Alternative data is transforming finance and business. As an expert in web scraping and data collection, I‘ve witnessed firsthand the explosive growth in alternative data usage over the last decade. In this comprehensive guide, I‘ll share insider knowledge into the world of alternative data based on my experience extracting millions of data points for clients.
The Evolution of Alternative Data
A decade ago, alternative data was an obscure, niche resource. The few hedge funds using it were reliant on complex in-house data pipelines. Fast forward to today – over 85% of institutional investment firms now incorporate some form of alternative data into their models and research according to recent Greenwich Associates survey. It has gone from a novelty to an essential component for participants across the financial industry.
Several interlinked factors catalyzed this rapid adoption of alternative data:
- Exponential growth in potential digital data sources – web, social media, smartphones, IoT etc.
- Increased accessibility to data through scraping tools and proxy services.
- Research confirming alternative data‘s alpha generation abilities if harnessed properly.
- Pressure on funds to find new edges with traditional indicators exhausted.
- Growth of data science talent capable of working with unstructured data.
This perfect storm led to alternative data spreading through the industry like wildfire. Asset managers now see it as a requirement to remain competitive.
Categories and Sources of Alternative Data
Alternative data comes in many forms from a multitude of sources. Here are the major categories and examples:
Web Scraped Data
- Product prices/inventory at major e-commerce sites
- Local business information from aggregators like Yelp
- Reviews and ratings across industry verticals
- Job listings providing economic and company insights
Social Media Feeds
- Brand and product sentiment analysis on Twitter and Reddit
- Monitoring commentary from executives and influencers
- Identifying new trends and viral issues
Geolocation and Foot Traffic
- Visitor stats at retail locations from phone tracking
- Supply chain tracking data from trucks/ships
- Satellite imagery of activity at stores, venues, or factories
Financial Transactions
- Debit/credit card purchase data showing consumer spending
- Email receipt data revealing customer habits
- Data from point-of-sale systems
Sensors and Internet-of-Things (IoT)
- Smart home device usage patterns
- Industrial sensor performance data
- Auto telemetry from connected cars
These categories provide just a glimpse into the breadth of alternative data. From web scraped trucking databases to RFID sensors, obscure data streams hold hidden insights giants like hedge funds aggressively seek out.
Harnessing Alternative Data at Scale
While tantalizing, unlocking alternative data presents challenges. Much of it comes from unstructured web sources and lacks historical context. Collecting the data requires specialized tools and expertise.
That‘s where web scraping and proxy services come into play. Scraping solutions provide turnkey access to harvest vast amounts of web data quickly. Software scripts target desired information and extract it in a structured format. Proxies rotate IP addresses to avoid blocks from websites and gather data at scale.
These technologies enable precise, automated extraction of alternative data. Location data from thousands of stores, millions of social media posts, product prices across the web – you name it. With the right approach, targeted alternative data covering almost any domain can be collected at scale. The raw material generated far surpasses what any manual process could handle.
Advanced Analysis Unlocks the Potential
But simply collecting alternative data doesn‘t magically provide insights. The terabytes of unstructured data require rigorous analysis to unlock the value contained within. This is where things get challenging for many firms eager to tap into alternative data.
To generate meaningful signals, alternative data analysis requires data science teams possessing advanced machine learning, natural language processing, and statistical modeling capabilities. The teams must construct robust analytical pipelines able to handle the volume, noise, and dimensionality of alternative data.
Common techniques like sentiment analysis, clustering, forecast modeling, and dimensionality reduction are routinely applied to alternative data. The raw data gets transformed into derived datasets telling specific stories – predictive supply chain disruptions, changes in consumer confidence, clinical trial success, etc.
The synthesis of quality alternative data and advanced analytics is where the real magic happens. Unique, actionable insights begin emerging, providing a valuable edge.
Insider Tips on Data Quality and Reliability
After a decade in this industry, I‘ve learned to identify reliable, high-quality data streams. Here are some tips:
- Verify accuracy – Spot check samples against primary sources. Many datasets have significant errors.
- Assess coverage – Is it a narrow niche or broad domain? Broad is generally better.
- Check update frequency – Stale data loses relevance quickly.
- Scrutinize sources – Avoid suspect sites and forums with loose moderation.
- Review methodologies – How and where is the data collected? Is it sound?
- Talk to analysts – Get feedback from actual users of the data.
- Watch for biases – Does the methodology favor certain data points?
- Validate with traditional sources – Does alternative data align with financial filings over time?
These evaluations help avoid wasting time and money on flawed data. It takes time to become an astute judge. Trusted alternative data aggregators and marketplaces like Thinknum and YipitData are good starting places while you learn the landscape.
Navigating the Regulatory Gray Areas
While alternative data offers advantages, its newness also creates regulatory uncertainty. Data privacy, info security, insider trading – the rules are still emerging around appropriate alternative data usage. Personally, I advise all clients to take a conservative approach:
- Avoid any personal or non-public consumer data
- Restrict datasets to public websites and domains
- Contract legal review for any regulatory gray areas
- Scrub extracted data of any confidential personal info
- Implement stringent data access controls and auditing procedures
The prudent course is to act in good faith until formal guidance and precedent emerges. Transparency, consumer privacy, and ethics should be the foremost priorities.
Incorporating Alternative Data With Traditional Sources
Alternative data works best when blended with traditional datasets like financial statements, macroeconomic indicators, and company disclosures. Rather than seeing alternative data as a replacement for conventional analytics, it should complement them.
Here are some best practices for integration:
- Use alternative data more for discovery and generating hypotheses. Avoid overreliance.
- Leverage alternative data to fill observational gaps in conventional sources.
- Validate signals from alternative data against other datasets when possible.
- Keep fine-tuning analytical models to refine signal value from alternative data.
- Continually compare the ROI of new alternative datasets vs. existing ones.
- Keep traditional datasets as the foundation of analysis layered with alternative data.
Blending alternative data smartly with trusted conventional sources yields the most powerful insights and predictive abilities.
A Framework for Adopting Alternative Data
For organizations new to alternative data, here is a framework I recommend based on past client success:
1. Identify Target Insights
- Outline 3-5 concrete questions/metrics alternative data should address
- Focus on highest-value gaps unaddressed by current data
2. Pilot a Project
- Start with a tightly scoped pilot using 1-2 alternative datasets
- Prove out the data‘s utility and integration with operations
3. Build Internal Capabilities
- Obtain data science and analytics talent to harness alternative data
- Create processes to continuously monitor and analyze new data streams
4. Expand Data Exploration
- Incrementally add new alternative datasets that provide unique insight
- Avoid overextending into niches without clear value
5. Iterate and Refine
- Continuously evaluate ROI of alt data and adjust sources accordingly
- Fine-tune analytical models to maximize signal from alternative data
This measured approach focuses resources on high-value alternative data and builds internal capabilities over time. Soon, the insights begin flowing.
The Future of Alternative Data
We‘re still just scratching the surface of alternative data‘s potential. Advancements in AI and data science will unlock additional opportunities and use cases. However, challenges around data privacy, security, and regulations will also come more into focus as alternative data spreads.
My personal outlook is overwhelmingly positive. Alternative data empowers better research, forecasting, and decision making across sectors. But we must also ensure it ethically brings benefits to consumers and society as a whole – not just financial gains to users. I‘m excited to see how the alternative data landscape evolves in coming years!
