needs of asset managers with the offerings of data providers. With over 21,000 data sources available, the article emphasizes the importance of easy-to-use datasets and the need for data companies to deeply understand the financial market's outcomes to succeed. It addresses the gap between the capabilities of top asset managers and the broader market, stressing the necessity for data providers to align their products more closely with client needs to unlock the true value of data.
Link: Why some data companies struggle to sell to the financial markets
Title: 8-Point point approach to evaluating data partners
Headline: The first pitch and demo look great. But then we look under the hood…
Date: 3 May, 2023
ChatGPT summary of the article: In this edition of The Data Score newsletter, Jason DeRise offers a meticulous 8-point approach for evaluating data partners, focusing on due diligence, ROI3 assessment, common sense validation, backtesting, transparency, feedback responsiveness, competitive positioning, and post-delivery service. DeRise's strategy is rooted in his extensive experience and emphasizes the importance of a thorough vetting process to ensure data integrity, relevance, and utility. This guide serves as a comprehensive framework for organizations seeking to navigate the complex terrain of data partnership with confidence and discernment.
Link: 8 point approach to evaluating data partners
Title: Don’t Bore Us—Get to the Chorus!
Headline: Lead with conclusions in your data marketing content to drive data discovery and usage, demonstrate data integrity, and support data monetization4.
Date: 25 March, 2024
ChatGPT summary of the article: In "Don’t Bore Us—Get to the Chorus!", Jason DeRise emphasizes the importance of engaging and concise data marketing content to capture the attention of time-constrained audiences. He suggests treating content like a product that delivers value, creating compelling hooks, simplifying complex information, and measuring content performance for adaptation. DeRise's advice centers on leading with conclusions, understanding audience needs, and effectively communicating data's relevance and integrity to drive discovery, usage, and ultimately, monetization.
Link: Don’t Bore Us—Get to the Chorus!
Top 3 Data Score articles for Data-Driven Decision-Makers
Title: A Different Approach to Revenue Estimates, Leveraging Alternative Data
Headline: Explore a fresh approach to revenue forecasting using alternative data5, by focusing on the customer journey rather than short-term trends
Date: 24 May, 2023
ChatGPT summary of the article: Jason DeRise's article in The Data Score explores a novel approach to revenue forecasting by utilizing alternative data through the lens of the customer acquisition funnel, moving beyond traditional transaction data. By examining customer behavior from awareness to loyalty, DeRise advocates for a broader, more creative application of alternative data, offering insights into long-term revenue trends and enabling more nuanced investment strategies.
Link: A Different Approach to Revenue Estimates Leveraging Alternative Data
Title: NVIDIA: How could alternative data be used to assess its long-term potential?
Headline: Discover how alternative data can reveal insights on NVIDIA's future, paving the way for smarter investment strategies and unearthing fresh insights in the GPU6 market.
Date: 15 June, 2023
ChatGPT summary of the article: Jason DeRise's exploration in The Data Score utilizes NVIDIA's journey to delve into how alternative data can be harnessed to predict long-term corporate prospects, focusing on GPU market dynamics, competitive landscapes, and innovation in technology sectors. This analysis underscores the potential of non-traditional data to enrich investment strategies and understand complex market trends.
Link: NVIDIA: How could alternative data be used to assess its long-term potential?
Title: Assessing the ROI of Data
Headline: Raw data is a cost; insights have value.
Date: 3 January, 2024
ChatGPT summary of the article: In "Assessing the ROI of Data," Jason DeRise emphasizes the critical examination of data investments for generating actionable insights, highlighting the necessity of transforming raw data into insight-ready information. The piece underscores the hidden costs associated with free data, the importance of seamless data integration, and the substantial value derived from insights that significantly impact decision-making. DeRise advocates for a meticulous approach to evaluating data's return on investment, considering its breadth, depth, and limitations, to ensure its effective contribution to economic outcomes.
Link: Assessing the ROI of Data
Top 3 Data Score articles for AI Explorers
Title: Blending AI and Human Creativity: Generative AI and Content Strategy
Headline: A Benchmark Approach: How I Use Generative AI7 in the Creative Process. (A bonus Data Score Newsletter Entry for the long-weekend).
Date: 28 May, 2023
ChatGPT summary of the article: In "Blending AI and Human Creativity: Generative AI and Content Strategy," Jason DeRise shares his process of integrating generative AI into his content creation for The Data Score newsletter. Despite AI's capabilities, DeRise emphasizes the importance of starting drafts personally to maintain depth and impact. He leverages AI tools like ChatGPT for summarizing, clarity, and editorial suggestions, enhancing the content without losing his unique voice and style. This approach underlines the symbiotic relationship between human creativity and AI, suggesting that while AI can augment the content creation process, the human element remains irreplaceable for producing insightful and engaging content.
Link: Blending AI and Human Creativity: Generative AI and Content Strategy
Title: Is ChatGPT Getting worse? A Case Study on Confirmation Bias
Headline: Is ChatGPT-4 getting worse? You already know the answer you believe and if you don’t pause to catch the inherent biases associated with being human, you may miss what the evidence actually shows.
Date: 20 July, 2023
ChatGPT summary of the article: In "Is ChatGPT Getting Worse? A Case Study on Confirmation Bias8," Jason DeRise delves into the evolving performance of ChatGPT-4, urging readers to question their biases before drawing conclusions. He highlights the complexity of AI behavior over time and the necessity for ongoing performance testing and model explainability. DeRise critiques a study suggesting ChatGPT's degradation, emphasizing the importance of precision and recall metrics in evaluating AI models. He cautions against the swift acceptance of findings that confirm pre-existing beliefs, underscoring the human tendency towards confirmation bias. This newsletter entry advocates for a more nuanced approach to understanding AI advancements, encouraging a balance between skepticism and open-minded analysis.
Link: Is ChatGPT Getting worse? A Case Study on Confirmation Bias
Title: When AI Forgets: The Hidden Pitfalls of Customizing Gen AI Models
Headline: I built a custom ChatGPT "Data Score Newsletter Editor" model. It was going well but then things got interesting.
Date: 4 March, 2024
ChatGPT summary of the article: Jason DeRise's journey with a custom ChatGPT "Newsletter Editor Model" for The Data Score highlights the potential and challenges of personalized AI. Initially boosting productivity, the model later encountered issues, possibly due to OpenAI's updates causing 'catastrophic forgetting9.' [Edit: The article is about potential catastrophic forgetting for my custom model, not OpenAI’s model]. DeRise's troubleshooting process underscores the importance of continuous monitoring and adaptability in using generative AI tools, reflecting on the broader implications for data professionals managing AI applications.
Link: When AI Forgets: The Hidden Pitfalls of Customizing Gen AI Models
What’s ahead of The Data Score Newsletter?
Over the next year, our topics will naturally evolve, building on the foundation laid in the previous twelve months.
For data product creators, we will explore topics to help improve product/market fit10, including opportunities to clarify the typical approach data-driven decision makers follow, especially in the institutional investment space.
For data-driven decision-makers, I hope to bring more interviews that show how insight-ready data is being used to make a difference and spark new ideas within your own data practice. Expect a few deep dives into topics about the best practices for turning raw data into insights.
For the AI explorers, the topics will likely pivot from the discovery of AI applications to their implementation in production. Despite all the rhetoric about what’s possible with AI, the enablement will be the core principles of computer science applied to new applications. AI may look like magic to the non-technical, but it’s the tried-and-true data practices that make it possible to trust AI driven output at scale.
Plus, where possible, I’ll provide my questions for conference participants ahead of time.
- Jason DeRise, CFA
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. Buyside typically refers to institutional investors (Hedge funds, mutual funds, etc.) who invest large amounts of capital.
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.
ROI (Return on Investment): a performance measure used to evaluate the efficiency or profitability of an investment or compare the efficiency of a number of different investments. ROI tries to directly measure the amount of return on a particular investment, relative to the investment’s cost. https://www.investopedia.com/terms/r/returnoninvestment.asp
Data Monetization: involves the process by which corporations that accumulate large quantities of data through their day-to-day operations, create an additional revenue stream. This is achieved by refining (cleansing and enriching) and structuring their data in such a way that it can be packaged and sold, often to external entities such as investment communities, for various purposes.
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.
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.
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.
Confirmation Bias: A tendency to search for, interpret, favor, and recall information in a way that confirms one's preexisting beliefs or hypotheses.
Catastrophic Forgetting: A phenomenon in machine learning where a model, after being trained on new tasks, completely forgets the old tasks it was trained on. This is a significant issue in neural networks and an ongoing area of research in the development of AI models that can retain knowledge from previous tasks while learning new ones.
Product/Market Fit: The ability of a product to meet the needs of customers, generating strong and sustainable demand for the product. This term refers to the point at which a product or service has been optimized to meet the needs and preferences of its target market, resulting in strong customer satisfaction and retention. Achieving product/market fit is considered essential for the success of a startup or new product.