Invest in These 5 Big Data Behemoths to Tap Wall Street Rally: What It Means for 2026 Investors

Big Data has gone from a tech buzzword to a real “engine room” for modern business. Every time you stream a video, tap a payment app, order food, or train an AI model, you create more data. And that data needs to be collected, cleaned, stored, protected, analyzed, and turned into decisions people can trust.

That’s why a recent market-focused roundup highlighted five big data leaders—companies that either build the tools to process data or monetize insights from it: Fair Isaac (FICO), Teradata (TDC), F5 (FFIV), S&P Global (SPGI), and Moody’s (MCO).

In this rewritten, expanded news-style feature (in English), you’ll get a detailed, SEO-friendly breakdown of what “big data” really means, why Wall Street cares right now, and how each of these five firms fits into the bigger story of AI, cloud, security, and financial intelligence.

Why Big Data Matters More Than Ever

Let’s keep it simple: big data is a massive mix of information—structured (like spreadsheets), unstructured (like text, images, video), and semi-structured (like logs from apps). Businesses don’t just want to store it. They want to use it.

When a retailer tries to predict which products will sell next month, or a bank tries to spot fraud in seconds, or a hospital tries to reduce patient risk, they’re leaning on analytics. Modern big data platforms power:

Predictive modeling (forecasting likely outcomes)
Machine learning (systems that learn from patterns)
Real-time decisioning (responding instantly, not next week)
Risk management (avoiding costly mistakes)

Industry research and forecasts have consistently pointed to explosive data growth. One widely cited IDC-backed view projects global data creation climbing dramatically over time, with some forecasts putting 2028 data generation near 393.9 zettabytes (that’s an almost hard-to-imagine scale).

Why is this happening? Because of three big forces:

1) AI Is Hungry for Data

AI models don’t run on wishes and magic—they run on data. Training, tuning, and operating AI systems requires clean pipelines, safe access, and strong governance. And as businesses push into “always-on” AI, query loads can rise fast, stressing platforms that weren’t built for nonstop demand.

2) The Internet of Things Keeps Multiplying

Factories, cars, ships, stores, and even office buildings now produce continuous streams of sensor data. That creates new value—but only if you can process it efficiently and securely.

3) Decision-Making Is Becoming Automated

In many industries, humans can’t manually review everything. So companies rely on scoring systems, automated workflows, and analytics dashboards to act quickly. Gartner’s definitions around predictive analytics and predictive modeling emphasize using data mining and statistical techniques to produce actionable forecasts—exactly the kind of activity big data platforms are designed to support.

What “Tap Wall Street Rally” Really Implies

When markets rally, investors often look for themes that can keep compounding over years—not just weeks. Big data is one of those themes because it sits underneath:

Cloud migration (more workloads shifting online)
Cybersecurity (more apps and data to protect)
AI adoption (more models, more monitoring, more governance)
Financial intelligence (risk, ratings, and trusted information)

That’s the basic logic behind spotlighting five “behemoths”: each benefits from the need to manage and monetize information at scale.

The 5 Big Data Behemoths: The Core List

Here are the five companies featured in the roundup, rewritten and expanded with clearer context:

Fair Isaac Corporation (FICO) — Data-driven scoring and decision platforms for lenders
Teradata Corporation (TDC) — Enterprise data platforms, analytics, and AI-ready workloads
F5, Inc. (FFIV) — Application delivery, security, and multi-cloud protection
S&P Global Inc. (SPGI) — Business information, analytics, and data-driven intelligence products
Moody’s Corporation (MCO) — Credit ratings, risk analytics, and expanding data capabilities

Source note: the original market commentary that inspired this rewrite grouped these five as big data-related plays and discussed their positioning, growth expectations, and strategic moves.

1) Fair Isaac (FICO): Turning Data Into Decisions That Move Money

FICO is best known for one of the most famous “data products” ever: the credit score. At its core, FICO is a big data business because it turns massive amounts of consumer and lender information into a numeric signal that helps companies decide whether to lend, at what rate, and with what risk controls.

What’s Driving FICO Right Now

In the recent commentary, FICO was described as benefiting from strong performance across its major segments—particularly Scores and Software.

One detail that matters in plain language: credit behavior is changing. New consumer habits—like “buy now, pay later” (BNPL)—create data signals lenders want to understand. FICO has worked to incorporate BNPL-related loan data into scoring approaches to improve predictive accuracy.

Why That’s a Big Deal

Credit scoring isn’t just a number. It can affect whether people qualify for mortgages, car loans, and other financial products. So when scoring models become more predictive, lenders can make decisions that are both smarter and potentially more inclusive—reducing defaults while improving access for responsible borrowers.

The commentary also referenced the development of newer scoring approaches for specific mortgage contexts (including mentions tied to “10T” and non-GSE mortgages). The important takeaway: FICO keeps evolving its models to stay relevant as lending changes.

Software + SaaS = Stickier Revenue

Beyond scoring, FICO sells decision software—tools that help banks and businesses automate risk decisions (like fraud detection, underwriting, and customer management). The write-up pointed to stronger adoption of SaaS and license revenues, suggesting durable platform engagement.

In simple terms: SaaS models can create recurring revenue and tighter customer relationships, because the software becomes part of daily operations.

What Investors Typically Watch With FICO

Regulatory and industry adoption of newer scoring models
Competitive pressure from alternative scoring approaches
Pricing power (scores are valuable, but pricing can be debated)
Execution in software (growth beyond the score franchise)

Bottom line: FICO fits the big data theme because it monetizes predictive analytics directly in high-stakes financial decisions.

2) Teradata (TDC): Data Platforms Built for the “Always-On” AI Era

Teradata is a long-time enterprise analytics player that has been pushing deeper into modern data platform needs. In the market commentary, it was positioned as a company that can benefit from improving recurring revenue trends, productivity efforts, and expanding workloads driven by new AI usage patterns.

Agentic AI and the 24/7 Query Problem

One of the most interesting ideas mentioned is that “agentic” AI—systems designed to operate continuously—could create nonstop demand for data queries. That means enterprise databases and analytics platforms may face heavier loads than before.

If AI agents are asking questions all day and all night (“What’s the best supply route?” “What’s the fraud risk?” “What should we reorder?”), the data platform becomes mission-critical infrastructure.

Portfolio and Platform Direction

The write-up listed several Teradata offerings tied to modern analytics fabrics, vector capabilities, and model operations (ModelOps). The key idea is that Teradata wants to support enterprises whether they run on-premises or in the cloud, while also supporting AI-friendly retrieval and governance workflows.

Even if the product names sound technical, they point to a clear strategy: help customers manage, connect, and operationalize data for analytics and AI—without losing enterprise-grade controls.

Acquisitions and Feature Expansion

Acquisitions were also noted as a way to expand capabilities in data search, exploration, and natural language interactions (“ask” experiences). These features matter because many business users don’t want to write complex queries—they want to ask questions in plain English and get reliable answers with governance baked in.

What Investors Typically Watch With Teradata

Recurring revenue momentum and customer retention
Cloud execution against fierce competitors
AI-related workload growth and platform differentiation
Free cash flow trends as efficiency programs roll through

Bottom line: Teradata is a “pipes and plumbing” big data play—if AI increases query intensity, strong platforms can matter more than ever.

3) F5 (FFIV): Big Data Needs Security, Speed, and Multi-Cloud Control

It’s easy to think big data is only about storage and analytics. But there’s another giant issue: applications. Data moves through apps, APIs, and services—and those pathways need to be fast and safe.

F5 lives in that world. In the market commentary, F5 was described as gaining traction from software growth and seeing tailwinds in public cloud and security offerings—especially in multi-cloud environments.

Why Multi-Cloud Security Is a Big Deal

Many companies don’t use just one cloud provider. They might run some workloads on one cloud, others elsewhere, and still keep sensitive systems on-premises. That creates complexity:

More entry points for attackers
More traffic flows to manage
More policy rules to enforce consistently

F5’s positioning is tied to managing and securing applications across these environments, where “application networking” and “Layer 4-7” capabilities become crucial for performance and protection.

Subscriptions and Platform Stickiness

The commentary pointed to acceleration in subscription software deals across several product lines. The big picture: subscription growth can signal customers are standardizing on a platform rather than buying one-off tools.

Acquisitions as Capability Builders

F5 has also used acquisitions to strengthen security capabilities over time, aiming to capture growth in network and app security demand.

Bottom line: in a world where data is everywhere, security and traffic control become part of the big data story. If data is the “oil,” then secure application delivery is the “pipeline.”

4) S&P Global (SPGI): Big Data as a Business—Selling Intelligence at Scale

S&P Global is not a traditional “database company” in the way some tech firms are. But it is absolutely a big data powerhouse because it collects and packages information that investors, businesses, and institutions use to make decisions.

The market commentary framed SPGI as well-positioned to benefit from demand for business information services and highlighted acquisitions and product launches as drivers of growth and reach.

Why Text Analytics Matters Now

A key move mentioned was SPGI’s acquisition of ProntoNLP in January 2025, intended to strengthen textual data analytics and support broader AI applications.

This matters because the world’s knowledge isn’t only in neat spreadsheets. It’s buried in:

earnings call transcripts
news reports
regulatory filings
research notes
contracts and documents

Tools that can interpret language at scale—while staying accurate—are valuable.

Supply Chain and Maritime Insights

The commentary also referenced acquisitions tied to supply chain and maritime tracking insights (including ORBCOMM) and data modeling/linking capabilities (TeraHelix).

S&P Global confirmed completing the TeraHelix acquisition in June 2025, describing it as a way to strengthen advanced data modeling and linking capabilities that can help customers advance AI and generative AI roadmaps.

When data is spread across systems, linking and modeling becomes critical. If companies can’t connect “customer,” “shipment,” “risk,” and “supplier” datasets reliably, AI projects can fail or produce shaky answers.

What Investors Typically Watch With SPGI

Subscription and recurring revenue strength
Demand for market intelligence during active markets
Integration success from acquisitions
Product innovation pace as AI reshapes research workflows

Bottom line: SPGI is “big data as a product.” It sells structured and unstructured insights that power real-world decisions.

5) Moody’s (MCO): Credit Ratings, Risk Data, and Expansion Through Acquisitions

Moody’s is often associated with credit ratings, but the bigger picture is risk intelligence—a data-heavy business where trust, methodology, and scale matter.

In the market commentary, Moody’s was described as holding a dominant position in credit ratings and using acquisitions and restructuring efforts to diversify revenue and expand its footprint.

Growth via Local Market Expansion

Two specific expansion moves were highlighted:

ICR Chile: Moody’s announced it fully acquired ICR Chile in June 2025, strengthening its presence in Latin America’s domestic credit markets.
Middle East Rating & Investors Service (MERIS): the commentary mentioned plans announced in August 2025 to secure majority equity ownership.

These kinds of moves can help Moody’s build local distribution, grow datasets, and cross-sell analytics and research services.

Bond Issuance Cycles Matter

Moody’s revenue can be influenced by debt market activity. The commentary suggested that a rebound in bond issuance volume could support growth, alongside a strong balance sheet and sustainable capital returns.

Bottom line: Moody’s is a big data play because credit ratings and risk products depend on deep datasets, strong models, and trusted processes—and the company continues expanding those capabilities.

Big Data Investing: The “What Could Go Wrong?” Section

No investment theme is risk-free. Big data is powerful, but investors should be realistic. Here are common risk buckets to keep in mind:

1) Competitive Pressure

In data platforms and cloud services, competition can be intense. Customers can be tempted by cheaper alternatives, bundled offerings, or newer tools.

2) Execution Risk

Strategy is one thing. Delivering is another. Product rollouts, platform migrations, and sales cycles can take time.

3) Regulation and Trust

For companies like FICO and Moody’s, trust is central. Regulatory changes, model scrutiny, or reputational issues can be meaningful.

4) Security Threats

Data and applications are prime targets for cyberattacks. Security companies can benefit from demand, but they must also stay ahead of threats.

How These Five Fit Together (A Simple Mental Model)

If you want a clean way to remember this group, think of the big data pipeline:

Decisions & scoring: FICO
Data platform & analytics engine: Teradata
App delivery & security layer: F5
Business intelligence data products: S&P Global
Risk & credit intelligence: Moody’s

They’re not identical businesses—and that’s the point. They represent different “lanes” inside the same megatrend: making data useful and safe at scale.

Practical Takeaways for Readers

Here are a few investor-style lessons that apply even if you’re not picking individual stocks today:

Follow the Workloads, Not the Buzzwords

AI hype is loud, but the real signal is workload growth: more queries, more data pipelines, more security demand, more governance needs.

Look for “Sticky” Customer Relationships

Recurring revenue models, deep integrations, and mission-critical usage can make customers less likely to switch.

Watch Acquisitions Carefully

Acquisitions can be smart growth moves (like the ProntoNLP and TeraHelix deals for SPGI), but integration matters.

Respect the Cycles

Even great businesses can be cyclical. For example, credit and issuance cycles can influence financial analytics and ratings demand.

FAQ: Big Data Behemoths and the 2026 Wall Street Rally

1) What is “big data” in plain English?

It’s huge amounts of information coming from many places (apps, sensors, transactions, documents). Big data tools help store it, process it, and turn it into useful decisions.

2) Why does AI make big data more valuable?

AI systems require lots of data to train, run, and improve. As AI spreads, data platforms, analytics, and governance tools become more important.

3) Are FICO and Moody’s really “big data” companies?

Yes. They rely on large datasets, models, and analytics to produce scores, ratings, and risk insights—data products that directly affect real financial decisions.

4) What does Teradata have to do with agentic AI?

The theme is that always-on AI agents could increase nonstop data queries. Platforms that can manage enterprise data efficiently and securely may benefit from higher demand.

5) Why is F5 included in a big data list?

Because big data depends on applications and cloud services moving information around. F5 focuses on application networking and security across multi-cloud environments, which supports safe, fast data usage.

6) What’s the main risk with big data-themed investing?

Competition and execution. Data and cloud markets move fast, customers can switch tools, and companies must keep innovating to stay ahead.

Conclusion: A Clear 2026 Big Data Map for Investors

Big data isn’t a passing trend—it’s the backbone of modern business. And as AI pushes companies toward faster, more automated decision-making, the need for reliable platforms, security, and trusted intelligence only grows.

That’s why these five names—FICO, Teradata, F5, S&P Global, and Moody’s—keep showing up in conversations about how to “tap” market momentum. They don’t do the same job, but together they cover the lifecycle of data: from collection and processing, to protection, to insight, to financial decisions.

Source note: This article is a rewritten, expanded English news feature based on a market commentary published via Nasdaq/Zacks listing the five companies and summarizing their positioning.

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