Exploring the Booming Alternative Data Industry: Statistics, Trends, and Getting Started
Alternative data has revolutionized the way investors, analysts, hedge funds, and corporations gain insights into markets, consumer behavior, and macroeconomic trends. Over the past decade, this once-niche discipline has grown into a multi-billion-dollar sector that provides competitive advantages to those able to harness non-traditional data. In this extensive guide, we’ll explore the current state of the alternative data industry, notable use cases, and step-by-step instructions for getting started. We’ll also include statistics, resources, and links from TickerTrends.io, docs.tickertrends.io, and other alternative data sources so you can hit the ground running.
Table of Contents
What Is Alternative Data?
Industry Statistics and Market Growth
Popular Types of Alternative Data
Key Industry Players and Data Platforms
Getting Started with Alternative Data
Common Challenges and Best Practices
Resources and Further Reading
What Is Alternative Data?
Alternative data refers to any non-traditional information that can provide novel insights into company performance, economic trends, or market conditions. Unlike traditional financial data (e.g., earnings reports, SEC filings, and analyst projections), alternative data sources may include social media sentiment, web traffic analytics, credit card transactions, satellite imagery, geolocation logs, and more.
Key reasons why alternative data has become popular:
Speed and frequency: Alternative data often updates in near real-time or with high frequency, enabling users to detect trends before conventional market reports.
Unique insights: By tapping into data sources beyond standard financial statements, analysts can discover signals that competitors might miss.
Technology and accessibility: Big data tools, APIs, and cloud computing have made the ingestion and processing of massive datasets more feasible than ever before.
Industry Statistics and Market Growth
Rapid Growth in Market Size
The alternative data market has grown exponentially in the last few years. According to research from AlternativeData.org, spending on alternative data by asset managers was estimated to reach $1.7 billion by 2020, up from $232 million in 2016. Meanwhile, various industry forecasts project the alternative data market to expand at a CAGR of over 40% between 2021 and 2027.
Proliferation of Data Providers
There are now over 450 alternative data providers globally, each delivering unique datasets—ranging from satellite data on shipping routes to consumer sentiment gleaned from social media. AlternativeData.org’s provider directory showcases a rapidly expanding ecosystem, reflecting growing demand from hedge funds, private equity firms, and even mainstream corporations.
Adoption by Different Sectors
While finance and investment firms are the largest consumers of alternative data, other sectors—such as retail, healthcare, real estate, and technology—are increasingly leveraging these insights. In 2023, over 70% of hedge funds surveyed by various industry reports indicated they use some form of alternative data in their decision-making processes.
Popular Types of Alternative Data
Web Scraping and Web Traffic
Tracking online engagement, product pricing, and even job postings
Tools like TickerTrends.io can help automate the gathering of real-time data for specific tickers, industries, or trends
Social Media Sentiment
Aggregating consumer opinions from Twitter, Reddit, and LinkedIn
Sentiment indices can be created to predict stock movements or consumer demand
Credit Card and Transaction Data
Analyzing the volume and value of purchases at specific merchants
Provides near real-time insights into company revenue performance
Satellite and Geospatial Imagery
Monitoring parking lots, shipping routes, crop yields, and construction activity
Early detection of shifts in supply chain or changes in consumer habits
Mobile and Geolocation Data
Capturing foot traffic to retail locations or event venues
Tracking consumer mobility patterns for a macro view of economic activity
Web Search Trends
Identifying growth in brand awareness or new product lines
Docs.TickerTrends.io offers detailed guidance on how to interpret search volume data
Key Industry Players and Data Platforms
1. TickerTrends.io
Overview: TickerTrends.io specializes in providing real-time web scraping and sentiment data for equities. Their platform offers APIs that track mentions, search interest, and other custom signals.
Documentation: For developers and data scientists, docs.tickertrends.io provides technical manuals, sample code, and best practices for integrating TickerTrends’ datasets into your workflows.
2. AlternativeData.org
Overview: AlternativeData.org is a popular hub for discovering new data providers and learning about the latest developments in the industry. They maintain a frequently updated directory and blog.
3. Quandl
Overview: Now part of Nasdaq, Quandl is known for its wide variety of alternative data, including sentiment indices, hedge fund performance, and global economic indicators. They provide an intuitive platform for data ingestion.
4. Kaggle
Overview: A well-known platform for data science competitions, Kaggle also offers a repository of both traditional and alternative datasets. Great for experimenting with new machine learning models.
5. Thinknum
Overview: Thinknum compiles data from social platforms, job listings, product pricing websites, and more to provide insights about company performance and market trends.
6. RavenPack
Overview: RavenPack focuses on news analytics and textual data. Their products transform unstructured data from news publications, social media, and regulatory filings into structured, actionable insights.
Getting Started with Alternative Data
Step 1: Define Your Use Case
Clearly outline what you hope to achieve. Are you looking to predict stock price movements, optimize supply chain routes, or track market sentiment? Having a well-defined question ensures you select the right data sources.
Step 2: Identify the Right Data Sources
Research potential data vendors or platforms that align with your goals. For example, if you need social sentiment data, check out TickerTrends.io. If you’re interested in satellite imagery, consider providers specializing in geospatial data.
Step 3: Acquire and Ingest Data
APIs and Integrations: Platforms like TickerTrends, Quandl, and others usually offer RESTful APIs or direct database connections.
Data Format: Ensure the dataset aligns with your analysis tools—CSV, JSON, or Excel files are standard.
Data Cleaning: Real-world data is messy. Spend time handling missing values, ensuring consistent formats, and validating data quality.
Step 4: Data Analysis and Modeling
Statistical Methods: Start with basic descriptive statistics or correlations to understand the relationships in your data.
Machine Learning: Tools like Python (with libraries like Pandas, scikit-learn, TensorFlow) or R can help in building predictive models.
Visualization: Plot data in charts or dashboards (e.g., using Matplotlib, Plotly, or Tableau) to glean insights quickly.
Step 5: Validate Findings and Iterate
Compare your alternative data insights with traditional performance indicators. If the signals seem robust, integrate them into your decision-making processes. Always iterate on your models and data sources to ensure continued accuracy.
Common Challenges and Best Practices
Data Quality and Reliability
Challenge: Alternative data sources can suffer from inconsistencies or missing points.
Best Practice: Work with reputable providers and conduct regular data quality checks.
Regulatory and Compliance Concerns
Challenge: Make sure the data you use adheres to privacy regulations (GDPR, CCPA) and does not violate insider trading rules.
Best Practice: Consult with legal teams and thoroughly review the terms of service from each data provider.
Scalability and Infrastructure
Challenge: Large volumes of data require robust infrastructure to store, process, and analyze.
Best Practice: Utilize cloud platforms (AWS, Azure, or Google Cloud) and scalable big data solutions (Spark, Hadoop).
Real-Time vs. Historical Analysis
Challenge: Balancing between immediate, real-time data needs and historical backtesting to ensure model reliability.
Best Practice: Begin with historical analysis to validate signals, then move to real-time ingestion and alerts.
Resources and Further Reading
TickerTrends.io – A leading platform for real-time web scraping and sentiment data
docs.tickertrends.io – Technical documentation, integration guides, and API references for TickerTrends
AlternativeData.org – Resource hub for industry news, data provider listings, and educational content
Quandl – Offers both traditional and alternative datasets, with an easy-to-use API
Kaggle – Provides a community platform for data science and access to a variety of datasets
Thinknum – Aggregates data from web sources, job listings, and more
RavenPack – Specializes in transforming textual data into actionable news analytics