Data & Integrations · 6 min read
A data integration project connects the tools your business already runs, so information that's entered once shows up correctly everywhere it's needed, without anyone re-typing it. In practice, that means three things: mapping what you actually have, deciding which system is the source of truth when two disagree, and building a connection between them that keeps working quietly in the background.
Before anything gets built, we map which tools hold which data, and where the manual hand-offs currently happen — the spreadsheet someone updates by hand, the export-and-re-import routine that happens every Monday morning. This is usually the step that reveals how much manual work has quietly built up around your systems, and it's rarely as small as it first looks once it's actually written down.
When two systems disagree — a customer's address, an order status, a stock count — something has to be treated as the source of truth. This is less a technical decision than a business one, and it's usually where most of the real thinking in a data integration project happens, not in the code that connects the systems afterward.
Once it's clear what should sync and which system leads, the tools get connected — through their existing APIs where possible, so data moves automatically instead of being re-typed by hand. This is the part that looks like "the project" from the outside, but it's usually the shortest phase if the first two steps were done properly.
For example: a customer updates their details in one system, but the invoicing tool and the support tool still show the old version until someone notices and fixes it manually. Connecting those systems means the update happens once and shows up everywhere it's supposed to — not because someone remembered to copy it across, but because the systems are actually talking to each other.
A pipeline that fails silently is worse than no pipeline at all, because it looks like it's working while quietly falling behind in the background. Part of the build is making sure that if a sync breaks, someone finds out immediately — not three weeks later, in a report that doesn't add up and nobody can explain why.
Not a new tool to learn — the tools you already use, kept in sync without anyone doing it by hand. Clean, reliable pipelines you can trust are the whole point: not a dashboard that looks impressive in a demo, but data that's correct in every system that touches it, every single time, without someone checking behind it.
Data integration overlaps with workflow automation — both are about removing manual, repetitive work — but they start from different questions. Automation usually starts with a repetitive task someone dreads doing; data integration starts with two or more systems that should agree and don't. Often a single project involves both. Either way, it runs through the same six-stage process as every other BBS project — understand the business first, then define what's actually worth building, before any connection gets built.
They're related but not identical. Automation is the broader category — replacing manual, repetitive processes with something that runs itself. Data integration is specifically about getting separate systems to agree and stay in sync. A project can be one, the other, or both, depending on what's actually going wrong. If you're not sure which one you need, start with the problem, not the label — we'll help figure out which one actually fits.
Yes — connecting tools requires access to the systems being connected. What that access looks like depends on the tools involved, and it's scoped clearly before anything starts, not assumed or requested all at once upfront.
Not every tool is built to connect easily. Where a direct connection isn't possible, the right answer is sometimes a narrower fix than a full integration — which is exactly the kind of thing worth scoping honestly before committing to a bigger project than the problem actually needs.
Workflow automation usually targets one specific repetitive task in isolation. A data integration is broader — it's about the systems themselves staying in sync, which often removes several manual tasks at once instead of just one.
If you're re-typing the same information into more than one tool, or two systems that should agree keep drifting apart, tell us what you're working with and we'll tell you plainly what a fix would actually involve.
Related service: Data & Integrations. Connecting the tools you already use so data moves on its own instead of by hand.
11 — Start here
Tell us what's slowing your business down, what you want to build, or what isn't working. We'll figure out the next step together.
