The Role of Alternative Data in Asset Management Research

Is traditional data really enough to stay ahead in today’s markets? Not quite. In fact, according to a report, the global alternative data market size is expected to reach $143.31 billion by 2030. It's a clear sign of progress.

Markets no longer operate on conventional patterns alone. Things are moving faster, signals are harder to spot, and real-time insights matter more than ever. As a result, relying on quarterly earnings reports and financial statements alone simply won’t cut it anymore.

Here’s what’s holding asset managers back:

• Rigid dependence on legacy data sources.
• Limited visibility into real-time consumer behavior.
• Information overload without prioritization.
• Inefficient data workflow automation

Now, picture gaining insights from satellite images or predicting revenue changes based on social media. In reality, it’s alternative data integration, and it’s transforming asset management research.

Could this be the future of asset management research? Let’s see.

This blog will walk you through the role of alternative data in asset management research. Continue reading!

What is Alternative Data?

Alternative data refers to non-traditional datasets used in asset management research to gain deeper market insights. Unlike conventional financial data, this includes:

• Satellite imagery of retail parking lots
• Social media sentiment tracking
• Credit card transaction patterns
• ESG (Environmental, Social, Governance) metrics
• Website scraping and clickstream behavior
• Job postings and hiring patterns

These sources provide a broader view of markets, helping investors spot trends and reduce risk in ways regular data can't. Isn’t it time to think beyond the usual numbers?

Structured vs. Unstructured Alternative Data

Alternative data exists as both structured and unstructured data. Thus, understanding the difference between them can help you explore powerful insights that traditional data just can’t provide.

Let’s break it down simply to make it clearer:

What’s driving this trend? Asset managers crave agility and edge, and here’s why alternative data integration is catching fire:

• Enables real-time decision-making.
• Unveils insights competitors might miss.
• Enhances predictive power of models.

• Offers unique angles on macro and micro trends.

Why Asset Managers Are Turning to Alternative Data

Information is no longer scarce, it’s excessive. An article recently revealed that over 78% of hedge funds now use some form of alternative data. 

Clearly, ignoring it isn’t an option.

Here are five compelling reasons why asset managers are diving in:

• Staying ahead of the curve: Data is the new oil. Data-driven investing helps identify trends before they go mainstream.
• Complementing traditional data: While earnings reports are valuable, combining them with foot traffic insights or job postings creates a fuller picture.
• Enhancing alpha generation: In a low-yield environment, outperforming the benchmark requires unique signals, not recycled strategies.
• Meeting client expectations: Today’s clients demand transparency and innovation in portfolio analysis. Alternative data answers that call.
• Gaining behavioral insights: Understanding investor or consumer behavior through social sentiment sharpens strategy.

Types of Alternative Data and Their Use Cases

At present, the investment world is crowded with noise and detail. As a result, clear signals are what give investors an edge.

Here’s an  insight from a report: 67% of investment firms reported using alternative data in 2024, up from 62% in 2023 and 31% in 2022.

Alternative data offers that clarity and here’s how:

• Geospatial Data

Satellite imagery provides an idea of retail parking lot density. As such, this offers real-time performance indicators for companies.

• Web Traffic and App Usage

Browser and app usage stats track tech engagement levels. In addition, they help forecast quarterly growth for digital-first businesses.

• Social Sentiment Analysis

Twitter, Reddit, and forums offer emotional insights. Consequently, fund managers can gauge optimism or fear around specific assets.

• Job Listings & Hiring Trends

More job openings in a sector often mean growth. Therefore, tracking hiring activity signals expansion or slowdown phases.

• ESG & Climate Data

Investors increasingly demand sustainable portfolios. As a result, ESG metrics drive asset allocations in eco-conscious funds.

Challenges in Leveraging Alternative Data

Innovation comes with its own hurdles. While promising, alternative data integration can be tricky.

Here’s why some teams struggle:

    1. Data Sourcing and Licensing

Finding reliable sources isn’t easy. Moreover, costs and legal usage rights remain a grey area.

Tips to tackle this:

• Partner with trusted data providers: Form partnerships with well-established data vendors who offer transparent pricing and licensing options.
• Utilize open data sources: Explore publicly available datasets or government-backed sources to reduce costs and avoid legal concerns.
• Negotiate usage rights upfront: Be clear on the data's intended use and negotiate licensing terms to ensure compliance.

• Regularly audit data usage: Track and audit data sources to ensure that the data complies with licensing agreements and legal restrictions.

  2. Integrating with Existing Investment Models

Old tools can’t process new data. Thus, firms need better infrastructure and updated workflows.

Tips to tackle this:

• Invest in modern infrastructure: Upgrade your technology stack to handle large volumes of data and integrate new sources efficiently.
• Use scalable platforms: Choose platforms that allow easy integration with existing models, allowing flexibility for future data needs.
• Automate data cleaning and processing: Automate repetitive tasks like data cleaning to improve workflow efficiency and data readiness.
• Test and iterate: Start small by integrating a few sources at a time, assess the performance, and adjust as necessary.

     3. Signal vs Noise Dilemma

Not all data is valuable. Consequently, separating actionable insight from fluff requires context.

Tips to tackle this:

• Develop clear criteria for relevance: Set up filters to identify high-quality, relevant data and avoid noise.
• Use AI and machine learning: Implement AI finance tools to help analyze data patterns, allowing you to focus on insights that truly matter.
• Leverage domain expertise: Collaborate with industry experts to better understand the context and relevance of the data.
• Perform data validation: Continuously validate the data’s accuracy and applicability before using it in decision-making processes.
  
  

  4. Regulatory and Compliance Concerns

Using personal or scraped data can be risky. Hence, staying compliant with SEC or GDPR rules is non-negotiable.

Tips to tackle this:

• Stay updated on regulations: Regularly review the latest regulatory guidelines to ensure compliance, especially with SEC and GDPR rules.
• Use ethical data practices: Ensure all data is sourced ethically and respects privacy rights, focusing on transparency.
• Seek legal counsel: Work with legal teams to ensure all data usage complies with applicable laws and regulations.
• Document and audit data usage: Maintain thorough records of how data is sourced, processed, and used, ensuring full transparency for audits.
  
  

   5. Talent and Technology Requirements

Skilled data scientists aren’t always on hand. In turn, building a capable team takes time and money.

Tips to tackle this:

• Invest in training: Upskill your current team with courses in data science, machine learning, and alternative data analysis.
• Partner with external experts: Bring in specialized consultants or contract workers for short-term needs to fill skill gaps.
• Leverage low-code tools: Use low-code or no-code platforms that allow less experienced team members to engage with data models.
• Promote a data-driven culture: Encourage team collaboration around data, integrating insights into decision-making at all levels.

Splore: Simplifying Alternative Data for Asset Managers

When complexity takes over the market, Splore offers clear direction. As the #1 generative AI platform for alternative asset managers, it transforms data overload into a strategic advantage.

Here’s how Splore redefines the game:

• Organizing fragmented data 

Splore combines scattered sources under one roof. Therefore, research analysts in asset management get a unified view, not data silos.

• Reducing research time

Its AI surfaces relevant, high-quality data in seconds. This means, teams spend less time hunting and more time analyzing.

• Game-changing simplicity

Managing Excel sheets, APIs, and web scraping is draining. That’s why, Splore’s interface feels intuitive even for non-tech pros.

Conclusion: Alternative Data is No Longer Optional

Data doesn’t wait, and neither should your strategy. The value proposition is clear: richer context, better risk models, and faster decisions. Asset management statistics already point to firms outperforming benchmarks with unique insights.

Now is the time for fund managers and analysts to embrace alternative data integration and transform how research enhances decision-making in asset management. The future of asset management research is built on curiosity & clarity and Splore can simplify complexity. 

Book a demo with Splore now to experience firsthand how its generative AI platform simplifies alternative data integration and empowers asset management research with actionable insights.