The Cost of Copying Data from One System into Another

The pain

Data silos and duplicate entries create a cycle of wasted effort. When staff copy information across systems, the organisation pays twice for the same information: once in manual data-entry time and again in potential data inconsistencies that echo through reporting, analytics and decision-making. The operational impact includes slower processing, higher error rates and degraded trust in data, which in turn affects customer-facing processes and internal controls.

The pattern is familiar. A sales order is captured in the CRM, then typed into the accounting system, then copied into a job or delivery tool. A new employee's details are entered into HR, payroll, email and the access system separately. Each step looks small. Nobody designed the process this way; it accumulated as each system was bought to solve a local problem without a plan for how information would move between them.

Why businesses tolerate it

Re-keying is tolerated because it works, more or less. The person doing it is competent, the delay is a few minutes, and the cost is hidden inside salaries rather than appearing as a line on an invoice. Integration projects, by contrast, look like a visible expense with technical risk.

The scale of the underlying problem is wider than most single businesses realise. The 2025 MuleSoft Connectivity Benchmark, a vendor survey of IT leaders, reports that the average enterprise manages 897 applications, of which only 29% are integrated, and that 90% of organisations identify business obstacles caused by data silos. Smaller businesses run far fewer applications, but the gap between "systems we own" and "systems that share data" is the same shape.

Operational consequence

Common symptoms include repeated data-entry tasks, inconsistent records across systems and approval delays caused by mismatched information. The quality cost compounds as teams attempt to reconcile data during reporting windows and audits, consuming time that could be better spent on value-added activities.

• Divergent records: a customer's address is corrected in one system but not the others, so invoices and deliveries go to different places.
• Slower cycle times: work waits until someone has time to re-enter it, so orders, invoices and onboarding all take longer.
• Reconciliation work: month-end becomes an exercise in finding why two systems disagree. See the labour cost of manual reconciliation between systems.
• Eroded trust: managers stop believing reports and start keeping their own spreadsheets, which adds another copy of the data.
• Key-person dependency: one or two people know the workarounds, and the process slows when they are away.

Financial consequence

The cost signal is twofold: direct labour to re-enter and validate data, plus risk-adjusted costs from inaccuracies and misinformed decisions. The business faces opportunity costs when decision cycles slow or errors require remediation, as well as maintenance overhead for multiple data pipelines.

An illustrative cost model

Every figure below is an illustrative assumption, not a benchmark. Replace each with your own volumes, timings and labour rates.

The 52,800 records come from 240 records a day × 220 days. In this example, re-keying labour alone accounts for about 73% of the total (R985,600 ÷ R1,357,120). If an integration removed 80% of that labour, the recovered capacity would be worth 0.8 × R985,600 = R788,480 a year, before counting fewer errors and a shorter month-end. That value is capacity, not automatically cash: it becomes a saving only if the time is redeployed to work the business would otherwise hire for, or if it allows growth without adding headcount.

What the model does not capture is often more important: decisions made on inconsistent figures, delayed invoicing that affects cash flow, and the audit or compliance effort needed to prove which record is correct.

Urgency

Duplicated entry becomes commercially urgent when:

• volumes are growing and the answer is to hire another administrator rather than fix the flow;
• customer complaints or credit notes are traced back to mismatched records;
• month-end close regularly overruns because systems disagree;
• a new system is about to be added, which would create yet another place to re-enter the same data;
• the business wants to use reporting or AI tools that depend on consistent data.

If the same customer, order or employee is typed by hand into more than one system, the business is paying a recurring labour charge for a problem that is usually solvable.

Resolution

1. Consolidate data into a single source of truth with robust integration.
2. Eliminate manual copy-paste by automating data flows and implementing data governance controls.
3. Adopt universal identifiers and standardised data models across systems.
4. Implement data quality checks and automated reconciliation to catch inconsistencies early.
5. Educate teams on the risks of duplicated data and the benefits of unified systems.

Start by mapping the handful of records that move most often: customers, orders, invoices, employees. For each, note which system creates the record, which systems receive a copy and who types it. That map makes the decision clear. Where both systems expose an API, a direct integration is usually the most durable fix. Where they do not, robotic process automation can bridge the gap: IBM describes RPA as software that handles repetitive tasks such as extracting data and filling in forms, while noting that it relies on rule-based logic and needs clearly defined processes. For the wider pattern, see the cost of business systems that do not talk to each other, and Mellorca's Systems Implementation & Integration service.

Bottom line

Reducing duplication lowers labour costs and enhances data integrity, enabling faster, more reliable insight and better customer outcomes. Duplicated data entry is rarely dramatic, which is why it survives. But every record typed twice is paid for twice, and every copy that drifts out of step creates future correction work. Measuring the volume, time and error rate makes the case for integration on the business's own numbers rather than on a generic promise.

Sources and further reading

• Harnessing the Future: Integration, AI, and APIs in Digital Transformation (last verified 10 October 2026)
• What is Robotic Process Automation (RPA)? - IBM (last verified 10 October 2026)

Related Mellorca reading

• The Cost of Retyping Customer Details into Every System
• Backing Up Data but Not the Systems That Use It
• Backups Nobody Has Tested: The Cost When Recovery Fails