The Real Cost of Bad Data: Why System Integration Matters

The Problem Nobody Budgets For

Gartner estimates that poor data quality costs organizations an average of USD 12.9 million per year. That figure comes from Fortune 500 companies, but the mechanics are the same at every scale. If your CRM says a customer paid but your accounting system says they have not, someone has to investigate. If your webshop creates a contact record that already exists in your CRM, you now have two records diverging over time. If a sales rep updates a phone number in one system but not the other three, your outreach fails silently.

For a 20 person company running four or five disconnected tools, these problems typically consume 5-15 hours per week in manual reconciliation, duplicate cleanup, and error correction. At EUR 40 per hour fully loaded, that is EUR 800 to EUR 2,400 per month -- spent not on productive work, but on fixing data that should have been correct in the first place.

How Data Silos Form

Data silos rarely result from a deliberate decision. They accumulate. Marketing adopts a CRM. Sales starts using it too, but finance was already on Exact Online. The warehouse uses a separate inventory tool. Customer service tracks tickets in yet another system. Each tool works fine in isolation. The problem is the gaps between them.

Here is how the damage compounds:

Duplicate records. A customer places an order on your webshop, which creates a new contact. But they already exist in your CRM from a sales conversation three months ago. Now you have two records. The webshop record has their shipping address; the CRM record has their decision maker name and deal history. Neither system has the full picture.

Stale data. A customer updates their billing address on your portal. The portal saves it. But Exact Online still has the old address, so the next invoice goes to the wrong location. The customer calls to complain. Someone manually updates the address in Exact Online. Two weeks later, the portal syncs again and overwrites it with the old data because the sync was configured to push, not pull.

Missing context. Your sales team closes a deal in Pipedrive and marks it as won. But the operations team uses a different system and never gets notified. The customer waits a week for onboarding, then calls asking what happened. Your team scrambles, and the customer starts the relationship with a negative impression.

Quantifying the Damage

The cost of bad data is not just the time spent fixing it. There are three layers:

Direct Costs

These are measurable in hours and euros. Data entry duplication, manual reconciliation, re-sending invoices to the correct address, re-doing work because someone acted on outdated information. For a typical SMB, we see EUR 1,000 to EUR 3,000 per month in direct labor costs attributable to data quality issues.

Indirect Costs

Slower decision making because you cannot trust your reports. A sales pipeline that overstates revenue because closed-lost deals were not synced back from accounting. Inventory counts that do not match between your warehouse system and your webshop, leading to overselling or unnecessary restocking. These are harder to quantify but often larger than the direct costs.

Opportunity Costs

The follow-up that never happened because the CRM did not flag it. The upsell conversation that never started because the account manager did not know the customer had expanded their usage. IBM estimated that bad data costs the US economy roughly USD 3.1 trillion per year, and a significant portion of that is missed revenue rather than wasted labor.

A Concrete Example

One of our clients ran Pipedrive for sales, Exact Online for accounting, and WooCommerce for their webshop. Before integration, here is what a typical week looked like:

• 12 new webshop orders manually entered into Exact Online (20 minutes each = 4 hours)
• 3-5 duplicate contact records created per week, discovered and merged manually (15 minutes each = 1 hour)
• 1-2 invoices sent to wrong addresses per month (30 minutes each to fix, plus customer irritation)
• Weekly "reconciliation meeting" where sales and finance compared numbers (1.5 hours, two people = 3 person-hours)
• Monthly report that took a full day to compile because data had to be pulled from three systems and cross-referenced manually

Total: roughly 12 hours per week, or EUR 2,000 per month in labor alone. The reconciliation meeting also delayed decisions by a week because numbers were only aligned once every seven days.

How Integration Solves This

The fix is not complicated in concept: make your systems talk to each other so data enters once and propagates correctly. In practice, this means building integrations that handle the specifics -- field mapping, conflict resolution, error handling, and monitoring.

After integrating the three systems for the client above:

• Webshop orders automatically create invoice drafts in Exact Online and update deal status in Pipedrive
• Contact data syncs bidirectionally with a defined source of truth (CRM wins for contact details, accounting wins for billing info)
• Address changes in any system propagate within 5 minutes
• The weekly reconciliation meeting was eliminated entirely
• Monthly reporting became a dashboard that updates in real time

The integration project cost EUR 9,000. Monthly labor savings were EUR 2,000. Payback period: under five months. And that does not account for the eliminated customer complaints or faster decision making.

We cover the technical patterns behind these integrations in our System Integration Best Practices guide, including how to handle the inevitable edge cases around webhook reliability and data conflicts.

Where to Start

If you suspect data quality is costing you money, start with a simple audit:

1. Pick one entity (contacts, orders, or invoices) and compare counts across all systems that store it. If the numbers do not match, you have a problem.
2. Track manual data entry for one week. Every time someone types information into a system that already exists somewhere else, log it. Multiply by 52 for your annual cost.
3. Ask your team where they do not trust the data. They already know which reports are unreliable and which systems have stale information. Their answers tell you where integration will have the highest impact.

If you are running Exact Online alongside a CRM or webshop, the Exact Online to CRM integration guide walks through the specific connection patterns and common pitfalls for that ecosystem.

The most expensive data problem is the one you have normalized. When your team says "we always double-check that in the other system," that is a data quality tax you are paying every day. Integration eliminates the tax.