Financial Data Marketplaces

financial data
Evolution of Financial Data Marketplaces.


This paper explores the emerging trends reshaping financial data marketplaces, with a particular focus on innovations in data delivery, the proliferation of alternative datasets, the integration of Artificial Intelligence and Machine Learning (AI/ML) technologies, and the evolving regulatory landscape. Innovations in data delivery are revolutionizing how data is stored and transmitted, with advanced file formats and cloud-native data warehouses enhancing the speed and efficiency of these processes. The rise of alternative datasets, such as satellite imagery and social media sentiment, is providing novel insights into market trends and consumer behaviors, enabling more accurate and timely financial forecasting. Meanwhile, AI/ML technologies, especially the advent of Large Language Models, are creating unprecedented opportunities for new data-driven services. However, the increasing role of AI/ML also presents challenges, such as ensuring the availability and diversity of training data and addressing issues of fairness and bias in AI/ML models.


Financial Data Marketplaces The financial data marketplaces serve as a key medium for the exchange of financial information, offering a range of data from basic market prices to complex alternative datasets. Their growing importance in the financial industry has been propelled by increased data-driven decision-making and regulatory demands for transparency.


The economics of financial data marketplaces can be characterized by the interaction of supply and demand forces, the cost structure, and pricing strategies:

  1. Supply and Demand: Supply comes from financial institutions, data aggregators, and alternative data providers. Demand is driven by hedge funds, investment banks, fintech startups, and other financial players looking for insights.

  2. Cost Structure: Major costs include data acquisition, data cleaning and processing, platform maintenance, and compliance.

  3. Pricing Strategies: Pricing models include subscription-based, tiered access, pay-per-use, and bundled services.

Competitive Landscape

The competitive landscape of the financial data marketplaces is evolving with new entrants, technology advancements, and regulatory changes. We discuss the implications of these dynamics for incumbents and newcomers, as well as for the consumers of financial data.

Incumbents and New Entrants

Incumbents such as Bloomberg and Reuters have been dominant players in the financial data industry for decades. Their vast datasets, comprehensive services, and established customer bases give them considerable market power.

However, the sector has seen a surge of new entrants in recent years. Startups like Quandl and Intrinio have emerged, offering innovative services often at a lower cost. These new entrants tend to focus on specific niches, such as alternative data or API-based services, and have disrupted the market with novel business models.

Technology and Innovation

Advancements in technology have significantly impacted the competitive dynamics in the financial data marketplace. On the supply side, technology has lowered the barriers to entry by reducing the cost of data storage and processing. On the demand side, advances in AI and machine learning have increased the appetite for complex datasets that can be used to generate predictive insights.

In addition, the proliferation of API-based services has further changed the competitive landscape by enabling seamless integration of data into clients” systems, making data more accessible and valuable.

Regulatory Changes

Regulatory changes have introduced both challenges and opportunities. Data privacy laws, such as the GDPR in Europe and the CCPA in California, have increased the complexity and cost of compliance. At the same time, regulations like MiFID II in Europe have increased the demand for transparent and auditable data, creating opportunities for providers that can meet these requirements.

Alternative Data

As the financial sector continues to evolve, there’s a growing interest in various forms of non-traditional data that can provide additional insights into the financial performance and risks associated with investments. Alternative data can provide both leading and lagging indicators, depending on the specific type of data and how it’s used.

  • Leading Indicators: These are predictive and give information about future events. For example, social media sentiment analysis could be a leading indicator, as shifts in public opinion on a brand or product might foreshadow future sales trends. Similarly, geolocation data showing increased foot traffic to a particular store could predict a strong sales report in the next quarter.

  • Lagging Indicators: These confirm long-term trends or changes in patterns after the fact. For instance, credit card transaction data is often a lagging indicator, providing insights into spending habits after purchases have been made. Similarly, email receipt data provides insights into consumer behavior after transactions have been completed.

Alternative Datasets

Here are a few categories of alternative data that are gaining traction:

  1. ESG Data: ESG (Environmental, Social, and Governance) data provides insights into a company’s behavior along these three dimensions. This includes information about a company’s carbon footprint, its labor practices, its board diversity, and much more. This kind of data is becoming increasingly important for investors who want to ensure their investments align with their values, and for those who believe that companies with good ESG practices are less risky and have better long-term prospects.

  2. Sustainability Data: Related to ESG data, sustainability data focuses specifically on a company’s impact on the environment and its efforts to mitigate this impact. This includes data about emissions, waste management, water usage, energy efficiency, and the like.

  3. Climate Risk Data: As the impacts of climate change become more apparent, there’s a growing interest in data that can provide insights into how climate change might affect a company’s performance. This includes both physical risks (like the risk of a company’s facilities being damaged by extreme weather events) and transition risks (like the risk of a company’s products becoming less desirable as carbon pricing is implemented).

  4. Supply Chain Data: As the global economy becomes more interconnected, understanding a company’s supply chain can be crucial for assessing its financial risks. This includes information about a company’s suppliers, the geographical distribution of its supply chain, and any potential vulnerabilities (like dependence on a single supplier for a key component).

  5. Cybersecurity Risk Data: As more business operations move online, cybersecurity risks are becoming an increasingly important factor to consider when making investment decisions. Data about a company’s cybersecurity practices, its history of cybersecurity incidents, and its potential vulnerabilities can be crucial for assessing these risks.

A number of additional categories of alternative datasets are listed in the Appendix.

Innovations in Data Delivery

Depending on the use case, there are a number of new delivery mechanisms that are available today compared to the past. Here is a simple framework for choosing the file format for data delivery considering two dimensions – data size and processing.

File Formats

Small Scale Large Scale
Batch Processing CSV, SQL Parquet, Avro
Stream Processing APIs Kafka, Kinesis
  • Quadrant 1 (Small Scale & Batch Processing): This represents traditional, small-scale data delivery methods such as CSV or SQL files. Data is typically pre-processed and delivered in batches.

  • Quadrant 2 (Large Scale & Batch Processing): This represents large-scale data delivery for batch processing. File formats like Parquet and Avro come into play here, allowing for efficient batch processing of large datasets due to their columnar storage.

  • Quadrant 3 (Small Scale & Real-Time Processing): This is for small scale real-time data delivery, where APIs are commonly used. APIs provide a way to pull or push data in real-time, and are often used when data needs to be up-to-date and immediately accessible.

  • Quadrant 4 (Large Scale & Real-Time Processing): This quadrant represents the delivery of large amounts of data in real-time. Streaming services like Apache Kafka or Amazon Kinesis can handle these use cases.

Cloud Native Datawarehouses

Snowflake and BigQuery are cloud data platforms that support different ways of sharing data, including views, user-defined functions, and stored procedures. Here’s another framework that could be used to evaluate data sharing using these platforms, considering both the data access frequency and data volume:

Low Volume High Volume
High Frequency Views Materialized Views
Low Frequency Exports Data Sharing
  • Quadrant 1 (Low Volume & High Frequency): If the data volume is low and the frequency of access is high, using database views can be an efficient method. Both Snowflake and BigQuery support creating views on the data.

  • Quadrant 2 (High Volume & High Frequency): For larger data volumes that need to be accessed frequently, materialized views can be a better choice. These are precomputed views that store the result of a query and can provide faster query performance.

  • Quadrant 3 (Low Volume & Low Frequency): If both the data volume and access frequency are low, data can be exported into a file format such as CSV, and then shared or loaded into another system.

  • Quadrant 4 (High Volume & Low Frequency): For high-volume data that doesn’t need to be accessed frequently, dedicated data sharing services might be the best option. Snowflake, for example, has a “Data Sharing” feature that allows sharing large datasets without the need to copy or move the data. BigQuery also has similar capabilities using Authorized Views.


Even when it comes to APIs, we now have more options than in the past e.g. to decide between REST, gRPC, and GraphQL, we could use a simple framework based on data efficiency and data complexity.

High Data Efficiency Low Data Efficiency
Low Complexity GraphQL REST
High Complexity gRPC N/A
  • Quadrant 1 (High Data Efficiency & Low Complexity): GraphQL is a good fit. It allows clients to specify exactly what data they need, which can reduce the amount of data that needs to be transferred over the network.

  • Quadrant 2 (Low Data Efficiency & Low Complexity): REST fits well here. It’s straightforward to use and understand, but isn’t as efficient with data transfer as GraphQL or gRPC because it can lead to over-fetching or under-fetching of data.

  • Quadrant 3 (High Data Efficiency & High Complexity): gRPC is a good choice. It uses Protobuf by default, which is a very efficient way of serializing structured data. However, gRPC has a steeper learning curve and more complexity than REST or GraphQL.

  • Quadrant 4 (Low Data Efficiency & High Complexity): Generally, you want to avoid this quadrant, as you don’t want high complexity without the benefits of high data efficiency.

Additional considerations:

Network Conditions: gRPC supports HTTP/2, which is more efficient than HTTP/1.1 (used by REST and GraphQL) and allows for server push, header compression, and multiplexing (multiple requests/responses can be in flight at the same time, on the same connection).

Streaming Support: If you need server-side streaming, gRPC is a good choice. GraphQL only supports client-side streaming, and REST doesn’t have built-in support for streaming.

Artificial Intelligence and Machine Learning

Search and Discovery

Data consumers in financial data marketplaces often need to sift through large amounts of complex and heterogeneous data. AI and ML can enhance search capabilities, making data discovery more intuitive, effective and efficient. For example, ML algorithms can help with:

  • Query Understanding and Expansion: LLMs can better understand user queries by capturing the semantics of the search phrase. They can also help expand or refine the search query based on the context, leading to more accurate search results.

  • Semantic Search: LLMs can enable semantic search by understanding the intent behind the user’s query. They can return results not just based on keyword matches but also the context and meaning of the query.

  • Intelligent Recommendations: LLMs can be used to build sophisticated recommendation systems. By understanding a user’s past interactions, preferences, and queries, these models can suggest relevant datasets that the user might be interested in.

  • Interactive Conversational Interfaces: LLMs can power conversational interfaces (like chatbots) for data discovery. Users can interact with these bots in natural language to find the data they need, making the discovery process more interactive, user-friendly and accessible to non-technical users as well..

Data Processing and Information Extraction

Artificial Intelligence (AI), Machine Learning (ML), and Large Language Models (LLMs) like GPT-4 have immense potential in improving data processing and information extraction. Here’s how:

  • Anomaly Detection: ML models can identify anomalies in data that may signify important financial events, such as fraud, a sudden market shift, or an outlier performance by a company.

  • Feature Extraction: ML models can be used to identify and extract valuable features from large, complex financial datasets. This can help highlight key insights or patterns that might be missed in manual analysis.

  • Text Extraction and Classification: LLMs, with their ability to understand and generate human-like text, can be very effective in processing unstructured textual data like financial reports, news articles, or social media posts. They can extract key pieces of information, classify them into different categories, or even detect sentiment, all of which can add value to the raw data.

  • Topic Modelling: LLMs can perform topic modelling on financial text data, grouping documents into different topics, which can help users quickly find relevant reports or articles.

  • Automated Data Cataloging: AI can automatically catalog new data that enters the marketplace, assigning it appropriate metadata and tagging, making it easier for consumers to discover.

  • Event Extraction: LLMs can extract events from financial news or documents, such as mergers, acquisitions, product launches, or executive changes, providing users with valuable context for investment decisions.

Creation of new data-driven services

AI/ML technologies also enable the creation of new data-driven services

  • Sentiment Analysis: LLMs can analyze the sentiment in financial news or social media posts, providing indicators of market trends. This can be particularly useful for traders and investors looking for market sentiment insights.

  • Risk Assessment: AI can analyze a vast amount of data to provide risk assessments for different investments, taking into account factors like market trends, company performance, economic indicators, and even geopolitical events.

  • Question Answering: LLMs can be used to build a question-answering system where users can ask financial queries in natural language and get accurate responses. For example, a user might ask, “What were the top-performing tech stocks last quarter?” and the system would provide a list based on the data available in the marketplace.

  • Real-time Market Commentary: LLMs can generate real-time market commentary, interpreting data from live feeds to provide updates on market trends, significant movements, and breaking news.

  • Contract Analysis: LLMs can analyze complex financial contracts or agreements, extracting key terms and conditions, obligations, rights, penalties, etc., and summarizing them in an easy-to-understand manner.

  • AI-driven Market Research: LLMs can assist in market research by scanning, summarizing, and analyzing vast amounts of financial news, reports, and social media chatter.


In conclusion, the landscape of financial data marketplaces is rapidly evolving, fueled by innovations in data delivery, the rise of alternative datasets, and advancements in AI/ML technologies. Data delivery mechanisms have become more sophisticated, with the emergence of new file formats and cloud-native data warehouses like Snowflake and BigQuery. They have significantly enhanced the speed, scalability, and efficiency of data transmission and storage, shaping a new era of financial data utilization.

Simultaneously, the surge of alternative data is revolutionizing financial decision-making. Novel data types, like satellite imagery and social media sentiment, are providing unique, often real-time insights into market trends, consumer behavior, and economic indicators. These, when leveraged correctly, can offer a competitive edge in investment strategies and financial forecasting.

The role of AI and ML, particularly with the advent of Large Language Models, is becoming increasingly central in managing and making sense of the data deluge. From data discovery and processing to predictive analysis and personalized recommendations, these technologies are unlocking new possibilities and services in the financial data marketplaces. However, with these advancements come significant challenges.

One of the main issues revolves around the availability and quality of training data. AI/ML models are only as good as the data they’re trained on. Ensuring the models have access to large, diverse, and representative datasets is crucial, yet often a daunting task. There are also concerns about the fairness and bias in AI/ML models. As these models are used to make increasingly impactful decisions, there’s a need for more transparency and interpretability to prevent potential discriminatory outcomes.

Lastly, the role of regulations in this rapidly evolving landscape cannot be overstated. While regulations aim to safeguard the integrity of the market and protect user data, they can also present challenges in terms of compliance. Striking the right balance between innovation and regulation is crucial.

As financial data marketplaces continue to evolve and transform under these emerging trends, they hold great promise for driving financial industry advancements. However, it will be equally important to address the associated challenges head-on, with thoughtful consideration of the ethical and societal impacts.


Alternative Datasets

  1. Geospatial Data: This includes data from satellite imagery, GPS data, and other geographically-related information. For example, hedge funds and investors may use satellite images of car parks to gauge customer footfall at retail stores, or GPS data to understand shipping routes and supply chain dynamics.

  2. Consumer Credit Data: For companies in the financial services sector, understanding the creditworthiness of consumers is critical. Traditional credit scoring is being complemented with data from various other sources like utility bills, rent payments, and even social media activity.

  3. Behavioral Analytics: These are insights derived from understanding consumer behavior. This could include purchase history, website clicks, and app usage, among other things.

  4. Real Estate Data: This includes data related to properties, home values, rental rates, occupancy rates, and sales data. It is often used by real estate investors and financial institutions involved in mortgage lending.

  5. Macro-economic Data: While macro-economic data like GDP, employment figures, and interest rates have long been used in financial analysis, the ways these data are collected and analyzed are evolving. For instance, real-time data on job postings or consumer spending can provide quicker and more granular insights into economic trends.

  6. Cultural and Demographic Data: Data about cultural trends and demographic shifts can also be valuable. For example, understanding changes in attitudes towards work-life balance, environmental awareness, or demographic trends such as aging populations can help predict shifts in market demand.

  7. Employee Skillset Data: As the economy becomes more knowledge-based, understanding the skills and capabilities of a company’s workforce can provide valuable insights. This might involve analyzing data from sources like LinkedIn or job postings.

  8. Product Reviews and Ratings Data: Online reviews and ratings can be a valuable source of information about a company’s products and their reception in the market. Analyzing this data can provide insights into product quality, customer satisfaction, and potential future sales trends.

  9. Patent and Research Data: For industries that rely heavily on innovation, such as technology or pharmaceuticals, tracking patent filings and research outputs can be a way to assess a company’s future potential.

  10. Political Risk Data:: For companies operating in multiple countries, data on political stability, corruption, conflict, and policy changes can be important for assessing potential risks.

  11. Blockchain Data: The rise of cryptocurrencies and blockchain technology has led to the generation of a wealth of data that can be used for financial analysis. This can include transaction data, wallet addresses, and other information found on public blockchains.

  12. Weather Data: Weather patterns can have significant impacts on various industries, from agriculture to retail. Access to real-time and forecasted weather data can help financial analysts predict sector performance, especially in industries sensitive to weather changes.

  13. Trade and Tariff Data: In the global economy, changes to trade agreements and tariffs can significantly impact companies” bottom lines. Keeping track of these changes can help predict economic trends and company performance.

  14. Market Sentiment Indicators: These include both traditional indicators, like consumer confidence indexes, and more modern measures like social media sentiment tracking. Market sentiment can often be a powerful driver of financial markets.

  15. Corporate Social Responsibility (CSR) Data: As investors increasingly consider companies” social impacts, data related to CSR initiatives is becoming more valuable. This can include charitable giving, community involvement, and efforts to increase diversity.

  16. Infrastructure Data: This could include data on the status of public infrastructure, construction and development projects, and urban planning initiatives. Such data could be particularly valuable for real estate, construction, and transportation businesses.

  17. Data from Drones and Other Aerial Devices: Drones can provide unique perspectives and data that are unattainable from the ground. For instance, insurance companies might use drone imagery to assess property damage, while agriculture companies might use it to monitor crop health.

  18. Credit Card Transaction Data: Although subject to privacy regulations and anonymization, this data can provide insights into consumer spending habits.

  19. Labor Market Data: This includes data on job postings, salaries, and workforce composition, and can offer insights into a company’s growth and labor market trends.

  20. eSports and Online Gaming Data: The gaming industry is booming, and data from online games and eSports can provide insights into consumer spending, engagement, and trends in the industry.

  21. Space Data: Data from space agencies and private satellite companies can offer insights into a range of economic activities. For example, nighttime light data can indicate economic activity, while remote sensing data can provide information about agriculture, climate change, and more.

  22. E-commerce Data: Data from online sales can provide granular insights into consumer behavior, market trends, and economic health. This can range from big-picture trends down to specific details about consumer preferences.

  23. Gaming and Gambling Data: Data from online gaming and betting can reveal insights into discretionary spending and consumer confidence. It may also provide early indications of shifts in sporting or cultural events.

  24. Virtual Economies and Digital Assets Data: In the burgeoning world of online gaming and virtual reality, virtual economies are growing. Tracking the trading and valuation of digital assets (like in-game items or virtual real estate) can offer new financial insights.

  25. Podcast and Streaming Data: Data about what people are watching and listening to can reveal trends in consumer sentiment and pop culture. This can be valuable for businesses in the entertainment sector, among others.

  26. Social Impact and Non-Profit Data: Data from non-profit and social impact organizations can reveal insights into societal issues and charitable giving. This can be particularly valuable for businesses focused on corporate social responsibility.

  27. Deepfake and Misinformation Data: As the prevalence of deepfakes and misinformation grows, data about these phenomena is increasingly valuable, particularly for cybersecurity firms, social media platforms, and businesses with significant brand recognition.

  28. Digital Footprint Data: This type of data includes a person’s or a company’s online presence, from website visits and clicks to social media interactions and posts. Analyzing digital footprints can provide insights into behaviors, interests, and trends.

  29. Open Source Data: Open source information, from software to databases, can provide valuable insights and tools for financial analysis. Open banking initiatives, for example, are transforming the finance industry by making it more transparent and customer-centric.

  30. Educational Data: Data related to education, such as enrollment rates, graduation rates, and fields of study can be used to predict trends in the labor market or to analyze the state of human capital in different regions.

  31. Art Market Data: Art as an asset class has unique characteristics and data related to art sales, valuations, and trends can be valuable to certain investors.

  32. Shipping and Logistics Data: Data related to the movement of goods around the world, such as shipping volumes, freight costs, and delivery times, can provide insights into economic activity and trade flows.

How to cite

Jandhyala, Vamshi. 2023. “Financial Data Marketplaces’, Sep 27, 2023. URL

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