Alternative Data for Finance: Market Overview, Examples, Providers
Table of Contents
What is Alternative Data in Finance?
Alternative data refers to non-traditional data sources that are leveraged to enhance financial analysis, uncover investment opportunities, and gain competitive insights beyond what is possible with standard financial disclosures. While traditional data includes company filings, earnings reports, macroeconomic indicators, and market prices, alternative data encompasses a broader and often less-structured set of information that is typically sourced from the digital and physical footprint of economic activity.
Examples of alternative data include satellite imagery of retail parking lots to estimate foot traffic, shipping and cargo data to anticipate supply chain movements, social media sentiment analysis to assess public perception of a brand, mobile geolocation data to monitor store visits, credit card transaction aggregates to evaluate consumer behavior, and web scraping of product reviews or job postings to infer operational trends. Other sources may include email receipts, IoT sensor streams, ESG signals, or even audio/video from earnings calls processed through NLP techniques.
The value of alternative data lies in its ability to provide real-time or forward-looking signals that precede or complement conventional indicators. For instance, an investment firm may detect a slowdown in foot traffic at a retail chain weeks before the company announces a dip in quarterly earnings. Hedge funds, asset managers, and quant trading firms increasingly use alternative data to build predictive models, conduct nowcasting, or refine risk assessments—seeking alpha where traditional models may lag.
Technologically, alternative data often requires advanced processing pipelines, including data cleaning, feature engineering, and machine learning techniques to extract usable signals from noisy and high-dimensional raw inputs. In many cases, the data must also be aggregated, anonymized, or fused with traditional datasets to deliver decision-ready insights. Due to its complexity, alternative data analysis typically involves data scientists, quants, and domain experts working in tandem.
As the financial industry becomes more data-driven, alternative data is no longer a niche tool but a strategic asset. Regulatory bodies and institutional investors are also beginning to scrutinize its usage, prompting the development of governance frameworks to ensure data quality, privacy, and ethical sourcing. When properly integrated, alternative data enables faster, smarter, and more differentiated financial decision-making in a highly competitive market.
Why Alternative Data Is Gaining Popularity Among Investors
In today’s highly competitive investment landscape, traditional financial metrics—such as earnings reports, balance sheets, and analyst recommendations—are no longer sufficient to generate alpha. With tighter margins, increased regulatory scrutiny, and faster-moving markets, investors are turning to alternative data as a new frontier for gaining informational advantages. Alternative data refers to any non-traditional dataset that provides insights into economic or financial activity. This includes sources like satellite imagery, social media sentiment, geolocation data, credit card transactions, supply chain activity, app usage, and even weather patterns.
The appeal of alternative data lies in its potential to enable faster and more informed decision-making. By analyzing consumer behavior in near real-time, investors can anticipate earnings surprises, supply-demand shifts, or market sentiment ahead of quarterly reports or news cycles. For instance, foot traffic data from retail locations can be used to predict sales performance, while web scraping of job postings can indicate a company’s growth trajectory or internal priorities. These data points support the construction of more agile, data-driven investment strategies.
As artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) technologies continue to advance, the ability to ingest and analyze massive volumes of unstructured or semi-structured alternative data has become increasingly viable. Hedge funds, asset managers, and private equity firms are now able to integrate hundreds of disparate data feeds, uncover hidden correlations, and build predictive models with a level of granularity previously unattainable. These capabilities not only improve forecasting accuracy but also support risk management, factor modeling, and event-driven trading strategies.
Moreover, the growth of cloud computing and data infrastructure has made access to alternative data more scalable and cost-effective. Data vendors now offer API-based delivery, curated datasets, and integrated analytics platforms tailored for financial professionals. Regulatory clarity around data sourcing and usage has also improved, reducing legal friction and encouraging broader adoption. As the competitive pressure to differentiate intensifies, alternative data is quickly moving from a niche advantage to a mainstream requirement in modern portfolio construction and asset allocation.
Common Examples of Alternative Data Sources
Satellite Imagery and Geospatial Data
Satellite imagery and geospatial intelligence offer real-time, objective observations of physical activity across the globe. Financial analysts use high-resolution images to monitor economic activity at locations such as shipping ports, warehouses, industrial plants, and retail parking lots. For example, changes in the number of containers at a port may indicate fluctuations in global trade volumes, while parking lot occupancy can serve as a proxy for in-store traffic and retail performance.
Beyond commerce, geospatial data is also applied in sectors like agriculture, where satellite-based vegetation indices help estimate crop yields weeks before official reports. Infrastructure growth in emerging markets, detected via construction imagery, may signal macroeconomic trends or investment opportunities. These datasets are often fused with weather data, transportation logs, or market benchmarks to build multi-factor investment models.
Web Scraping: E-commerce, Reviews, and Social Sentiment
Web scraping involves programmatically extracting public data from websites to gain insights into consumer trends, product performance, and brand perception. Analysts scrape e-commerce sites to track changes in product listings, pricing dynamics, discount frequencies, or stock availability—helping to identify demand shifts or supply chain constraints.
In addition, reviews from platforms like Amazon, Yelp, or Trustpilot reveal user satisfaction and product quality trends. Social media content—posts, hashtags, engagement metrics—feeds into sentiment analysis algorithms that assess brand strength, market buzz, or reputational risk. For example, a sudden spike in negative sentiment on Twitter about a consumer electronics brand may forecast a drop in quarterly sales. NLP and machine learning tools are often used to structure and analyze this unstructured text data at scale.
Transactional Data: Credit Card and Point-of-Sale Systems
Aggregated and anonymized transaction-level data from credit card networks and POS systems provides a direct, high-frequency view into consumer spending. These datasets track metrics such as average ticket size, transaction frequency, merchant category trends, and regional demand shifts.
For institutional investors and hedge funds, transaction data offers a leading indicator of company performance—allowing them to anticipate revenue results or consumer demand well before earnings announcements. For example, sustained spending growth at a specific restaurant chain or apparel brand could suggest positive upcoming earnings. Transaction data can also be segmented by customer demographics, geography, or time period to support granular sector analysis or economic forecasting.
Sensor and IoT Data in Supply Chain Analysis
Sensor and IoT-based data streams from logistics networks, shipping containers, or manufacturing equipment offer deep visibility into real-time operations. These devices track variables such as temperature (for cold chain monitoring), GPS location, shipment delays, inventory movement, or equipment uptime.
Investors and procurement managers use this data to evaluate supply chain reliability, vendor performance, and geopolitical risk exposure. For instance, increased sensor-reported delays from a factory region may indicate impending supply constraints for a manufacturer. In portfolio risk management, this information supports early identification of operational bottlenecks or ESG risks linked to unsustainable logistics practices.
App Usage and Mobile Device Analytics
Mobile analytics track user interactions across mobile apps and platforms, revealing trends in digital engagement, market penetration, and platform health. Key metrics include daily or monthly active users (DAUs/MAUs), session length, in-app purchases, and churn rates.
Such data is valuable for evaluating tech companies, digital service providers, or app-driven businesses. For example, rising engagement metrics on a fintech app could signal user acquisition momentum, while declining retention may indicate saturation or increased competition. App usage data is frequently used by venture capital firms and market analysts to assess early-stage traction, benchmark competitors, and track market share evolution in real time.
How the Alternative Data Market is Evolving
Market Size and Growth Trajectory
The global alternative data market is experiencing unprecedented growth, fueled by increasing demand for differentiated insights in a highly competitive financial environment. According to industry analysts, the market is projected to surpass tens of billions of dollars in annual value within the next few years, with a compound annual growth rate (CAGR) exceeding 30%. This surge is driven by the need for more granular, real-time, and forward-looking information that cannot be obtained from conventional financial reports or macroeconomic indicators.
Institutional investors, hedge funds, and asset managers are now dedicating substantial portions of their research and analytics budgets to acquiring, integrating, and interpreting alternative datasets. The use of alternative data has expanded beyond quantitative hedge funds to include fundamental investors, private equity firms, and even central banks. Increasingly, alternative data is being incorporated directly into investment models, signals, and dashboards, making it a core component of modern financial research infrastructure.
Emerging Trends: ESG Data, Privacy-Conscious Sources
One of the most prominent emerging trends is the rise of Environmental, Social, and Governance (ESG) data as a form of alternative insight. Investors seeking to align portfolios with sustainability goals or responsible investment frameworks are turning to non-financial indicators such as carbon emissions, labor practices, board diversity, and community impact. ESG alternative data may come from satellite imagery (e.g., measuring pollution), supply chain monitoring, or even textual sentiment from company disclosures and social media.
Simultaneously, there is growing awareness around data privacy, security, and ethical use—especially as regulatory frameworks such as GDPR, CCPA, and China’s PIPL become stricter. As a result, privacy-conscious data sourcing is gaining traction. Technologies such as synthetic data generation, differential privacy, and federated learning are being applied to ensure that alternative data remains compliant while retaining analytical value. These innovations allow institutions to derive meaningful insights from behavioral, geolocation, or biometric data without exposing personally identifiable information (PII).
Rise of Data-as-a-Service (DaaS) Providers
The complexity and heterogeneity of alternative data have led to the rise of Data-as-a-Service (DaaS) providers, who offer streamlined, scalable solutions for data acquisition, integration, and governance. These platforms provide access to pre-cleaned, enriched, and regularly updated datasets through cloud-based APIs and platforms, eliminating the need for firms to build their own data pipelines from scratch.
DaaS providers often bundle value-added services such as metadata catalogs, taxonomy mapping, outlier detection, and data normalization. Licensing models are also evolving, with flexible subscription options, usage-based pricing, and on-demand provisioning designed to support diverse institutional needs. These services are especially valuable for buy-side firms looking to deploy alternative data rapidly into trading models, research platforms, or portfolio construction engines. By abstracting away technical and legal complexity, DaaS providers have lowered the barrier to entry for firms of all sizes and accelerated the institutionalization of alternative data usage across global financial markets.
Key Steps in Using Alternative Data for Financial Analysis
Identify Strategic Use Cases (Alpha Generation, Risk Management)
The first step in leveraging alternative data is to clearly define the strategic objectives of its use. Financial analysts and portfolio managers must identify specific use cases, such as alpha generation through early signal detection, enhanced credit risk assessment, volatility forecasting, or event-driven trading. Each objective requires a different lens when selecting data—satellite imagery may be useful for retail foot traffic prediction, while app usage data may offer insights into consumer engagement trends.
Aligning data selection with clearly articulated use cases ensures that investments in data acquisition and modeling deliver measurable value. This stage often involves cross-functional discussions between data scientists, investment teams, and compliance officers to align on the intended application, expected outcomes, and acceptable risk thresholds.
Acquire and Curate Alternative Datasets
Once the use case is defined, the next step is to source the most appropriate alternative datasets. These may come from external vendors, data marketplaces, strategic partners, or internal telemetry such as clickstreams, IoT data, or customer transactions. Data acquisition involves evaluating not just cost and licensing, but also legal and regulatory compliance—especially when dealing with user-generated content or geolocation data.
Curation is critical to ensure relevance. Analysts assess data freshness (update frequency), granularity (resolution at entity, event, or time level), coverage (geographic or sectoral scope), and completeness. Poor-quality data can lead to spurious model outputs or regulatory red flags. Therefore, quality control processes such as sample audits, schema validation, and metadata checks are often applied before integration.
Preprocess and Clean Data
Alternative data is often noisy, semi-structured, or unstructured—especially in formats like text, sensor logs, or HTML pages. Preprocessing includes parsing files, harmonizing units, resolving encoding issues, and converting raw data into structured tabular or time series formats. Deduplication, missing value imputation, and outlier detection are core activities at this stage.
In some cases, domain-specific enrichment is applied—for example, mapping merchant codes to industry sectors, or converting timestamps across time zones. For machine learning use, categorical fields may be encoded, embeddings extracted (for text/image), or normalization applied to ensure consistency across feature sets. This step determines how accurately models can later learn from the data.
Integrate with Analytical Models or AI Systems
Cleaned and structured alternative data is then integrated into analytics pipelines or AI/ML systems. Depending on the use case, this integration may involve feature engineering, time alignment with traditional financial metrics, or the creation of composite indicators. For quantitative strategies, data is often used in regression models, time-series forecasts, or factor analysis. In natural language processing (NLP), text data might feed sentiment models or news signal extractors.
In deep learning contexts, alternative data may be processed using convolutional or recurrent neural networks, especially when handling spatial, sequential, or hierarchical structures. Increasingly, hybrid models combine structured and unstructured signals to drive more nuanced financial predictions. Throughout this step, maintaining consistency in data granularity, temporal frequency, and input format is key to robust performance.
Evaluate Performance and Compliance
The final step is validating that the models and analyses built using alternative data are effective, explainable, and compliant. Performance evaluation includes backtesting, cross-validation, and scenario analysis to ensure that the insights derived from the data add genuine predictive value and are not artifacts of overfitting or noise.
From a governance perspective, institutions must ensure compliance with both internal data policies and external regulations such as GDPR, CCPA, or SEC/FINRA guidelines. This includes maintaining audit logs, usage documentation, model explainability (especially for black-box AI), and data lineage tracking. Ethical review boards or model risk committees may also review how alternative data is sourced, interpreted, and acted upon—particularly when the data could influence decisions about individuals or protected groups. Integrating strong controls at this stage helps organizations scale their use of alternative data responsibly and sustainably.
Leading Alternative Data Providers
Azoo AI: Synthetic Alternative Data Generation for Privacy-Critical Sectors
h3와 관련한 Azoo AI의 기술 설명
Quandl
Quandl, acquired by Nasdaq, is a prominent data platform offering both traditional financial datasets and a growing portfolio of alternative data. It specializes in providing economic indicators, commodity flows, energy consumption patterns, and sector-specific consumer activity metrics. Quandl’s curated datasets are favored by quantitative analysts, portfolio managers, and institutional investors for building econometric models and backtesting investment strategies.
The platform supports direct API integration, enabling seamless ingestion of data into Python, R, or enterprise analytics environments. Quandl also emphasizes data quality and metadata transparency, which are critical for compliance-conscious institutions. Its coverage spans macroeconomic forecasts, geopolitical risk signals, and ESG-linked behavioral datasets—making it a comprehensive tool for multi-asset research workflows.
Thinknum
Thinknum aggregates and structures vast amounts of web-based information into usable signals for the financial community. Its core datasets include employee headcount trends scraped from career pages, product pricing fluctuations from e-commerce sites, app rankings from app stores, and real-time tracking of physical store openings and closures.
These indicators are especially valuable for equity analysts and hedge funds looking to detect inflection points in company fundamentals before they appear in earnings reports. For example, a sudden drop in job postings or app engagement for a retail brand may flag a slowdown in consumer demand. Thinknum also offers visualization tools and dashboards that allow analysts to monitor company-level changes over time without extensive coding. The firm’s strength lies in its dynamic tracking of corporate activity at scale, often offering leading signals that pre-empt official disclosures.
YipitData
YipitData specializes in deriving structured insights from unstructured data sources such as credit card transactions, email receipts, and mobile activity. Its primary focus is on capturing consumer behavior and operational metrics for digital-first companies, particularly in sectors like e-commerce, food delivery, ride-sharing, and streaming media.
Investors use YipitData to track granular performance trends such as customer acquisition cost (CAC), order frequency, average transaction value, and retention cohorts. What sets Yipit apart is its domain-specific dashboards and analyst-curated insights, which are designed to mirror KPIs used by management teams. As such, clients can benchmark real-time performance against Wall Street expectations with high confidence. The service is widely adopted by hedge funds and long-only managers looking to validate or challenge earnings estimates with empirical, behavior-based evidence.
S&P Global Market Intelligence
S&P Global is traditionally known for its deep repository of structured financial and credit data. However, its Market Intelligence division has expanded significantly into alternative data, providing institutional clients with ESG datasets, supply chain analytics, sentiment scores from news and social media, and geopolitical event tracking.
S&P’s alternative data offerings are often integrated into its broader analytics platforms such as Capital IQ, which allows users to blend traditional valuation metrics with unconventional signals for deeper due diligence. For instance, supply chain data can be used to analyze company exposure to geopolitical disruptions, while ESG controversy tracking can inform sustainability-focused investment mandates. Because of its regulatory-grade infrastructure and long-standing client relationships, S&P is often the provider of choice for large banks, insurance firms, and compliance-sensitive asset managers.
Key Considerations in Provider Selection (Data Quality, Coverage, Licensing)
Selecting an alternative data provider requires more than just identifying interesting datasets—it involves a holistic evaluation of data utility, operational fit, and regulatory safety. One of the most critical dimensions is data quality. This includes the accuracy, consistency, and completeness of the data, as well as how it has been cleaned, deduplicated, and normalized. Providers should offer transparent data dictionaries, field definitions, and clear lineage to enable effective integration and validation.
Data coverage is another important factor. Organizations should assess the breadth of industries, geographies, and time periods included in the dataset. For instance, a provider may offer excellent transaction data in North America but limited visibility in Europe or emerging markets. Historical depth is equally important for backtesting and model training—datasets with at least 2–5 years of history are typically preferred in quantitative research and forecasting.
Documentation and onboarding support play a key role in operational efficiency. Providers should supply comprehensive API guides, sample queries, schema files, and usage examples. The availability of SDKs or prebuilt integrations (e.g., for Python, Snowflake, or AWS) can significantly reduce deployment time. Organizations should also evaluate the responsiveness and expertise of the provider’s support team—especially when working with unstructured or complex data types that may require iterative clarification.
Finally, licensing terms must be scrutinized carefully. Legal teams should verify whether the data is ethically sourced, anonymized where necessary, and compliant with relevant regulations (e.g., GDPR, CCPA). Usage restrictions—such as redistribution limits, user caps, or retention clauses—should be clearly defined and compatible with the organization’s intended use cases. Clarity around intellectual property rights and indemnity provisions is especially important for public-facing or client-serving applications. In many cases, negotiating favorable licensing terms upfront can prevent downstream legal or operational challenges.
Use Cases: How Financial Firms Use Alternative Data
Investment Forecasting with Non-Traditional Indicators
Financial institutions are increasingly using non-traditional indicators—such as satellite imagery of retail parking lots, shipping activity data, or online consumer behavior—to forecast investment performance. These alternative signals help investors anticipate revenue trends, supply chain dynamics, and macroeconomic shifts earlier than conventional financial reports allow.
Credit Risk Assessment Using Behavioral Data
Beyond credit scores and income statements, lenders are leveraging behavioral data—such as mobile phone usage patterns, e-commerce activity, and bill payment histories—to evaluate creditworthiness. This approach enables more inclusive lending practices by providing insights into borrowers with limited traditional credit histories, while also improving risk segmentation and loan performance prediction.
Real-Time Monitoring of Market Events via Social Media
Social media platforms serve as real-time sources of sentiment and event detection. Financial firms monitor trends, breaking news, and public opinion on platforms like Twitter, Reddit, or LinkedIn to identify sudden market shifts, reputational risks, or regulatory concerns. Natural language processing (NLP) tools help quantify sentiment and extract actionable insights at scale.
Private Equity and M&A Insights Using Web and Hiring Data
Private equity firms and M&A analysts increasingly use web traffic trends, employee reviews, job postings, and online mentions to assess the operational health and strategic direction of target companies. Hiring surges or declines, new technology-related roles, or spikes in product-related search traffic can all signal growth opportunities or hidden risks ahead of formal disclosures.
Insurance Risk Modeling with Environmental and Mobility Data
Insurers incorporate environmental data—such as satellite-derived climate patterns, pollution levels, or flood zones—as well as mobility data from vehicles and smartphones to build more dynamic risk models. These inputs enhance the accuracy of underwriting, pricing, and claims prediction, especially in areas prone to natural disasters or changing urban infrastructure.
Benefits of Using Alternative Data
Uncovering Hidden Market Signals
Alternative data allows financial firms to detect early-stage signals that may not yet be reflected in traditional financial reports. For instance, declining foot traffic at a major retailer—visible through satellite imagery—can precede revenue slowdowns by several weeks. Web search trends, job postings, or customer reviews may reveal evolving sentiment or operational changes long before official disclosures.
By identifying such leading indicators, investment teams can anticipate market shifts, spot inflection points in business performance, and capture alpha-generating opportunities before consensus estimates adjust. These insights are particularly valuable in high-frequency trading, thematic investing, and sector rotation strategies where timing is critical.
Enhanced Risk Detection and Early Warning Systems
Alternative data provides financial institutions with enhanced visibility into emerging operational, financial, and reputational risks. For example, social media chatter may indicate rising customer dissatisfaction, while sensor data might reveal production delays in key suppliers.
Real-time tracking of regulatory actions, ESG controversies, or environmental anomalies supports proactive risk mitigation. Banks and insurers increasingly integrate alternative risk indicators into their early warning systems to flag anomalies, conduct stress testing, and inform contingency planning—reducing exposure to tail-risk events.
Differentiation and Competitive Edge in Asset Management
In a highly saturated asset management environment, the ability to source and effectively interpret non-traditional data has become a key differentiator. Firms that invest in alternative data infrastructure and talent often gain a competitive edge by identifying opportunities missed by traditional models.
This can manifest in the form of improved forecasting accuracy, reduced drawdowns, or access to uncorrelated signals. Boutique quant funds and systematic macro strategies, in particular, rely heavily on differentiated datasets to identify market inefficiencies, execute faster trades, and refine factor models with proprietary indicators.
Greater Personalization in Financial Products
Retail banks, insurers, and fintech companies use alternative data to deepen customer understanding beyond basic demographics. Behavioral signals such as transaction histories, app usage, or mobility patterns enable hyper-segmentation of customer profiles.
This makes it possible to tailor financial products—such as dynamic credit limits, pay-per-use insurance, or personalized interest rates—based on actual customer behavior. Personalized offerings increase conversion rates, improve risk-adjusted returns, and enhance overall customer satisfaction and retention in increasingly digital ecosystems.
Challenges of Working with Alternative Data
Data Licensing and Legal Compliance
Alternative data often originates from third-party platforms, web scraping, or consumer devices—raising complex licensing and legal considerations. Firms must ensure they have the appropriate rights to collect, store, process, and use the data. Contracts should clarify whether the data is first-party, aggregated, anonymized, or derived, and whether redistribution or commercial use is permitted.
In addition, compliance with global data protection regulations—such as GDPR (EU), CCPA (California), and PDPA (Singapore)—is mandatory, especially when data contains or can infer personally identifiable information (PII). Missteps in this area can lead to financial penalties, reputational harm, or even regulatory investigations.
Noise and Inconsistency in Raw Data
Unlike structured financial reports, alternative datasets are often messy, unstructured, and lacking standard schema. Social media content may include sarcasm or bots, transaction logs may contain duplicate or missing entries, and geolocation data may be imprecise or incomplete.
Transforming this raw data into usable signals requires significant effort: data engineers and analysts must clean, filter, normalize, and cross-validate inputs before modeling. Organizations often need to invest in NLP, image processing, anomaly detection, and quality scoring systems to ensure robustness. Poor preprocessing can lead to misleading insights and model degradation.
Integration with Legacy Systems
Many financial institutions still operate on legacy infrastructure with rigid schemas, batch-based data workflows, or siloed systems. Integrating alternative data often requires significant architectural changes—such as building new ingestion pipelines, adding scalable storage (e.g., data lakes), and implementing real-time APIs.
This technical debt can slow adoption or create friction between innovation teams and IT departments. Firms must develop middleware, sandbox environments, and hybrid cloud strategies to bridge legacy and modern data architectures. Change management, cross-functional coordination, and vendor interoperability are also critical success factors.
Data Privacy and Ethical Considerations
The use of alternative data, especially behavioral, biometric, or location-based information, raises serious ethical concerns. Consumers may be unaware that their data is being used for financial profiling, credit decisions, or investment modeling. This can erode trust and draw scrutiny from regulators, media, and advocacy groups.
To address these concerns, firms must implement transparent data governance policies, ensure informed consent wherever required, and minimize the use of sensitive attributes unless demonstrably necessary. Privacy-preserving technologies such as differential privacy, federated learning, and synthetic data generation can help reduce re-identification risks while retaining analytical value.
FAQs
What qualifies as alternative data in finance?
Alternative data includes any non-traditional information used for investment or financial analysis, such as satellite images, web traffic, IoT sensor data, or app usage metrics.
How is alternative data different from big data?
Alternative data refers to the type and source of data, while big data refers to its scale and structure. Not all big data is alternative, and not all alternative data is necessarily large-scale.
What tools are used to analyze alternative data?
Data scientists use tools such as Python, R, Spark, and AI/ML frameworks including Scikit-learn, TensorFlow, and proprietary platforms that support large-scale ingestion, preprocessing, and model deployment.
Is it legal to use web-scraped data for investment decisions?
It depends on the site’s terms of use, local regulations, and how the data is used. Legal review is recommended. Many firms prefer licensed datasets to reduce risk.
How does synthetic data improve alternative data strategies?
Synthetic data helps simulate rare events, expand training datasets, and protect privacy.
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