10 Alternative Data Sources Hedge Funds Should Evaluate in 2027
From consumer spending and satellite imagery to web traffic and AI infrastructure signals, these ten alternative data categories can help investment teams uncover new insights, test investment theses, and strengthen their research workflows.
For hedge funds and asset managers, finding differentiated information has become an increasingly important part of investment research. Traditional financial statements, earnings calls, and economic indicators remain essential, but they often tell investors what has already happened. Alternative data can offer additional perspectives on business activity, consumer behavior, industry trends, and changes in the real economy.
The challenge isn't simply finding more data. It's identifying which datasets can answer a meaningful investment question, evaluating their reliability, and determining whether they provide useful information beyond existing research sources.
As investment teams look toward 2027, developments in artificial intelligence, data infrastructure, and automated research workflows are creating new opportunities to discover and analyze alternative data.
So which sources should hedge funds evaluate?
Here are ten alternative data categories worth considering, along with their potential investment applications and the key questions to ask before incorporating them into a research process.
What Is Alternative Data?
Alternative data refers to information that supplements traditional financial and economic sources to help investors better understand companies, industries, markets, and economic activity.
Sources can include consumer transactions, satellite imagery, web activity, geolocation, corporate job postings, and other datasets generated through digital platforms, sensors, commercial activity, and proprietary research.
Depending on the source and methodology, alternative data can help investors monitor business performance between reporting periods, assess demand trends, evaluate supply chains, and test investment hypotheses.
However, a dataset is not automatically valuable because it is new or unconventional. Its usefulness depends on the investment question, data quality, coverage, timing, methodology, and the degree to which it provides information that existing sources do not.
1. Consumer Transaction and Card Data
Best for: Retail, restaurants, travel, consumer discretionary, and financial services research.
Consumer transaction data can help investors monitor spending patterns across merchants, categories, geographies, and customer segments.
For hedge funds analyzing consumer-facing businesses, these datasets may provide additional insight into demand trends before companies report quarterly results.
For example, an investment team researching a restaurant operator might use transaction data to examine changes in consumer spending, compare performance across locations, or evaluate trends relative to competitors.
Retail investors may use similar datasets to monitor changes in spending at specific merchants, assess market share, or compare consumer demand across brands.
Questions to ask when evaluating a dataset:
How representative is the underlying consumer panel?
What proportion of relevant transactions or merchants does it cover?
How frequently is the data updated?
How are refunds, merchant classifications, and missing observations handled?
Does the dataset provide enough historical depth to test an investment hypothesis across different market conditions?
Investment consideration: Transaction data can be valuable, but panel bias, merchant mapping, changes in coverage, and seasonality can distort conclusions. Investors should validate whether observed trends reflect genuine business performance rather than changes in the dataset itself.
2. Receipts, Loyalty, and Product-Level Purchase Data
Best for: Consumer staples, retail, food and beverage, household products, and brand-level research.
While transaction data can show how much consumers spend, receipt and loyalty datasets may reveal more detail about what they purchase.
Depending on the source, these datasets can provide information about product categories, brands, purchase frequency, basket composition, promotions, and repeat buying behavior.
An investor researching a consumer packaged goods company, for example, might examine whether a product is gaining traction, whether customers are trading down to cheaper alternatives, or whether promotional activity is influencing purchasing behavior.
These signals can be especially relevant when investors want to understand differences between overall category growth and the performance of individual brands.
Questions to ask when evaluating a dataset:
Does the dataset identify individual products, brands, or broader categories?
How representative are the participating consumers and retailers?
Can purchases be tracked consistently over time?
How well does the dataset capture promotions, returns, and changes in product availability?
Does it offer meaningful incremental information beyond transaction data?
Investment consideration: Product-level detail can improve research granularity, but a limited sample or incomplete retailer coverage can produce misleading conclusions about the broader market.
3. Web Traffic and App Usage Data
Best for: Technology, e-commerce, travel, digital advertising, software, and online marketplaces.
Web traffic and app usage data can help investors monitor changes in digital engagement, customer acquisition, and online business activity.
Potential indicators include website visits, session duration, downloads, active users, engagement patterns, and changes in traffic sources.
For an investor researching a subscription software company, for example, changes in web traffic or application engagement might help frame questions about demand, competitive positioning, or customer interest.
For e-commerce businesses, digital traffic trends can provide another perspective on consumer demand and brand visibility.
Questions to ask when evaluating a dataset:
How is traffic or usage measured and estimated?
What share of relevant websites, applications, or devices is represented?
Are estimates calibrated against observable company disclosures?
How does the methodology account for bots, privacy changes, and tracking limitations?
Can the data distinguish meaningful engagement from low-intent visits?
Investment consideration: Web traffic is not equivalent to revenue, and downloads do not necessarily translate into active users or paying customers. Investors should test the relationship between observed activity and the financial metrics they are trying to understand.
4. Geolocation and Foot Traffic Data
Best for: Retail, restaurants, shopping centers, travel, hospitality, and physical-world businesses.
Geolocation data can help investors understand patterns of movement and visits to physical locations.
Depending on coverage and methodology, datasets may help estimate store visits, shopping center traffic, visits to competing businesses, or changes in activity across geographic markets.
Consider an investor researching a national retailer. Foot traffic data might help compare visit trends across locations, identify geographic differences in demand, or assess whether a new store format is attracting customers.
Investors may also use these datasets to monitor activity around industrial facilities, transportation hubs, or other economically significant locations.
Questions to ask when evaluating a dataset:
How is a visit defined and distinguished from a pass-by?
How representative is the underlying device panel?
How accurately are devices assigned to locations?
How are repeat visits, privacy restrictions, and changes in panel composition handled?
Can results be analyzed consistently across locations and time periods?
Investment consideration: Foot traffic is a measure of activity, not necessarily sales. The relationship between visits, conversion rates, and revenue varies by business model and should be tested rather than assumed.
5. Satellite Imagery and Remote Sensing
Best for: Energy, agriculture, mining, industrials, real estate, and infrastructure.
Satellite imagery can provide a view of physical activity that may be difficult to observe through traditional company disclosures.
Depending on the imagery and analytical methods used, investors can investigate changes in agricultural conditions, industrial activity, inventories, construction progress, or activity around selected facilities.
For example, an investor analyzing commodity markets might use satellite-derived indicators to assess changes in storage levels or activity at relevant facilities. An investor researching industrial companies might examine imagery-based estimates of activity at factories, ports, or other sites.
The value often comes from combining imagery with other datasets rather than interpreting an image in isolation.
Questions to ask when evaluating a dataset:
How frequently are relevant locations observed?
What spatial resolution is available?
Are measurements based on raw imagery or derived analytical estimates?
How are weather, cloud cover, and other observation limitations handled?
Has the methodology been validated against independent information?
Investment consideration: Satellite imagery can provide distinctive information, but coverage gaps, processing assumptions, and the difficulty of translating physical observations into financial outcomes require careful validation.
6. Maritime, Shipping, and Supply Chain Data
Best for: Industrials, energy, commodities, shipping, manufacturing, and global trade.
Supply chain data can help investors monitor the movement of goods, changes in trade flows, shipping activity, and potential disruptions to industrial production.
Sources may include vessel tracking, port activity, freight information, customs records, trade flows, and other logistics indicators.
For an investor researching an industrial company, changes in shipping activity might help generate questions about inventory levels, production schedules, or end-market demand.
Similarly, commodity investors may investigate vessel movements and trade flows to better understand the movement of relevant raw materials.
Questions to ask when evaluating a dataset:
What portion of relevant shipments or vessels is observable?
How are missing observations and reporting delays handled?
Can the data distinguish changes in activity from changes in coverage?
How accurately can shipments be linked to companies, products, or commodities?
Can the dataset be combined with other information to test a specific investment thesis?
Investment consideration: Shipping and supply chain indicators can be affected by rerouting, weather, geopolitical developments, and changes in reporting. A decline in observed activity does not necessarily imply a decline in underlying demand.
7. Job Postings and Workforce Data
Best for: Technology, enterprise software, healthcare, financial services, and labor-intensive industries.
Job postings and workforce datasets can provide additional insight into hiring demand, skill requirements, organizational changes, and potential investment in new capabilities.
For example, an investor researching enterprise software companies might monitor hiring for sales, engineering, or artificial intelligence roles to develop a view on business priorities and expansion plans.
Across industries, job postings may help identify emerging technologies, changes in labor demand, or differences between companies pursuing similar strategies.
Workforce-related datasets can also offer clues about the pace and direction of organizational change.
Questions to ask when evaluating a dataset:
How comprehensively does it capture job postings across relevant platforms?
Are duplicate, evergreen, or automatically generated listings removed?
Can postings be mapped accurately to companies and job functions?
How are changes in recruitment platforms and hiring practices handled?
Is there evidence that the observed trends correspond to meaningful business activity?
Investment consideration: A job posting indicates a stated hiring need, not a completed hire or a guaranteed increase in spending. Investors should distinguish between hiring intentions, realized employment, and financial outcomes.
8. Pricing, Product Assortment, and E-Commerce Intelligence
Best for: Retail, consumer goods, e-commerce, travel, and competitive strategy research.
Digital commerce data can help investors monitor prices, product availability, assortment changes, discounting, and other aspects of competitive behavior.
For a retailer or consumer brand, these signals may help investors assess pricing pressure, promotional intensity, product launches, and changes in competitive positioning.
An investor researching a consumer electronics company, for example, might examine changes in advertised prices across retailers or compare the availability of selected products over time.
For companies with significant online distribution, these datasets may provide another way to assess market dynamics between earnings reports.
Questions to ask when evaluating a dataset:
How often are prices and product listings captured?
Can products and variants be matched consistently across retailers?
Does the dataset distinguish advertised prices from actual transaction prices?
How are stockouts, promotions, and discontinued products treated?
Is retailer and geographic coverage sufficient for the research question?
Investment consideration: Listed prices and product availability do not necessarily represent realized revenue, units sold, or profitability. Interpretation should account for promotions, channel mix, and changes in assortment.
9. Search Trends, Online Sentiment, and Digital Consumer Interest
Best for: Consumer brands, technology, travel, media, and companies exposed to changing public interest.
Search activity and online discussion can provide additional context on consumer interest, emerging topics, and changes in attention toward companies, products, or industries.
For example, an investor researching a travel business might examine changes in search interest around destinations or travel services. A technology investor might monitor shifts in attention toward products, platforms, or emerging technology categories.
Depending on the source, these datasets can help investors identify questions to investigate further or compare patterns of interest across brands and markets.
Questions to ask when evaluating a dataset:
What platforms, search engines, or online sources are included?
Does the data measure absolute activity or a normalized index?
How are bots, spam, coordinated activity, and duplicated content handled?
Can changes in interest be separated from news-driven attention?
Has the relationship between the signal and the relevant business outcome been tested?
Investment consideration: Attention is not the same as purchase intent, and online sentiment can be noisy or unrepresentative. These sources are often most useful when combined with more direct indicators of business activity.
10. AI Infrastructure, Cloud Activity, and Technology Adoption Signals
Best for: Semiconductors, cloud computing, software, data centers, utilities, and companies exposed to AI investment.
Artificial intelligence is creating new research questions around the infrastructure required to train and run models, the companies supplying that infrastructure, and the businesses adopting AI technologies.
Relevant data sources may include indicators of cloud usage, computing capacity, semiconductor shipments, power demand, data center development, technology hiring, and other measures of investment or adoption.
For example, an investor researching semiconductor suppliers might combine shipment indicators with other evidence of demand from cloud providers and data center operators.
An investor studying AI software adoption might examine relevant technology usage or hiring trends, where reliable data is available, to help assess whether adoption is expanding.
These datasets can also help investors investigate the relationship between infrastructure investment, operating costs, and potential commercial returns.
Questions to ask when evaluating a dataset:
Does the data measure actual activity, estimated usage, or stated investment intentions?
How closely does the metric relate to the company or industry being researched?
How frequently is the data updated, and what is the reporting lag?
Can changes in measurement methodology be separated from changes in underlying activity?
Does the dataset offer a differentiated perspective beyond public disclosures and conventional industry research?
Investment consideration: AI-related activity is not automatically evidence of profitable growth. Investors should distinguish infrastructure spending, actual utilization, customer adoption, and monetization rather than treating them as interchangeable signals.
How to Evaluate Alternative Data Before Adding It to Your Research Process
Identifying a promising data source is only the first step. Before purchasing a dataset or incorporating it into an investment workflow, research teams should evaluate whether it is reliable, relevant, and worth the cost.
A structured evaluation can help teams compare vendors and avoid spending time on datasets that do not meet their requirements.
1. Start with a specific investment question
Define what you want to understand, forecast, or monitor. A clear research question makes it easier to determine which data source is relevant and how its usefulness should be measured.
2. Assess quality and coverage
Review historical depth, geographic coverage, update frequency, revisions, missing observations, and methodology. Understand what the data captures—and what it does not.
3. Test incremental value
Determine whether the dataset contributes information beyond your existing research inputs. Where appropriate, evaluate its relationship to relevant outcomes using a disciplined methodology that avoids look-ahead bias and overfitting.
4. Review legal, licensing, and compliance requirements
Understand permitted uses, privacy considerations, data provenance, redistribution restrictions, and any applicable contractual or regulatory requirements. Involve the appropriate legal and compliance stakeholders early.
5. Evaluate integration and total cost
Consider delivery formats, infrastructure, implementation effort, ongoing maintenance, and licensing costs. A promising signal may not justify the expense if it is difficult to operationalize or does not improve the research process sufficiently.
6. Define success criteria before a pilot
Establish what a successful evaluation looks like, who will assess it, and how the findings will inform a purchase decision. Clear criteria help prevent an open-ended trial from consuming research resources without producing a decision.
Discover Your Next Alternative Data Source at BattleFin
Evaluating alternative data takes more than reading a vendor description. It requires understanding the underlying methodology, asking detailed questions, comparing potential solutions, and determining how a dataset might fit into an investment workflow.
BattleFin Discovery Day events bring financial institution data buyers together with alternative data and AI providers through focused discussions, product discovery, and curated one-to-one meetings.
For investment teams building their 2027 research priorities, two upcoming events offer opportunities to explore new sources of data and engage with the companies developing them.
BattleFin Discovery Day New York Fall 2026
October 29, 2026 | New York Athletic Club, New York City
New York Fall brings together data strategists, data scientists, portfolio managers, and alternative data and AI providers to examine emerging investment themes and practical applications.
The published agenda includes discussions on consumer and card data, physical-world signals from geospatial and supply chain sources, and the role of AI in investment research.
For buyers, the event offers an opportunity to investigate datasets relevant to specific research questions, hear how providers approach different use cases, and identify solutions worth evaluating further.
Explore Discovery Day New York Fall 2026
BattleFin Discovery Day Miami 2027
January 20–22, 2027 | Nobu Eden Roc, Miami Beach, Florida
Discovery Day Miami looks ahead to the next generation of alternative data and AI-enabled investment research, with a particular focus on agentic AI, autonomous workflows, and emerging approaches to finding alpha.
For data buyers, Miami provides another opportunity to explore new datasets, assess data and analytics solutions, and engage directly with providers as research priorities for 2027 take shape.
Whether your team is evaluating consumer spending indicators, physical-world activity, technology adoption, or new AI-enabled research tools, the objective is to connect your investment questions with relevant solutions.
Explore Discovery Day Miami 2027
Final Thoughts: Build a More Informed Alternative Data Strategy for 2027
The best alternative data strategy isn't about collecting the largest number of datasets. It's about finding the sources that help your team answer important investment questions more effectively.
Consumer transactions can provide insight into spending. Geolocation and satellite imagery can help monitor physical-world activity. Web and workforce data can offer additional perspectives on digital demand and business priorities. AI infrastructure signals can help investors investigate the investment cycle surrounding emerging technologies.
Each category has potential applications, but every dataset must be evaluated on its own merits.
As you plan your 2027 research agenda, define your most important questions, identify the data categories that could help answer them, and establish a disciplined process for evaluating providers.
Ready to discover your next data source? Join BattleFin at Discovery Day New York Fall on October 29, 2026, or Discovery Day Miami, January 20–22, 2027, to explore alternative data and AI solutions and connect with providers relevant to your research needs.
Visit BattleFin's Discovery Day events page to learn more.
If you enjoyed this article, you may also like, Exabel Releases New Buy-Side Research on Alternative Data Trends Heading into 2026.