Ask a business owner how they want their systems connected and the answer is almost always the same: real time. It sounds like the obvious upgrade. Nobody asks for "slightly delayed."
But real time is not a quality setting. It's an architecture decision with a price tag, and the price shows up later — in API costs, in brittle handoffs, in the pager going off because a vendor's endpoint had a bad afternoon. The better question isn't how fast can we sync this? It's how fast does this need to be before someone makes a worse decision?
Latency Tolerance Is a Business Question, Not a Technical One
Every data flow between two systems exists to support a decision or an action. A customer deciding whether to buy. A warehouse picker deciding what to pull. A controller deciding whether the books are ready to close.
So for each flow, ask: who acts on this data, and what happens if they act on a version that's fifteen minutes old? Sometimes the answer is "we oversell an item we don't have and lose a customer." Sometimes it's "absolutely nothing, because nobody looks at this report until Tuesday."
Those two flows should not have the same architecture, and yet in most mid-market stacks they do — because someone turned on every available sync at maximum frequency and moved on.
A Rough Tiering That Holds Up
Most data movement in a growing business falls into one of a few buckets:
- Real time (seconds): anything a customer sees or feels — inventory availability on the website, payment confirmation, order status, account access after signup.
- Near real time (minutes): internal operational work — new orders flowing to fulfillment, leads reaching a rep's queue, support tickets tied to account records.
- Scheduled batch (hourly or nightly): financial postings, reporting warehouses, price list updates, commission calculations, data enrichment.
- On demand: bulk imports, annual catalog refreshes, one-off migrations.
If a flow can't be argued into the first tier with a concrete consequence, it probably belongs in the second or third.
What Real Time Actually Costs
The sticker price is the easy part. The real cost is operational.
Event-driven, record-by-record syncing means every single transaction is an opportunity for a partial failure. One order syncs, the next one doesn't, and you don't find out until a customer calls. Batch jobs fail loudly and completely — which sounds worse but is usually easier to catch and re-run.
Real time also couples your systems' uptime together. If your ERP needs a live response from your CRM to complete an action, your CRM's maintenance window is now your ERP's maintenance window. Batch processes are far more forgiving; a queue builds up and drains when the other side comes back.
Then there are the boring constraints that sink projects quietly: API rate limits, per-call pricing on some platforms, and ordering problems where an update arrives before the record it's updating. These are all solvable. They're just work, and that work should be spent where it buys something.
Near Real Time Is the Underrated Answer
The middle ground deserves more attention than it gets. A queue that processes events every few minutes gives you most of the responsiveness of real time with most of the resilience of batch. Records pile up safely during an outage. Retries are contained. Volume spikes get absorbed instead of amplified.
For internal operations — order-to-fulfillment, lead routing, inventory adjustments between locations — near real time is usually indistinguishable from instant to the people doing the work, and dramatically cheaper to run and debug.
Write Down the Register
Before you build or buy another connector, document every flow in a single table. For each one: source system, destination, what data moves, direction, cadence, who owns it, and what should happen when it fails.
This document does more work than it looks like it should. It surfaces duplicate flows nobody knew about. It exposes bidirectional syncs that are quietly fighting each other. It gives you a defensible answer when a vendor insists their real-time option is mandatory. And when something breaks at 4pm on a Friday, it tells whoever is on the keyboard where to look.
Most importantly, it turns integration from a pile of point-to-point connections into a system you can reason about — which is the difference between a stack that scales with you and one you outgrow in two years.
Where to Start
Pick your three highest-volume flows and interrogate them. What decision depends on each one? How stale can the data be before that decision gets worse? You'll likely find at least one flow running at a cadence nobody chose deliberately.
If your systems are connected but you couldn't draw the map on a whiteboard, that's worth a conversation. We build integration architectures around how businesses actually operate — not around whatever the default setting was. Reach out and we'll walk your stack with you.