Alternative data represents an invaluable and rapidly growing resource for investors seeking an information edge in financial markets. As traditional datasets like financial statements become more commoditized, alternative data from unconventional sources offers a way to gain unique, actionable intelligence on companies and industries.
In this comprehensive guide, we‘ll explore the world of alternative data, including the top data types and sources in 2024, how investors use this data, and real examples that demonstrate the power of alt data for investment analysis and decision making.
What is Alternative Data?
The term “alternative data” refers to information coming from non-traditional sources outside of companies‘ financial statements, earnings calls, investor presentations, and other conventional disclosures. This includes data derived from sources like:
- Web traffic and digital behavior metrics
- Location data from mobile devices and connected cars
- Satellite and aerial imagery
- Sentiment analysis across social media, news, reviews, and forums
- Credit card transactions and point of sale data
- Supply chain monitoring with IoT sensors
Whereas traditional datasets offer backwards-looking summaries, alternative data provides dynamic, real-time views into business performance and trends. From web traffic and sentiment to supply chain analytics, alt data delivers the kind of intelligence that moves markets.
Top Alternative Data Sources in 2024
Now let‘s explore some of today‘s most important and widely-used alternative data sources:
Web Scraping Data
Extracting insights from across the internet through web scraping represents a primary alternative data type today. From e-commerce platforms to job listings to local review sites, the web holds vast stores of previously untapped information relevant to investment analysis.
Use cases for scraped web data include:
- Monitoring e-commerce demand indicators like sales ranks, ratings, reviews, and inventory levels.
- Tracking hiring demand and employee sentiment shifts via job listing sites.
- Performing rapid analysis of menu changes, customer reviews, and foot traffic for restaurants.
Leading data providers like Thinknum, YipitData, and Quad Analytix offer web scraped datasets covering sectors from retail to real estate to hospitality. Expect web scraping to continue growing in adoption among investors.
Geospatial Data
Both satellite imagery and location data from mobile devices and connected cars have become vital geospatial intelligence streams. By linking activity across brick-and-mortar sites to financial performance, geospatial data provides reliable leading indicators for industries like retail, auto, and real estate.
Investors can leverage geospatial data to:
- Estimate store or dealership visitors by analyzing satellite images of parking lots.
- Monitor manufacturing facility activity using spatial patterns as proxy metrics.
- Model construction progress compared to permits based on sequential build site photos.
From drones to image recognition AI, new technologies continue advancing geospatial analytics capabilities for investors.
Credit Card and POS Data
Debit/credit transaction datasets have emerged as a highly valued alternative data source, offering detailed visibility into consumer spending patterns across merchants, sectors, geographies, and demographics.
Investors can gain unique insights by applying transaction data to:
- Track retail sales growth compared to earnings reports.
- Identify shifting discretionary vs. non-discretionary spend ratios.
- Detect early performance divergence between competitors.
As leading provider Second Measure explains, "By analyzing nearly $1 trillion in annualized credit card spending, Second Measure provides real-time insights into revenue trends, customer demand, and market share across sectors and competitors."
Powerful on its own, integrating credit card data with other alternative datasets creates even richer perspectives for investors.
Supply Chain Monitoring
Modern supply chains generate massive volumes of real-time telemetry from sensors, meters, transponders, and other Internet of Things (IoT) devices. Tapping into these complex data flows allows investors to monitor key efficiency, safety, and reliability metrics across industrial environments and transportation networks.
IoT data supports investment analysis use cases like:
- Verifying company reports on utilization rates for critical assets like pipelines, rigs, and refineries.
- Estimating mining/drilling output by analyzing equipment activity patterns.
- Profiling shipping delays, bottlenecks, and logistics performance issues.
Attracted by the potential investment insights, VC funding for industrial IoT startups offering alternative supply chain data exceeded $6 billion globally in 2021.
News and Social Media Sentiment Data
Natural language processing and machine learning tools have opened the door to mining value from unstructured text data across news, blogs, forums, reviews, and social media. The emotions and ideas captured in these massive text corpora offer indicators of shifting consumer preferences, brand reputation, product quality, corporate culture, and more.
Applications for analyzing sentiment data include:
- Monitoring customer satisfaction shifts from review site text.
- Detecting PR crises, executive controversies, and lawsuit risks from news.
- Evaluating employee morale and turnover likelihood via public posts.
Look for sentiment analytics to become table stakes for investor research as AI continutes advancing text understanding capabilities.
Challenges and Limitations of Alternative Data
While alternative data represents a game-changer for investment research, effectively leveraging these novel data streams brings inherent obstacles around quality, comparability, and interpretability.
Data Quality Concerns
Since alternative datasets come from less structured, less regulated sources than financial statements or government statistics, they raise legitimate quality questions on accuracy, consistency, completeness, bias, and more.
Investors can address data quality issues through:
- Rigorously vetting data partners on their collection methods, quality control protocols, and transparency standards.
- Combining multiple alternative data signals to validate insights across independent sources.
- Correlating alt signals with traditional data benchmarks to surface inconsistencies.
The most experience data providers invest heavily in quality assurance and allow clients to probe their methodologies.
Comparability Constraints
Unlike time series from financial reports that apply standard definitions across companies and time periods, alternative data metrics can vary significantly in what they specifically measure and how consistently they measure it.
Investors should interpret alt dataset comparability with caution or instead focus comparative analysis on:
- Percentage changes for a company over time rather than absolute values.
- Rank ordering companies according to a metric instead of comparing magnitudes.
- Segmenting industries, geographies etc. with tighter definitions when comparing metrics.
Challenges Interpreting Data
Alternative data reveals correlates and leading indicators. But transforming these signals into investment insights requires skill and experience interpreting what drives the underlying activity behind the numbers.
For example, web traffic growth could reflect rising demand or indicate technical issues funneling traffic inefficiently. Without putting figures in proper context, misinterpreting drivers can lead investors astray.
Overcoming limited interpretability mandates:
- Constructing hypotheses on what business realities could explain the data.
- Corroborating multiple datasets to confirm or reject hypotheses.
- Retaining specialized expertise where needed to decode industry-specific signals.
The Hyper-Growth Alternative Data Market
From hedge funds to private equity players to Fortune 500 companies, the universe of organizations incorporating alternative data for intelligence and analytics continues expanding at a stellar pace.
Key statistics demonstrating the hyper-growth in alternative data adoption:
- In 2022, 32% of institutional investment organizations had an allocation to alternative data, nearly doubling since 2019.
- Total spend on alternative data among asset managers reached $813 million in 2021, rising over 40% year-over-year.
- The global market for alternative data is projected to surge to $19 billion by 2028 according to Precedence Research.
- Top-quartile hedge funds generate over 70% of alpha gains from alternative data according to results from Eurekahedge.
"We are still in the early days of adoption," explained Anant Srivastava, Chief Data Officer at global investment firm KKR. "The more management teams get comfortable using this type of data, the more it will end up becoming an important part of the investment decision process."
Powerful tailwinds around greater data accessibility, better analytics tools, and competitive pressure will continue accelerating alternative data usage and maturity.
Real-World Examples: Alt Data in Action for Investors
While headline figures showcase the soaring adoption, real-world examples best illustrate the investible edge alternative data can deliver:
Tracking Retail Revenue Trends
A 2018 analysis by 7Park Data compared the official earnings reports from Macy’s, Nordstrom, JCPenney, and Kohl’s with transaction data aggregated from millions of anonymous credit and debit card purchases. The card spend metrics revealed quarterly revenue changes 9 days before earnings release, highlighting outliers like Kohl’s 6% reported sales uptick against a mere 1.7% increase per 7Park’s data. Investors could have exploited this performance divergence by shorting Kohl’s ahead of a subsequent 11% share price dip after underwhelming guidance.
Monitoring Electric Vehicle Adoption
In Q3 2021, geospatial analytics provider Orbital Insight partnered with import/export data company Panjiva to assess early indicators of electric vehicle (EV) model penetration based on actual vehicle flows into Norway – a global leader in EV sales per capita. Tracking registrations of battery-powered imports before official registration statistics became available provided an information advantage to ascertain demand shifts as automakers like Volkswagen, Tesla, and Ford rolled out new offerings. Investors could derive insights on brand momentum, price elasticity, and consumer preferences to inform bets across manufacturers attempting to lead the pivotal transition to EVs.
Predicting Oil & Gas Equipment Distress
Geologic Systems analyzed billions of IoT telemetry signals from oil rig assets to profile typical pumpjack waveform activity under normal operations. Their machine learning models detected deviance patterns predictive of emerging failure modes. In multiple cases, models alerted anomalies up to 8 months ahead of catastrophic failures requiring expensive repairs and extended production halts. This intelligence on equipment distress hotspots enabled energy investors and drillers to optimize maintenance routing to minimize costs and risk. With over $9 billion spent annually fixing oil/gas infrastructure breakdowns, alt data offers an opportunity to avoid disruptions and beat analyst forecasts.
The Future of Alternative Data
Already transforming investing, alternative data still remains in its early stages, with enormous room left to penetrate strategies and fuel returns.
Key predictions on what lies ahead:
- Alternative data spend by asset managers will quadruple to over $5 billion by 2030 according to Opimas research.
- Advances in AI technologies like computer vision, NLP, predictive analytics, and causal machine learning will uncover even more investible signals hidden within alt data.
- New LOD (Level of Detail) satellite constellations from SpaceX’s Starlink to microsatellite networks will provide daily or even hourly image refresh rates globally.
- Wearables, autonomous vehicles, smart homes/cities, digital twins, and exponential IoT growth will expand alt data possibilities.
- Stricter privacy laws and ethical concerns could regulate some alternative data practices and providers.
The bottom line is that alternative data has shifted from niche curiosity to mission-critical investment research pillar. As alt data literacy spreads, those putting in the work to master these new signals stand to amplify their edge.