From Commodity Data to Proprietary Alpha: How a European Long-Short Fund Built Unique Data Infrastructure To Outperform

The Client

A Multi billion AUM European equity long-short fund managing capital for institutional investors and family offices. Strong fundamental research capabilities, experienced portfolio managers, disciplined risk management—but operating with the same Terminals, market and alternative data vendors every other sophisticated investor used. Information parity meant no sustainable edge. Alpha came from interpretation skill alone, not information advantage.

The Challenge: Data Commoditization Eliminates Edge

The fund's research team subscribed to twelve alternative data providers. Credit card transaction data, satellite imagery, web traffic analytics, app download statistics, shipping container volumes—every dataset they licensed was also available to dozens or hundreds of other investment managers. Some datasets sold to thousands of subscribers.

The Head of Research explained the fundamental problem: "We'd identify a promising alternative data signal, build models around it, generate alpha for maybe six months—then watch that alpha decay as more subscribers licensed the same data and traded the same signals. We weren't building sustainable information advantage. We were renting temporary edges that evaporated as data became commoditized."

Three specific constraints demanded resolution. First, all commercially available alternative data suffered from the same commoditization cycle—early adopter advantage followed by rapid alpha decay as competitor adoption increased. Second, their proprietary research insights required data that simply didn't exist as commercial products. Third, standard data feeds provided generic coverage when what they needed was surgical precision on specific companies, sectors, or market dynamics aligned with their investment strategy.

The fund needed something fundamentally different: not licenses to existing data products, but custom data infrastructure built exclusively for their strategy, monitoring sources unavailable to competitors, delivering signals that couldn't be purchased elsewhere.

The Hermes Intelligence Solution: Custom Data Product Development from Unique Sources

We didn't sell them alternative data. We built them alternative data infrastructure—custom collection systems, proprietary processing pipelines, and bespoke signal engineering specifically designed for their investment strategy. Every data product we created was exclusive to them, impossible for competitors to replicate through commercial vendors.

Bespoke Data Product Architecture: Build What Doesn't Exist

The fund identified information gaps where their investment thesis required data that no commercial provider offered. For each gap, we engineered custom data products from raw sources.

For European industrial companies where supply chain health drove earnings volatility, we built a supplier relationship stability index. The data product aggregated signals from procurement job postings, supplier financial filings, logistics contract renewals, and payment term changes in trade credit insurance markets. No vendor sold this. We created it by identifying relevant source data, building collection infrastructure, developing processing algorithms, and engineering predictive signals specifically for the fund's European industrials book.

For technology companies where R&D productivity determined competitive positioning, we constructed an innovation velocity score combining patent filing quality metrics, technical talent acquisition patterns, research publication citations, open source contribution activity, and developer community engagement. The fund's tech analysts gained quantitative tracking of innovation trajectories months before those trajectories showed up in product launches or financial results.

For consumer discretionary holdings where brand strength drove pricing power, we engineered a consumer sentiment granularity metric that went far beyond standard social listening tools.

Proprietary Data Collection Infrastructure: Sources Competitors Cannot Access

We built collection systems monitoring sources that commercial alternative data providers either don't track or aggregate into broad industry datasets that dilute signal value. Every source was selected specifically because it provided differentiated insight relevant to the fund's strategy.

For their healthcare holdings, we monitor pharmaceutical regulatory pathways across eighteen jurisdictions. Not just approval decisions that everyone tracks, but the subtle language in regulatory feedback letters, committee meeting minutes, and advisory panel deliberations that forecast approval likelihood and post-approval commercial restrictions. When regulatory commentary shifted for a portfolio company's drug candidate six weeks before formal guidance was published, the fund adjusted position sizing ahead of the news flow.

For their industrial holdings, we track infrastructure procurement patterns in key end markets. Municipal tender documents, corporate capex budgets in annual reports, government infrastructure spending authorizations—aggregated and processed to forecast order book strength three to six months before reported backlog data shows the trend. The fund's industrials portfolio manager describes it as "demand visibility that doesn't exist in sell-side models because the underlying data isn't available to Street research."

For their financial services holdings, we built collection infrastructure monitoring commercial real estate transaction data at granular property-type and geographic levels. Not the aggregated market statistics everyone sees, but building-level transaction evidence that reveals portfolio composition shifts and valuation pressure points before they show up in regulatory filings or earnings reports.

Custom Signal Engineering: Transform Raw Data Into Actionable Alpha

Raw data holds limited value. Alpha comes from signal extraction—identifying the patterns in data that actually predict future price movements. We engineer predictive signals customized to the fund's specific investment approach and risk tolerance.

For their long-duration quality growth holdings, we developed an executive decision quality score. The signal combines management commentary sentiment analysis, capital allocation track record metrics, strategic consistency measures, and competitive positioning assessments.

For event-driven positions around M&A and corporate restructuring, we engineered a transaction completion probability model incorporating regulatory filing language analysis, antitrust precedent mapping, political risk assessment in relevant jurisdictions, and financing market condition indicators. The model improved position sizing and risk management on merger arbitrage opportunities, reducing capital allocation to deals that ultimately broke while increasing exposure to transactions with underappreciated completion certainty.

Real-Time Event Monitoring: Corporate Actions Before Announcements

Market-moving corporate events don't materialize overnight. They leave footprints weeks or months before official announcements. Our monitoring infrastructure detects those footprints.

We track hiring pattern anomalies that signal strategic pivots. When a portfolio company's hiring in one geography or function suddenly accelerates while hiring elsewhere slows, our system flags it immediately. Management might announce the strategic reallocation six months later, but the hiring data telegraphed it first. The fund adjusts their thesis and position sizing with that advance visibility.

We monitor management team changes not just at the C-suite level everyone watches, but two and three levels down where operational expertise concentrates. Unusual executive departures in specific divisions, especially when those executives join competitors or startups, signal strategic challenges or market opportunity shifts before they surface in investor communications.

We track patent activity not just for quantity but for strategic direction signals. When patent filings shift from one technology domain to another, when collaborative patents appear with unexpected partners, when defensive patents cluster around specific capabilities—these patterns reveal strategic priorities and competitive threats before products launch or partnerships get announced.

Alternative Data Fusion: Connecting Disparate Signals

Individual alternative data sources provide limited insight. Real intelligence comes from connecting signals across data types to build comprehensive pictures of corporate health and market dynamics.

Vertical Integration: Complete Control Over Data Pipeline

We don't source data from third parties who might sell similar outputs to competitors. We control the entire pipeline from raw data collection through processing, signal engineering, and delivery. This vertical integration guarantees exclusivity and eliminates data quality surprises from vendor dependencies.

When the fund identifies a new information gap, we design collection infrastructure from first principles. What sources contain the relevant signals? How can we access them programmatically? What processing transforms raw data into structured insights? What quality controls ensure reliability? Every component gets built specifically for the fund's needs, and the resulting data products belong exclusively to them.

Seamless Integration: Data Flows Into Existing Workflows

Alternative data delivers value only when analysts actually use it. We integrated every custom data product directly into the fund's research and portfolio management workflows.

Data flows into their proprietary research management system through API connections, appearing alongside traditional financial data and research notes. Analysts don't need to access separate platforms or wrangle data formats. Predictive signals appear as scored metrics in their stock screening tools. Alert systems flag material changes in monitored indicators, pushing notifications to relevant analysts immediately.

Portfolio managers view aggregated data product signals in their risk monitoring dashboards. They see which holdings show improving or deteriorating alternative data indicators, enabling proactive position sizing adjustments. The CIO reviews monthly summary reports showing how alternative data signals contributed to investment decisions across the portfolio.

The Results: Proprietary Data Infrastructure as Sustainable Alpha Source

Annual Alpha Generation

Over months, the custom data infrastructure contributed additional alpha compared to the fund's benchmark and peer group. This alpha came from multiple sources: identifying long opportunities before Street consensus recognized the positive fundamentals, sizing short positions where custom data revealed deteriorating business quality, avoiding value traps where standard metrics looked attractive but alternative data showed underlying problems, and timing position changes based on leading rather than lagging indicators.

Multiple Custom Data Products Delivering Differentiated Intelligence

We built distinct data products across the fund's sector coverage areas. Each product addresses a specific information gap relevant to the fund's strategy. Each product is exclusive to the fund—no competitor can purchase equivalent data from commercial vendors because these products don't exist commercially.

AUM Growth Driven by Performance and Differentiation

The fund's assets under management grew, driven primarily by strong performance attribution and the ability to articulate differentiated information advantages to institutional allocators. In consultant due diligence meetings, the fund demonstrates proprietary data capabilities that peers relying on commercial vendors cannot replicate. The custom data infrastructure became both a performance driver and a fundraising differentiator.

Zero Data Commoditization Risk

Unlike licensed alternative data where alpha decays as more subscribers trade the same signals, the custom data products maintain their predictive power because only the fund has access. Competitors cannot erode the edge by licensing the same datasets. The information advantage is structural and sustainable.

Capital Efficiency: Higher Conviction Sizing on Best Ideas

The Head of Research noted: "Custom data didn't just generate new ideas—it gave us conviction to size our best ideas more aggressively. When our fundamental analysis and our proprietary alternative data both point the same direction, we're willing to hold larger positions because we're operating with information advantages others lack."

Five Data Products That Delivered Significant Alpha

The Supplier Relationship Stability Index: European Industrials

For European industrial companies, supplier relationship disruptions often precede earnings deterioration. Standard financial analysis misses these dynamics until they appear in results. The fund needed early warning signals.

We built a supplier relationship stability index aggregating procurement job posting patterns, supplier financial health metrics from trade credit markets, logistics contract renewal data, and payment term extensions visible in supplier filings. The composite score quantified supplier ecosystem health for each portfolio company.

When the index deteriorated sharply for one of the fund's largest industrials holdings, showing multiple tier-one suppliers experiencing stress simultaneously, the fund investigated. Management had mentioned "supply chain optimization" in routine communications, but the alternative data suggested something more serious.

Two months later, the company announced a major supplier bankruptcy that disrupted production and forced costly emergency sourcing. The stock declined.

The Innovation Velocity Score: Identifying Tomorrow's Winners Today

In technology sectors, companies that maintain innovation leadership capture disproportionate value creation. But measuring innovation proves difficult using traditional metrics. R&D spending means little. Patent counts measure quantity, not quality. The fund needed better signals.

We engineered an innovation velocity score combining patent filing quality metrics weighted by technical significance and citation impact, technical talent acquisition patterns from specialized engineering domains, research publication citations in relevant fields, open source contribution activity in strategic technology areas, and developer community engagement measured by ecosystem adoption metrics.

The score identified two mid-cap software companies demonstrating accelerating innovation velocity despite relatively modest R&D budget increases. Both showed strong technical talent recruitment, meaningful open source community engagement, and high-quality patent activity in emerging technology domains.

Both companies launched significant product innovations that Street analysts had not anticipated. Revenue growth accelerated, margins expanded as new products commanded premium pricing, and both stocks delivered returns exceeding sixty percent. The innovation velocity signal provided advance indication of the product cycle inflections that drove the stock performance.

The Consumer Sentiment Granularity Metric: Brand Health Before Financial Impact

For consumer holdings, brand health drives long-term value creation. But aggregate sentiment scores miss crucial details. Different demographic cohorts might show diverging sentiment. Geographic markets might vary significantly. Competitive context might be shifting even when absolute sentiment appears stable.

We engineered a consumer sentiment granularity metric tracking sentiment across demographic cohorts, geographic markets, competitive positioning context, and purchase occasion categories. The data product revealed sentiment dynamics invisible in aggregate measures.

For a luxury apparel holding, aggregate brand sentiment looked stable. But the granular analysis revealed deteriorating sentiment specifically among younger affluent consumers in key growth markets, precisely the demographic driving category growth and future customer lifetime value. Older cohorts remained loyal, masking the erosion in aggregate scores.

The sentiment deterioration showed up in comparable store sales trends and geographic revenue mix. The stock underperformed as growth decelerated. The early warning from granular sentiment analysis enabled the fund to exit most of the position before the market recognized the problem.

The Infrastructure Demand Leading Indicator: Visibility Street Analysts Lack

For industrial companies serving infrastructure end markets, order timing creates earnings volatility. Selling analysts forecast demand using lagged indicators—government budget announcements, prior period contract awards, reported backlog. The fund wanted leading indicators.

We built collection infrastructure monitoring municipal tender documents, corporate capex budgets in annual reports, government infrastructure spending authorizations, and engineering firm project pipeline data. The aggregated data provided three to six month visibility into order activity before it appeared in reported metrics.

The infrastructure demand indicator showed accelerating activity in renewable energy infrastructure and electric grid modernization projects across European markets. The fund increased positions in companies with exposure to those specific infrastructure categories.

The Full Capability Stack: Why This Works

Custom Data Product Development: Build What You Need, Not What Vendors Sell

Commercial alternative data vendors build products for broad market appeal, covering the most common use cases across many subscribers. We build data products specifically for the fund's strategy, monitoring exactly what they need to know, ignoring generic coverage that dilutes resources.

When the fund identifies an information gap, we design and engineer a custom solution. No compromises to fit vendor product specifications. No accepting coverage gaps because the vendor doesn't serve that niche. Complete customization to strategy requirements.

Source Identification and Access: Data That Doesn't Exist as Products

The most valuable data sources aren't packaged as commercial products. They're raw information streams that require custom collection infrastructure, proprietary processing, and signal engineering to transform into usable intelligence.

We identify which sources contain the signals the fund needs, regardless of whether those sources are already monitored by the alternative data industry. Municipal procurement databases, regulatory feedback documents, specialist job boards, technical publication repositories, supplier financial filings in secondary jurisdictions—if the source contains predictive signals, we build infrastructure to access it.

Proprietary Signal Engineering: Transform Data Into Predictive Alpha

Raw data means nothing. Predictive signals mean everything. We or our clients develop custom algorithms, statistical models, and machine learning systems that extract signals specifically calibrated to predict outcomes the fund cares about—earnings surprises, margin trends, competitive position shifts, management quality deterioration, innovation cycle inflections.

Every signal gets backtested rigorously against historical performance to validate predictive power before deployment. We measure signal decay over time to ensure sustainability. We monitor signal correlation with the fund's other data sources to ensure incremental information value.

Vertical Integration: Control Every Step to Guarantee Exclusivity

We control the entire data pipeline from source access through collection, processing, signal engineering, quality assurance, and delivery. No third-party vendors who might sell similar data to competitors. No dependencies that create risk of data availability disruption. Complete vertical integration ensures the fund's data advantages remain proprietary.

Continuous Refinement Based on Performance Attribution

We don't build data products and walk away. Every quarter, we analyze which data products contributed to profitable investment decisions and which proved less predictive than expected. Products showing strong performance attribution get expanded. Products with weak performance get re-engineered or retired. The data infrastructure continuously improves based on real results.

Seamless Integration: Data Where Analysts Work

Alternative data delivers value only when it influences decisions. We integrated every custom data product directly into the fund's research workflows, portfolio management systems, and risk monitoring infrastructure. Analysts don't access separate platforms or wrangle unfamiliar data formats. The data appears where they already work, making adoption natural and usage continuous.

Pay-As-You-Go Model Aligned With Value Creation

The fund pays monthly for active data products based on demonstrated value contribution, not locked into annual contracts based on projected usage. When a data product stops generating alpha, they can cancel it. When a new strategy requires new data, we build it without renegotiating contracts. Complete flexibility aligned with actual value delivery.

What Made This Partnership Succeed

Pilot That Demonstrated Concept Before Full Commitment

We started with three custom data products focused on the fund's European industrials portfolio—the sector where they felt existing alternative data was weakest. The twelve-week pilot delivered signals that influenced two significant position changes, both of which proved profitable. The demonstrated value justified expanding to comprehensive coverage across all sectors.

Strategy-First Data Design: Understanding Their Edge Before Building Products

We didn't begin by proposing data solutions. We began by understanding the fund's investment strategy, analytical edge, and information gaps. Where did their fundamental research prove most valuable? Where did uncertainty constrain position sizing? What questions could they not answer with existing data? The data products we built addressed those specific gaps.

Collaborative Development Process With Iterative Refinement

Each custom data product went through collaborative development. We proposed initial designs based on strategy understanding. The fund's analysts provided feedback on what signals would prove most valuable. We built prototypes, tested them against historical periods, refined based on analyst input, and delivered production versions only after validation confirmed value.

Performance Attribution Discipline Ensuring Resource Allocation to Value

Every quarter, we jointly review which data products contributed to investment decisions and how those decisions performed. Products showing strong attribution get enhanced investment. Products showing weak attribution get re-evaluated. This discipline ensures resources concentrate on data that actually generates alpha rather than data that's intellectually interesting but strategically useless.

Complete Transparency on Methodology and Limitations

Every data product comes with detailed methodology documentation. The fund understands exactly how data gets collected, how signals get engineered, what backtesting revealed about predictive power, and what limitations exist. This transparency enables appropriate usage—analysts know which signals work best in which contexts, reducing misapplication and improving signal-to-noise ratios.

Why This Couldn't Be Replicated With Commercial Alternative Data

Commercial alternative data providers sell standardized products to many subscribers. Their business model requires broad appeal and scalable delivery. They cannot justify engineering highly customized data products for individual investors because the economics don't work when development costs can't be amortized across many customers.

What we built for this fund operates under completely different economics and delivers fundamentally different value. Custom data products engineered specifically for their strategy and information gaps. Source selection and collection infrastructure designed around their sector focus and analytical approach. Signal engineering calibrated to predict outcomes they care about rather than generic market signals. Complete exclusivity ensuring no competitor can purchase similar data and erode the alpha.

The Head of Research described it precisely: "Commercial alternative data gives you the same information dozens or hundreds of other investors have. You might get it a bit faster or process it slightly better, but it's fundamentally a commoditized advantage. Custom data infrastructure gives you information others simply cannot access. That's sustainable edge."

Strategic Impact Beyond Alpha Generation

Fundraising Differentiation in Competitive Asset Management Market

The custom data infrastructure became a core component of the fund's institutional marketing. In due diligence meetings with consultants and allocators, they demonstrate proprietary data capabilities that peer funds using commercial vendors cannot match. The ability to show exclusive data products monitoring sources competitors don't access creates clear differentiation in a crowded market.

Analyst Recruitment and Retention Advantage

Top investment analysts want to work where they have the best tools and information. The fund now recruits senior analysts from larger competitors by demonstrating data advantages. "Join us and you'll have access to proprietary alternative data specifically built for our strategy" proves compelling when backed by examples of custom data products and their performance attribution.

Portfolio Construction Confidence Enabling Higher Active Share

Custom data doesn't just help to pick better stocks—it gives confidence to construct a higher-conviction portfolio. When you have information advantages on our largest positions, you're willing to size them more aggressively. Proprietary data reduces uncertainty on our best ideas.

Risk Management Enhancement Through Leading Indicators

Many custom data products provide leading indicators for risk events—supplier disruptions, accounting quality deterioration, competitive threats, demand softening. These leading indicators improve risk management by flagging problems before they appear in financial results, enabling proactive position adjustment rather than reactive damage control.

Key Lessons From Custom Data Development

Specificity Creates Value: Generic Coverage Dilutes Resources

The most valuable data products monitor very specific phenomena highly relevant to the strategy. Broad market coverage trying to serve many use cases dilutes collection resources and analytical focus. Narrow, deep coverage of strategically important signals delivers superior alpha per dollar invested in data infrastructure.

Source Exclusivity Matters More Than Data Volume

Having exclusive access to a few high-value sources generates more sustainable alpha than having shared access to dozens of commoditized sources. The fund learned to prioritize source exclusivity and signal differentiation over data volume and breadth of coverage.

Integration Drives Usage, Usage Drives Value

The most sophisticated data products deliver zero value if analysts don't use them. Seamless integration into existing workflows proves essential. Data that requires changing analyst behavior or learning new platforms gets ignored. Data that appears naturally in existing workflows gets used continuously.

Performance Attribution Discipline Prevents Waste

Without rigorous performance attribution, funds waste resources on data that doesn't generate alpha. The discipline of quarterly review asking "did this data product contribute to profitable decisions?" ensures resources concentrate on what works and eliminates what doesn't.

Scaling the Custom Data Infrastructure

After validating the model, the fund is expanding custom data coverage to support new strategy initiatives. They're building data products for emerging markets exposure where local information opacity creates larger information advantages. They're developing credit-focused data products to support potential expansion into long-short credit strategies. They're engineering ESG data products monitoring actual corporate behavior rather than relying on commercial ESG rating agencies whose scores suffer from methodology disagreements and lagging information.

The fund is also exploring using custom data infrastructure to support activism and engagement initiatives, developing data products that track corporate governance quality, capital allocation discipline, and management strategic consistency to identify potential activism targets or support engagement conversations with portfolio companies.

Most strategically, they're not worried about competitors replicating their edge. The custom data infrastructure is exclusive to them, purpose-built for their strategy, and continuously refined based on their performance feedback. Competitors using commercial alternative data vendors remain trapped in the commoditization cycle where alpha decays as data proliferates across subscribers.

The Bottom Line: Custom Data Infrastructure as Durable Competitive Moat

Investment performance increasingly depends on information advantages. In markets where every sophisticated investor uses the same terminals, accesses the same research, and licenses the same alternative data, sustainable alpha requires differentiated information sources.

This fund transformed from being a price-taker in the alternative data market—licensing whatever commercial vendors offered—to building proprietary data infrastructure specifically engineered for their strategy. The results speak clearly: hundreds basis points of annual alpha, AUM growth, zero data commoditization risk, and fundraising differentiation that commercial data users cannot match.

They're not renting temporary information advantages that evaporate as data commoditizes. They're building permanent information infrastructure that competitors cannot replicate because the data products don't exist commercially and the sources aren't available for licensing.

When allocators ask what makes this fund different, the answer is concrete: proprietary alternative data infrastructure built exclusively for their strategy, monitoring sources others don't access, delivering signals others cannot purchase.

That's sustainable competitive advantage. That's the Hermes Intelligence difference.

Build Your Proprietary Data Advantage

Every sophisticated investor has access to Traditional Market Data Providers, and the same commercial alternative data vendors. That information parity eliminates sustainable edge. If you're serious about generating differentiated alpha, you need data infrastructure competitors cannot replicate.

Start With Strategy Assessment and Data Gap Analysis

We'll analyze your investment strategy, identify where existing data serves you well and where information gaps constrain conviction or limit position sizing. Then we'll propose custom data products specifically designed to fill those gaps with sources and signals unavailable commercially.

Pilot Custom Data Products on Your Highest-Conviction Sectors

We'll build three to five custom data products focused on sectors where you believe your fundamental research creates alpha but existing alternative data proves inadequate. Twelve weeks to validate that custom data actually improves decision-making and performance attribution.

See Examples of Custom Data Products We've Built

Request a demonstration of custom data products we've engineered for other investment strategies. We'll show you examples of source identification, signal engineering, and integration approaches—illustrating what's possible when data infrastructure gets built around strategy rather than strategy adapting to available data products.

Alpha doesn't come from having the same data as everyone else. It comes from having data others cannot access. The question isn't whether you need proprietary data infrastructure—it's whether you'll build it before your competitors do.

Request Your Custom Data Strategy Assessment

Contact Hermes Intelligence to discuss building proprietary alternative data infrastructure aligned with your investment strategy.

Email: info@hermesintelligence.com

Phone: +44 203 576 1173

Hermes Intelligence: Alpha doesn't announce itself—but proprietary data reveals it before markets move.