7 Things to Consider When Integrating Systems

There's a particular kind of chaos that emerges when business systems don't talk to each other. I've walked into organizations where employees spend half their day copying data between applications, where critical decisions are made on week-old information, and where a single customer might exist as three different records across three different platforms — each with slightly different details.

System integration promises to solve these problems, and when done well, it absolutely does. I've seen integrations reduce manual data entry by 90%, eliminate entire categories of errors, and give business leaders real-time visibility they never thought possible. But I've also seen integration projects fail spectacularly — over budget, behind schedule, and ultimately abandoned.

The difference between success and failure rarely comes down to technology. It comes down to how you approach the project. After two decades of connecting systems — from legacy mainframes to modern cloud platforms, from simple two-system connections to complex enterprise-wide orchestrations — I've identified seven considerations that consistently determine outcomes. Whether you're planning your first integration or your fiftieth, these principles will help you build connections that last.

The most common mistake I see in integration projects is starting with the technology. Someone decides they need to "integrate Salesforce with the ERP system" and immediately jumps into API documentation and field mapping. But here's the question that should come first: What business problem are we actually solving?

An integration isn't valuable because it moves data. It's valuable because of what that data movement enables. Maybe it's eliminating the three hours a day your sales team spends on data entry. Maybe it's ensuring that inventory levels are always accurate so you stop overselling. Maybe it's giving your CFO a real-time view of revenue instead of waiting for month-end reports.

When you start with business outcomes, everything else becomes clearer. You can prioritize which data actually needs to sync and which doesn't. You can determine acceptable latency — does this need to be real-time, or is hourly sufficient? You can define what success looks like and measure whether you've achieved it.

I worked with a retail company that initially wanted to sync "everything" between their POS system and accounting software. When we dug into the actual business needs, we discovered they really only needed daily sales summaries and inventory adjustments. What could have been a complex, expensive integration became a straightforward daily batch process that solved their actual problem at a fraction of the cost.

Here's an uncomfortable truth that surfaces in almost every integration project: your data isn't as clean as you think it is. Duplicate records, inconsistent formatting, missing fields, outdated information — these issues often hide in plain sight until you try to move data between systems.

Integration doesn't fix bad data; it amplifies it. If your CRM has customers with missing email addresses, those gaps will propagate to every connected system. If your product catalog has inconsistent naming conventions, that chaos will spread. The old saying "garbage in, garbage out" applies doubly to integrations because now you're spreading that garbage across your entire technology landscape.

Before any integration project, conduct an honest assessment of your data quality. Run reports on completeness — what percentage of records have all required fields populated? Check for duplicates — how many variations of the same customer or product exist? Examine consistency — are phone numbers formatted the same way? Are addresses standardized?

This assessment might reveal that you need a data cleanup initiative before or alongside your integration project. That's not scope creep — it's acknowledging reality. I've seen organizations spend months building beautiful integrations only to realize they're perfectly synchronizing garbage data. Address quality issues upfront, and build validation rules into your integration to prevent future degradation.

Systems fail. Networks have outages. APIs time out. Databases lock. If your integration assumes everything will always work perfectly, you're building on a foundation of wishful thinking.

Robust integrations are designed with failure as an expected condition, not an exception. This means implementing proper error handling for every possible failure point. What happens if the source system is unreachable? What if the destination system rejects a record? What if the integration itself crashes mid-sync?

Effective failure design includes several components. Retry logic should automatically attempt failed operations with appropriate backoff intervals. Dead letter queues should capture records that can't be processed so nothing gets lost. Idempotency should ensure that if the same record gets processed twice, it doesn't create duplicates or corrupt data. Monitoring and alerting should notify the right people when something goes wrong — before users start complaining.

I once inherited an integration that had no error handling whatsoever. When the destination system went down for maintenance, the integration kept trying to send data, failed silently, and moved on. Three weeks of customer records simply vanished into the void before anyone noticed. Don't let this happen to you. Design for failure, test your failure scenarios, and make sure you can recover gracefully when things go wrong.

When the same data exists in multiple systems, you need to decide which system is authoritative. Without this clarity, you'll face constant conflicts — which customer address is correct when the CRM says one thing and the billing system says another?

For each type of data, designate a single source of truth. This is the system where that data is mastered — where it gets created, updated, and maintained. Other systems receive copies of this data through the integration, but they don't modify it. If changes need to happen, they happen in the source system and propagate outward.

The source of truth doesn't have to be the same system for all data types. Your HR system might be the source of truth for employee information, while your CRM owns customer data, and your ERP owns product and inventory data. What matters is that for any given piece of data, there's no ambiguity about which system's version is correct.

This principle extends to data creation as well. If customers can be created in multiple systems, you need clear rules about how those records get reconciled. Does the integration check for existing matches before creating new records? How do you handle near-duplicates? These questions are much easier to answer when you have a clear hierarchy of system authority.

Integrations create pathways between systems, and pathways can be exploited. When you connect your customer database to your email marketing platform, you're creating a potential vector for data exfiltration. When you link your HR system to your payroll provider, you're exposing sensitive compensation information. Security can't be an afterthought.

Start with authentication and authorization. How does each system verify that integration requests are legitimate? API keys are a minimum; OAuth tokens or mutual TLS provide stronger guarantees. Ensure credentials are stored securely, rotated regularly, and have appropriate scope limitations — an integration that only needs read access shouldn't have write permissions.

Consider data in transit and at rest. Is information encrypted as it moves between systems? If the integration stores data temporarily — in queues, logs, or intermediate databases — is that storage secured? Are you transmitting sensitive data that doesn't actually need to be shared? Minimizing data exposure reduces risk.

Don't forget about audit trails. You need to know who accessed what data, when, and why. When something goes wrong — whether it's a security incident or a compliance inquiry — you'll need clear logs showing exactly what happened. Build comprehensive logging into your integration from the start, and make sure those logs themselves are protected.

The systems you're integrating today will change tomorrow. APIs get deprecated. Data models evolve. New fields get added, old fields get removed. Vendors release major upgrades that break backward compatibility. Your integration needs to accommodate this reality without requiring a complete rebuild every time something shifts.

Build abstraction layers between your integration logic and the specific systems involved. Instead of directly mapping Field A in System 1 to Field B in System 2, create an intermediate canonical model that represents your business entities. Each system adapter translates to and from this model. When a system changes, you only need to update one adapter rather than every integration that touches that system.

Version your integrations and maintain backward compatibility where possible. When you need to make breaking changes, have a migration path. Document your integration contracts clearly so that when team members change or new developers join, they understand what exists and why it was built that way.

Also plan for business change, not just technical change. What happens when you add a new product line? When you expand to a new region with different regulatory requirements? When you acquire a company with its own systems? Integrations that are too rigidly designed become obstacles to business agility. Build flexibility into your architecture so you can adapt as the business evolves.

An integration is only as good as your ability to understand what it's doing. Without proper monitoring and observability, you're flying blind — you won't know if data is flowing correctly, if errors are accumulating, or if performance is degrading until users start reporting problems.

Implement monitoring at multiple levels. Health checks should verify that integration components are running and responsive. Data flow metrics should track how many records are processed, how many succeed, and how many fail. Latency monitoring should measure how long operations take, with alerts when times exceed acceptable thresholds. Business-level metrics should verify that the expected outcomes are actually happening — if you're syncing orders, are order counts matching between systems?

Create dashboards that give stakeholders visibility into integration health. Operations teams need to see technical metrics. Business users need to see functional outcomes. Everyone benefits from understanding whether the integration is doing what it's supposed to do.

Don't underestimate the value of good logging. When something goes wrong — and something will go wrong — detailed logs are how you diagnose and fix the problem. Log inputs, outputs, decisions, and errors. Make logs searchable and correlatable. But also be mindful of what you log; sensitive data shouldn't appear in plain text in your log files.

Making Integration a Competitive Advantage

System integration isn't just an IT project — it's a business capability. Organizations that integrate well can move faster, make better decisions, and serve customers more effectively than those stuck in silos of disconnected data. The companies I've seen gain the most from integration treat it as a strategic investment, not just a technical necessity.

The seven considerations we've discussed — focusing on business outcomes, addressing data quality, designing for failure, establishing sources of truth, prioritizing security, planning for change, and investing in observability — aren't just technical best practices. They're the foundation for integrations that deliver lasting value.

I've seen small integrations transform how teams work, eliminating hours of tedious manual processes and freeing people to focus on work that actually requires human judgment. I've seen enterprise integration initiatives break down organizational silos and enable entirely new ways of operating. The potential is real, but it requires approaching integration thoughtfully.

Start small if you need to. Pick a pain point, design a solid integration that addresses it, and learn from the experience. Each successful integration builds organizational capability and confidence. Over time, you'll develop the patterns, practices, and expertise to tackle increasingly ambitious projects. The goal isn't to integrate everything at once — it's to build a connected ecosystem that evolves with your business.