Alternative data has become a significant force in investment research. Once the domain of quantitative funds, alternative data now influences fundamental investing across strategies and asset classes.

But what exactly is alternative data, how does it complement traditional research, and what should investors understand about its promise and limitations?

Defining Alternative Data

Alternative data encompasses information sources outside traditional financial data feeds:

Digital exhaust: Data generated by online activity—web traffic, app usage, social media, search trends.

Transaction records: Credit card spending, point-of-sale data, receipt captures.

Physical observation: Satellite imagery, geolocation data, sensor networks.

Unstructured content: News articles, earnings call transcripts, regulatory filings.

Human intelligence: Expert network insights, survey responses, channel checks.

The unifying characteristic is that these sources weren't created for investment analysis but can be repurposed to generate investment signals.

The Alternative Data Landscape

Providers and Platforms

The alternative data ecosystem has grown substantially:

Data originators: Companies that generate data as a byproduct of their primary business.

Data aggregators: Firms that collect, clean, and package data from multiple sources.

Analytics providers: Companies that process raw data into investment-ready signals.

Marketplaces: Platforms that connect data sellers with investment buyer.

This ecosystem continues to expand as new data sources emerge and processing capabilities advance.

Common Data Types

Certain categories have achieved mainstream adoption:

Consumer transaction data: Tracking spending patterns at company and category level.

Web and app analytics: Monitoring digital engagement and user behavior.

Satellite and geolocation: Observing physical activity from retail traffic to industrial production.

Social and sentiment: Measuring public opinion and discussion trends.

Expert intelligence: Systematizing insights from industry practitioners.

Each category has different characteristics, lead times, and coverage patterns.

Integration with Traditional Research

Alternative data doesn't replace traditional research—it complements and extends it.

Hypothesis Generation

Alternative data can surface potential investment ideas:

Transaction data revealing company momentum before earnings
App trends suggesting emerging category leaders
Sentiment shifts indicating changing narratives

These signals prompt deeper fundamental investigation.

Hypothesis Testing

Once an investment thesis is formed, alternative data can test assumptions:

Is the company actually gaining market share?
Are customer acquisition trends sustainable?
Is management's guidance realistic?

Data evidence adds confidence to conclusions from qualitative research.

Monitoring Positions

After establishing positions, alternative data enables ongoing monitoring:

Real-time indicators of business trajectory
Early warning of competitive threats
Validation of quarterly expectations

This monitoring informs position sizing and exit decisions.

Expert Networks and Alternative Data

Expert interviews and alternative data are complementary, not competing, approaches.

Different Strengths

Factor	Alternative Data	Expert Interviews
Coverage	Scale across many companies	Deep focus on specific situations
Objectivity	Consistent measurement over time	Subject to human interpretation
Timeliness	Real-time or near-real-time signals	Point-in-time perspectives
Precision	Quantitative metrics	Qualitative nuance
Context	Data without explanation	Rich interpretation and meaning
Perspective	Historical patterns	Forward-looking views
Causation	Correlation signals	Understanding of why things happen

Integrated Approaches

The most sophisticated investors combine both:

Data to inform questions: Alternative data patterns prompt specific expert questions.

Experts to interpret data: Industry practitioners explain what data patterns mean.

Cross-validation: Data confirms or challenges expert perspectives.

Iteration: Each approach informs the other in ongoing research cycles.

The combination yields insight neither approach provides alone.

Practical Considerations

Data Quality Challenges

Alternative data requires careful evaluation:

Coverage: What universe does the data represent? Sample sizes may be smaller than they appear.

Bias: Is the sample representative, or does it systematically over- or under-weight certain segments?

Consistency: How stable is the data over time? Methodology changes can corrupt historical analysis.

Timeliness: How fresh is the data, and does that match investment time horizon?

Due diligence on data quality matters as much as due diligence on investments.

Processing Requirements

Raw alternative data rarely provides direct investment signals:

Cleaning: Removing errors, outliers, and inconsistencies.

Normalization: Adjusting for seasonality, growth trends, and methodology changes.

Modeling: Building analytical frameworks to extract signal.

Validation: Testing whether signals predict outcomes of interest.

These capabilities require investment in technology and talent.

Economic Considerations

Alternative data economics vary widely:

Cost: Ranges from free public data to millions annually for premium sources.

Exclusivity: Some data is widely available; some offers periods of exclusive access.

Decay: Signal value often degrades as more investors access the same data.

Scalability: Fixed costs favor larger investors who can spread them across more positions.

ROI analysis should consider not just direct costs but implementation requirements.

Limitations and Risks

Alpha Decay

As alternative data becomes mainstream, early adopters' advantages erode:

More investors accessing the same data
Data providers selling to multiple clients
Market efficiency incorporating signals faster

Sustainable advantage requires either exclusive data or superior processing.

Overfitting

With many potential data sources, finding spurious correlations is easy:

Historical backtests can overstate predictive power
Complex models may not generalize forward
Multiple testing creates false discoveries

Rigorous statistical methods and out-of-sample validation help, but risks remain.

Execution Gap

Signal generation is only part of the value chain:

Processing latency may exceed signal half-life
Portfolio constraints may prevent acting on signals
Transaction costs may exceed expected alpha

Alternative data alpha requires execution capabilities to capture.

Regulatory Uncertainty

Legal and ethical boundaries aren't always clear:

Privacy regulations affecting certain data types
Potential for data to constitute material non-public information
Evolving regulatory views on data practices

Legal review of data sources and usage is essential.

Building Alternative Data Capabilities

Starting Points

For firms new to alternative data:

Prioritize ruthlessly: Focus on data most relevant to your investment strategy.

Start with processed signals: Buying analytics rather than raw data reduces implementation burden.

Partner strategically: Relationships with quality providers accelerate learning.

Build evaluation capabilities: The ability to assess data quality matters more than data volume.

Advancing Capabilities

As sophistication grows:

Develop internal processing: Proprietary analytics create differentiation.

Combine multiple sources: Multi-source signals often outperform single sources.

Integrate with workflows: Embed alternative data into research and decision processes.

Track performance: Measure actual contribution to investment results.

Team Considerations

Alternative data requires specific capabilities:

Data science: Statistical and analytical skills for signal extraction.

Technology: Infrastructure for data ingestion, storage, and processing.

Domain knowledge: Investment understanding to guide analysis.

Critical evaluation: Ability to distinguish signal from noise.

Few individuals combine all capabilities; cross-functional collaboration is typical.

Looking Forward

Alternative data continues to evolve:

New sources: Continued emergence of novel data types.

Better processing: AI advances enabling more sophisticated analysis.

Democratization: Tools making alternative data more accessible to smaller investors.

Regulation: Evolving rules shaping what's permissible.

The investors who develop strong alternative data capabilities—and integrate them effectively with traditional research—will maintain meaningful advantages.

InsightAgent helps investment teams capture human intelligence from expert interviews. Learn more.

The Role of Alternative Data in Modern Investing