What Is Market Data and How Businesses Use It

What Is Market Data and How Businesses Use It

We’ll cut to the chase: what is market data and how do you use it to propel your business? Put simply, it’s the stream of prices, volumes, quotes, and reference rates that describe the state of a financial market at any moment. And you use it to price products, trigger automations, measure risk, and feed models. Simple, right? Well, yes and no. Because not all market data is created equal, and how you access, license, and integrate it can decide whether your automation runs smoothly or grinds under latency and cost.

Types Of Market Data That Actually Matter

Real-time vs delayed: Real-time feeds deliver prices with minimal lag; delayed feeds (often 15–20 minutes) suit dashboards and archival use where immediacy isn’t required. For transactional or automated decision systems, real-time is a must.

Then there are asset classes: FX markets, equity indices, commodities, and fixed income. Each one brings different cadence and connectivity needs. FX is enormous: global FX turnover runs in the trillions per day (BIS reported $7.5 trillion/day in April 2022 and more recent surveys show even larger totals). That scale explains why infrastructure and licensing are serious considerations for firms touching FX.

Why Latency, Licensing, And Caching Are Important

Latency shapes what you can automate. Milliseconds can change whether a price used by your algorithm reflects the current market or yesterday’s trade (and that translates directly to P&L and execution quality). Academic and industry studies show that low-latency activity alters spreads and improves market quality, and it also forces you to design for speed and ordering guarantees.

Licensing dictates cost and distribution: some exchanges charge for real-time outbound display, redistribution, or historical data. If your product displays prices externally, plan for exchange and vendor fees early. Caching reduces vendor call volume and smooths spikes, but you must manage staleness and observe licensing restrictions (many vendors forbid storing/distributing certain datasets).

How to Embed Feeds Into Business Workflows

Here are practical examples you can implement today:

• Dynamic pricing: pull a real-time index or FX rate and recalculate customer prices on quote generation (use short TTL caches to lower API costs but keep spreads tight).
• Budget variance alerts: stream settlement prices to a budgeting engine and trigger alerts when realized rates deviate from forecasts by a threshold (use event-driven rules in your integration platform).
• Risk signals: ingest ticks into a risk microservice that computes intraday Greeks or exposure; persist summaries and raise alerts when limits breach.

If you use an integration platform, you can orchestrate feed ingestion, normalization, and downstream actions without writing a bespoke pipeline, which is pretty useful when multiple teams need the same canonical feed.

As for market-data vendors, they span exchanges, aggregators, and brokers. Larger brokers can also provide tradable access and real-time streams. For instance, Axi shows tradable FX and CFD instruments alongside execution endpoints (useful when you want both market access and market data in the same workflow). You want to plan provider selection around instrument coverage, latency SLAs, and contractual terms.

Architecture: Webhooks vs Polling (And Hybrid)

• Polling: simple to implement; fine for delayed data or low-frequency checks. But it wastes requests and increases latency between updates.
• Webhooks / push: efficient and near-real-time; better for tick-sensitive automations. Requires reliable receiver endpoints and replay/backfill logic.
• Hybrid: use push for immediate updates and fall back to periodic polling or snapshotting to repair missed events and maintain a consistent state.

Design tips: add sequence numbers to messages, persist last-seen offsets, implement idempotency, and apply backfill logic so missed updates won’t corrupt your state.

So, Where Do You Start?

Once you’ve mapped out how market data fits into your workflows, the next question is what to tackle first. Good news: you don’t need to overhaul your systems overnight. Just focus on a few fundamentals that keep data accurate, responsive, and compliant.

Here’s a quick list to anchor your plan:

• Decide whether you truly need real-time or if delayed data suffices.
• Evaluate provider SLAs, licensing for redistribution, and pricing models.
• Choose push where latency matters; add polling snapshots to ensure completeness.
• Cache smartly: short TTLs for pricing, longer for non-critical references, and document expiry policies.

Finally, treat market data as a living system. As your automation stack evolves, so will your need for speed, precision, and cost control. That's all fine and expected. The goal is to adapt fast to those changes; that's how you become a business that sets the pace for everyone else.