How to Connect Disconnected Business Systems

Why This Is the Hardest 80%

System integration is the unglamorous foundation of every successful data project. It involves negotiating APIs that are often poorly documented, building extraction pipelines that need to handle rate limits and downtime, transforming data formats that were never meant to be compatible, and doing all of this reliably — not once, but every day, automatically. IBM estimates poor data quality costs the US economy $3.1 trillion per year¹ — and disconnected systems are a primary driver. This is not the kind of work that makes it into vendor presentations. But it is the work that determines whether everything built on top actually functions.

The Practical Steps

Step one: audit your systems. Map every platform that holds business-critical data. Understand what data lives where, how it is structured, and what APIs or export mechanisms are available. Step two: prioritise. You do not need to connect everything at once. Start with the systems that drive your core commercial decisions — typically your CRM, accounting platform, and primary operational system.

Step three: build extraction pipelines. For each system, build a reliable, automated pipeline that pulls data on a schedule. This needs to handle failures gracefully, log every run, and alert when something breaks. Step four: normalise. Raw data from different systems uses different formats, different naming conventions, different definitions. Normalisation is the process of creating a single, consistent language — assigning unique identifiers, standardising financial details, classifying transaction types, and resolving duplicates.

Common Pitfalls

The most common mistake is trying to do this inside a BI tool. BI tools are designed to visualise data, not to integrate and normalise it. Trying to use them as an integration layer creates a fragile, unmaintainable architecture that breaks every time a source system changes. Gartner predicts that 80% of organisations seeking to scale digital business will fail because they lack a modern approach to data governance². The second mistake is underestimating the normalisation work. Getting data out of silos is step one. Making it consistent and reliable is where the real value is created — and where most DIY attempts fail.

The Result

When your systems are connected and your data is normalised, you have a single, reliable data layer that can power anything. Dashboards that refresh automatically. Reports that assemble in seconds. Forecasting models that train on clean historical data. AI capabilities that actually work because they have structured data to work with. Every capability you want to build in the future becomes faster, cheaper, and more reliable because the foundation is in place.