The Ultimate Guide to Alternative Data for Financial Analysis

The finance industry is built on information. Analysts who find better signals faster than their competitors are the ones who protect client capital and surface opportunity before others can price it in. That is the core appeal of alternative data: it captures what is happening in the real world before official sources report it.

This guide covers what alternative data is, where it comes from, which types matter most, how buy-side and sell-side analysts use it, and how to evaluate the providers selling it. 

What Is Alternative Data?

Alternative data refers to information used in financial analysis that is generated or collected outside traditional channels. The company doing the analysis does not create this data itself, and it does not come from official sources like press releases, SEC filings, or quarterly earnings reports.

What makes it valuable is precisely that distance from conventional sources. Because it is collected continuously and reflects real-world behavior rather than curated corporate disclosures, it gives analysts a different kind of signal: one that is timelier, harder to replicate, and often more predictive.

How Is Alternative Data Generated?

Alternative data is produced by three broad types of sources:

• Sensors: Satellites, mobile beacons, surveillance cameras, and WiFi hotspots collect geospatial information such as weather conditions, physical imagery, and movement patterns in and around specific locations.
• Individuals: Every time a person posts on social media, leaves a product review, visits a website, takes a survey, or uses a mobile app, they generate data that businesses and investors can use. These actions are non-transactional but cumulatively informative.
• Business processes: Corporate operations produce data as a byproduct. Payment processors track transaction volumes. Retailers generate receipt data. Company websites publicly advertise prices, inventory levels, and product availability. All of this can be mined.

Sensor- and individual-generated data tends to be cheaper to acquire but requires significant processing before it is usable. Business-process data is usually cleaner and faster to analyze but comes at a higher cost.

5 Main Types of Alternative Data

The five categories below represent the most widely used forms of alternative data in financial analysis. This is not an exhaustive list, but these are the types SafeGraph works with most closely.

1. Transactions

Transaction data covers the buying and selling activity that flows through businesses every day. Companies typically disclose very little of this publicly, and only when legally required. However, analysts can access it through other means: email receipts tracked at scale, or anonymized and aggregated debit and credit card transaction records purchased from the payment processors that facilitate them. SafeGraph’s Spend dataset is built on this approach, using permissioned consumer spending data that is anchored to specific physical locations to add geographic context to transaction signals.

2. Human Mobility

Human mobility data captures anonymized measurements of how people move through a defined geographic area over time. At its most basic level, this includes which places people visit, how many visitors a location receives, and how long people stay. More granular datasets include origin and destination patterns, giving analysts a clearer picture of consumer behavior at scale without identifying any individual.

3. Point of Interest (POI)

Point of interest data provides structured information about non-residential locations that people visit for commercial or informational purposes. Standard attributes include hours of operation, price tier, brand affiliation, business category, street address, and contact details. SafeGraph’s Places dataset covers millions of locations globally and is maintained through ongoing data quality processes to ensure attributes stay accurate and current.

4. Property Details

Property data covers physical and financial characteristics of land parcels and the buildings on them. Financial attributes include ownership records, assessed value, prior sales, mortgages, and leases. Physical attributes include square footage, room count, construction materials, and HVAC specifications. Building footprints, which are spatial representations of a structure’s exact physical boundaries, are a particularly useful subset. SafeGraph’s Geometry dataset provides these footprints and supports precise geographic attribution for analysis.

5. Demographics

Demographic data aggregates population characteristics at the neighborhood, city, state, or regional level. Attributes like age distribution, income levels, employment status, educational attainment, and ethnic composition give analysts a contextual layer for understanding who lives or works in an area and what they are likely to spend money on. A significant portion of this data is publicly available through government sources. SafeGraph offers a cleaned and structured version of US Census Bureau American Community Survey data to make it easier to work with.

Benefits of Using Alternative Data for Analysis

Traditional financial data has a well-known limitation: it is published on a fixed schedule, which means it is always retrospective by the time it reaches analysts. Alternative data addresses this in four concrete ways:

• Immediacy: Alternative data is produced daily or more frequently, which means analysts can monitor conditions between official reporting windows rather than waiting for the next scheduled disclosure.
• Frequency: Higher production frequency means larger sample sizes for trend analysis. A signal that appears repeatedly across multiple periods is much more likely to be a genuine trend than a one-time anomaly.
• Context: Knowing that a company’s revenue is declining is one thing. Understanding why it is declining, whether due to reduced foot traffic, competitive store openings nearby, or negative social sentiment, is another. Alternative data provides that surrounding context.
• Creative angles: Analysts working with unconventional data sources can build investment theses that competitors relying solely on traditional data cannot replicate. This includes both identifying hidden risks in deals that look safe on paper and surfacing opportunities in deals others overlook.

Alternative Data Use Cases for Financial Investors

How alternative data is applied depends partly on the type of role the analyst occupies. Buy-side analysts work for specific clients and focus on investment selection and portfolio management. Sell-side analysts work for brokerages or investment banks and produce research and recommendations for a broader audience. The data types overlap considerably; the strategic use differs.

Buy-Side vs. Sell-Side: A Quick Overview

• Buy-side analysts: Their role is to find investment opportunities and make recommendations to a specific client, often a hedge fund or private equity firm. Accuracy and depth matter more than speed.
• Sell-side analysts: Their role is to produce financial research for clients of a brokerage or investment bank. Speed and breadth matter, as they compete to publish timely analysis that keeps clients engaged with the firm.

1. Monitoring Online Activity

Role type: Both

Web traffic patterns, mobile app usage, social media commentary, online product reviews, and logistics data all reflect consumer intent before that intent shows up in financial statements. Analysts tracking these signals can spot shifts in brand sentiment or competitive positioning weeks ahead of official reporting.

2. Deducing Industry or Brand Relationships

Role type: Both

The physical proximity of complementary businesses has real commercial significance. A fitness studio opening near a health food retailer, for example, is a positive signal for both. Geospatial alternative data makes it possible to model these relationships quantitatively rather than anecdotally.

3. Forecasting Demand

Role type: Sell-side

Transaction data gives the clearest demand signal, but POI data contributes meaningfully too. Tracking store openings and closings in a category, combined with foot traffic trends around those locations, helps sell-side analysts build demand forecasts that outperform those based on earnings alone.

4. Sourcing and Evaluating Deals

Role type: Buy-side

Buy-side analysts use alternative data to evaluate potential investments from multiple angles simultaneously: competitive density around key locations, visitor volume patterns, brand affinity signals derived from co-visit data, and geographic comparables. This multi-dimensional view supports sharper initial screening and stronger conviction before committing capital.

5. Modeling Financial Performance

Role type: Sell-side

Financial performance models improve meaningfully when they incorporate consumer demand signals, supply chain data, geographic comparables (locations with similar POI density and demographic profiles), and social sentiment. Sell-side analysts who integrate these layers build more defensible projections and deliver more differentiated research.

6. Performing Due Diligence

Role type: Buy-side

Due diligence in large private transactions leaves little room for error. Alternative data gives buy-side analysts a richer evidence base for stress-testing every dimension of a deal, from site-level performance to competitive exposure to demographic tailwinds and headwinds.

7. Finding Competitive Edges

Role type: Sell-side

Speed is a real competitive differentiator for sell-side research. Because alternative data updates more frequently than traditional disclosures, firms that use it can publish meaningful analysis faster. Tracking store opening and closing patterns in a geographic market, for instance, can produce a leading indicator of a retail company’s expansion or contraction trajectory long before management commentary arrives.

8. Managing Portfolios

Role type: Buy-side

After capital is deployed, the monitoring challenge begins. Alternative data streams that update continuously give portfolio managers the ability to catch unexpected deterioration early. A sudden drop in visitor volume at a key retail location, for example, is a signal worth investigating well before the next earnings call.

Top Alternative Data Providers: What to Look For + Comparisons

The alternative data market includes dozens of providers across very different data categories. Choosing the right one requires more than matching on data type. The table below covers what to evaluate before you commit to a vendor relationship.

Evaluation Criteria

Beyond these six criteria, it is worth asking whether the provider produced the data themselves or aggregated it from third parties. Each sourcing path introduces different quality and bias risks that are worth understanding before the data enters your models.

8 Alternative Data Providers Worth Evaluating

The providers below specialize in different data types. Where their offerings overlap, differentiation typically comes down to coverage depth, update frequency, and how much processing work is required before the data is usable.

Provider Profiles

1. SafeGraph | Major data types: POI, property, transactions. Key use cases: retail investment, consumer insights, risk assessment, real estate site selection. SafeGraph is the market leader in global POI data. The Places and Geometry datasets cover detailed attributes and building footprints for millions of locations worldwide. The Spend dataset is the first US consumer transaction dataset anchored to where spending occurs, providing geographic context that spending-amount data alone cannot supply.
2. ClimateCheck | Major data types: US properties, historical weather. Key use cases: real estate investment and risk assessment. ClimateCheck runs historical US weather data through more than 25 internationally recognized climate change models to generate 30-year projections for over 140 million US homes, covering drought, storm, fire, and flood vulnerability.
3. Greenwich.HR | Major data types: financial, labor statistics. Key use cases: workforce analytics, talent acquisition. Greenwich.HR allows analysts to assess corporate health through a workforce lens: open positions, compensation ranges for more than 80% of roles, and hiring activity across 5 million companies in over 200 countries.
4. HARNESS Data | Major data types: UK POI, properties, addresses, PDF document analysis. Key use cases: real estate investment, insurance risk, logistics, fraud prevention. HARNESS has the most complete UK address, property, and POI dataset available, including a free per-square-meter price assessment for over 16 million properties in England and Wales.
5. Infutor | Major data types: property, demographics, phone and email metadata, automotive transactions, addresses. Key use cases: real estate, consumer profiling. Infutor covers US consumers across multiple dimensions, making it a useful source for analysts who need to build a broad consumer portrait alongside property or automotive investment signals.
6. Transparent | Major data types: vacation rental property. Key use cases: travel, hotel, and real estate investment. Transparent tracks more than 35 million vacation rental listings across all major booking platforms, with over 50 attributes per listing including occupancy limits, pricing, and minimum booking periods.
7. Veraset | Major data types: property, mobility. Key use cases: visit attribution, consumer insights, site selection. Veraset aggregates footfall data across more than 150 countries, with a higher-resolution US dataset covering building footprints for over 6 million POI to distinguish actual building entry from passerby traffic.
8. Vertical Knowledge | Major data types: online transactions, rental property, transportation, business summaries, POI. Key use cases: retail insights, real estate, travel, corporate research. Vertical Knowledge sources public web data and transforms it into privacy-compliant structured datasets covering retail locations, rental properties, travel metrics, and company summaries.

Conclusion

Alternative data has moved from a niche advantage to a standard part of how financial analysts build conviction. The firms that integrate transaction records, mobility patterns, POI attributes, property details, and demographic context into their workflows are operating with a fuller picture of the market than those relying on quarterly disclosures alone.

The right starting point depends on the question you are trying to answer. For analysts focused on retail performance, real estate site selection, or competitive positioning, POI and geometry data offer some of the clearest, most actionable signals available, and they pair well with transaction and demographic datasets for a more complete view.

Whether you are evaluating providers for the first time or looking to add a new data layer to an existing workflow, the evaluation criteria in this guide (scope, attribution, accuracy, freshness, interoperability, and cost) provide a practical framework for making that decision with confidence.