Increasingly, investment organizations such as hedge funds, private equity firms, and asset managers are harnessing alternative data to gain competitive advantages. Recent research indicates that alternative data techniques have moved beyond experimental stages to mainstream tools for financial decision-making.
According to Funds Europe, 86% of investment managers plan to increase their use of alternative datasets over the next two years. Demand is growing for geolocation data and spending data, particularly with key data sources such as social listening, employment trends, and natural language processing (NLP), among others.
Progress with alternative data has the potential to revolutionize how investment strategies are developed, executed, and optimized. In this article, we explore the use of alternative data in financial services, identifying the risks, challenges, and opportunities, as well as methods your firm can adopt to begin using this resource successfully.
The Emergence of Alternative Data in Financial Services
Alternative data refers to non-traditional information sources that fall outside conventional financial metrics, such as company filings, press releases, analyst reports, and market data. The market for this data has expanded, reaching over $9 billion globally in 2024, according to Precedence Research.
As traditional data sources often lag behind actual economic activity—a view shared by 98% of investment professionals, according to Funds Europe—non-traditional data provides timely insights that can reveal market shifts before they become apparent through conventional channels.
Financial firms recognize that competitive advantages increasingly stem from unique data insights rather than merely faster access to the same information. In fact, 67% of investment advisers for hedge funds and private equity firms now use alternative data—more than double the 31% reported in 2022, Lowenstein Sandler reports.
Types of Alternative Data
A wide variety of data types and sources fall into the “alternative” category. Here are some of the most common examples used today.
- Consumer transaction data: Analyzing aggregated credit card transactions, point-of-sale data, and eCommerce activity provides insights into company revenues and sector trends before official earnings reports.
- Geolocation data: Monitoring foot traffic and mobility patterns through mobile device signals offers real-time indicators of economic activity, retail performance, and supply chain efficiency.
- Social media and sentiment analysis: Natural language processing (NLP) applied to news articles, social platforms, and online forums reveals public perception and market sentiment.
- Web scraping: Extracting online pricing trends, inventory levels, and product availability allows for competitive analyses and forecasting of market dynamics.
- Satellite imagery: Aerial photographs of parking lots, shipping ports, agricultural fields, and industrial facilities provide physical activity indicators.
- Mobile app usage: Data on application downloads, user engagement, and in-app purchases reveals consumer behavior trends and company performance metrics.
- Supply chain data: Shipping manifests, customs records, and logistics information help predict inventory levels and supply disruptions.
- Biometric data: Increasingly, biometric information offers novel insights into consumer behavior and market trends.
- Cloud platform data: Information from cloud services provides visibility into technology adoption and enterprise activity.
Sources like cloud platforms, app usage, biometric data, and web scraping have grown in their utilization by investment firms by 16 – 20% each compared to past surveys, Lowenstein Sandler reports. Now, 94% of investment firms plan to increase their alternative data budgets for 2025.
Strategic Applications in Investment Management
For most investment firms, non-traditional data no longer functions as a standalone resource but complements traditional analysis. Here, we consider strategies for different types of financial services firms.
Hedge Fund Strategies
Hedge funds leverage alternative data to develop unique investment strategies and generate alpha. Since these types of data can reveal trends in retail performance before quarterly earnings reports are released, funds can adjust their investment strategies ahead of the market, potentially improving predictive accuracy by up to 25% in certain trading models, according to one industry publication.
Private Equity Due Diligence
Private equity firms can use alternative data to improve due diligence. Transaction data, web scraping, and geolocation data help investors evaluate the health of a company’s supply chain, customer base, and competitive position before acquisition. This visibility reduces investment risk and improves valuation accuracy.
Equity Research and Forecasting
Equity analysts can use alternative data to forecast stock performance. By analyzing web traffic patterns, social media sentiment, or shipping activity, analysts can better understand a company’s potential performance. This predictive advantage enables more timely investment decisions.
Risk Assessment and Management
Alternative data can help investment firms assess and mitigate risks. Supply chain monitoring through IoT devices and satellite imagery allows investors to track potential disruptions that might impact a company’s performance. This visibility supports more resilient portfolio construction.
Building Scalable Data Architectures for Alternative Data
Competitive advantages with alternative data also depend on firms’ ability to process, analyze, and derive actionable insights from them. Here, we consider how technology infrastructure supports the integration of unconventional types of data.
Cloud-Based Infrastructure
Cloud platforms support alternative data operations, providing the scalability needed to handle diverse and growing datasets. Their flexibility means firms can rapidly deploy new analytical tools and adjust compute resources based on changing requirements.
Data Integration Pipelines
Sophisticated data pipelines help firms ingest, transform, and standardize alternative data from disparate sources. These pipelines must handle varied data formats, frequencies, and quality levels while ensuring consistency in how information flows into analytical systems. Effective integration frameworks combine batch processing for historical analysis with real-time streaming for time-sensitive insights.
AI and Machine Learning Systems
Merging alternative data with artificial intelligence (AI) represents a powerful combination that is reshaping investment management. The application of machine learning (ML) to alternative datasets allows for pattern recognition and predictive modeling at scales impossible for human analysts alone.
Data Quality and Governance
“Governance structures and information, communication, and reporting processes may need to evolve to address [alternative data] from a risk perspective,” the Harvard Law School Forum on Corporate Governance reports. Effective governance includes systematic vendor due diligence, data validation protocols, and comprehensive documentation of methodologies.
Implementation Challenges and Considerations
Despite its growing adoption, alternative data integration presents significant challenges that investment firms must address.
Legal and Compliance Issues
The use of alternative data raises important legal and regulatory considerations. Primary concerns include ownership and privacy issues, increased compliance burdens, and data security risks. Firms must navigate regulations like the SEC’s AI examination priorities while ensuring they avoid material non-public information that might constitute insider trading.
Data Quality and Reliability
Alternative data often lacks the standardization and quality controls associated with traditional financial information. Investment firms must develop sophisticated validation methodologies to assess dataset accuracy, representativeness, and potential biases. This validation becomes increasingly complex as firms combine multiple alternative data sources with traditional inputs.
Technical Expertise and Resources
Firms may need specialized skills in data science, machine learning, and cloud architecture. The competition for talent with these capabilities remains intense, creating potential barriers for smaller firms. Additionally, the costs associated with acquiring and processing new data types can be substantial—Lowenstein Sandler found that half of investment firms spend over $1 million annually on alternative data.
Integration with Existing Systems
Incorporating new types of data into established investment processes presents integration challenges. Firms must develop frameworks that allow insights from alternate data sources to enhance rather than disrupt existing analytical workflows. This integration often requires significant customization and ongoing refinement to maximize value.
The Future of Alternative Data in Financial Services
The trajectory of non-traditional data in financial services points toward several important developments.
Synthetic Data and AI Generation
Synthetic data generated by AI and ML can simulate market conditions and investor behaviors while preserving privacy and addressing data gaps. The integration of generative AI with alternative data creates the possibility for insights at a scale and speed previously unattainable.
Democratization of Access
Cloud-based platforms and specialized data providers make unconventional data more accessible to mid-sized investment firms. This democratization will likely accelerate competition while driving innovation in data utilization.
Enhanced Predictive Modeling
As alternative data becomes more integrated with AI and ML, predictive modeling will continue to advance. Combining diverse data streams with sophisticated algorithms supports more accurate forecasting of market movements, company performance, and macroeconomic trends. This evolution will further blur the lines between quantitative and fundamental investment approaches.
Regulatory Evolution
Regulations for alternative data will evolve as its use becomes more prevalent. Investment firms should anticipate increased scrutiny regarding data sourcing, privacy protections, and algorithmic transparency. Proactive engagement with regulators will be essential for sustainable non-traditional data strategies.
Conclusion
Alternative data has transitioned from an experimental tool to a fundamental component of modern investment management. Its impact extends across the investment landscape, from hedge fund strategies and equity research to private equity due diligence and risk management. As investment firms continue to increase their commitment to leveraging alternative types of data, the competitive advantages it provides will become increasingly critical for success.
The firms that will thrive in this environment will be those that build scalable data architectures capable of integrating diverse datasets while addressing the technical, legal, and analytical challenges inherent in alternative data utilization. Through thoughtful implementation and ongoing innovation, alternative data will continue to transform how investment decisions are made, ultimately reshaping the financial services industry.
Cloud-Based Infrastructure with Option One Technologies
Option One Technologies specializes in scalable cloud infrastructure solutions for financial firms. Contact us today to learn how partnering with us can help you unlock the power of alternative data.
