How to Eliminate Manual Data Entry Between Apps

The average knowledge worker spends 2.5 hours per day on manual data transfer between applications. They copy a customer name from an email into the CRM, then re-type the same information into the accounting system, then enter it again into the shipping platform. Each re-entry introduces a 1-3% error rate, which compounds across systems into a tangled web of mismatched records, failed reconciliations, and hours of cleanup work.

Eliminating manual data entry is not about replacing one tool with another. It is about connecting the tools you already use so that data entered once flows automatically everywhere it needs to go. This guide shows you how to audit your data flows, select the right integration approach, and build the connections step by step.

Step 1: Audit Your Current Data Flows

Before building any automation, you need a complete map of how data moves through your business. Spend one week documenting every instance of manual data transfer:

• Source and destination: Where does the data originate, and where does it need to go? For example: "Customer email order (source) to QuickBooks sales order (destination)."
• Frequency: How often does this transfer happen? Daily, per-order, per-customer, monthly?
• Volume: How many records per transfer? 5 per day or 500?
• Who does it: Which team member performs the data entry? What is their hourly rate?
• Error impact: What happens when this data is entered incorrectly? Wrong shipment? Incorrect invoice? Missed order?

Most businesses are shocked to find they have 15-25 distinct data transfer points happening manually every day. Prioritize by volume times impact: the transfers that happen most frequently and cause the most damage when wrong should be automated first.

Figure 1: Manual data entry as the bottleneck (left) vs. automated integration hub (right)

Step 2: Evaluate Your Apps' Integration Capabilities

Every app in your stack has a different level of integration readiness. Assess each one:

• Native integrations: Many apps connect directly to each other (e.g., Shopify to QuickBooks). These are the easiest to set up but often lack customization. Check each app's integration marketplace first.
• API availability: Apps with REST APIs (most modern SaaS tools) can be connected through Make.com or Zapier. Check the API documentation for the specific endpoints you need (create, read, update, delete).
• Webhook support: Apps that support webhooks can push data in real-time when events occur. This is superior to polling (checking for changes on a schedule) for speed and efficiency.
• File-based integration: Legacy systems that only support CSV/Excel import/export can still be automated. Use Make.com to generate files, upload them via SFTP, and trigger imports on a schedule.
• No integration at all: Some apps (especially legacy desktop software) have no API. In these cases, consider browser automation tools like Puppeteer or desktop RPA, or plan to migrate to a modern alternative.

Step 3: Design Your Integration Architecture

With your data flow map and integration assessment complete, design how data will move between systems. There are three primary patterns:

• Event-driven (recommended): A change in one system triggers an immediate action in another. Example: a new order in Shopify immediately creates a customer record in QuickBooks. This provides real-time data flow and is the most reliable approach.
• Scheduled sync: Run a batch process at regular intervals (every 5 minutes, hourly, daily) that checks for new or changed records and syncs them. Use this when real-time is not necessary or when APIs have strict rate limits.
• Hybrid: Use event-driven for critical paths (orders, inventory, invoices) and scheduled sync for less time-sensitive data (customer updates, product catalog changes).

Always build idempotent integrations. If the same data is processed twice (due to a retry or error), the second run should not create duplicates. Use unique identifiers (order numbers, PO numbers) to check for existing records before creating new ones.

Step 4: Build and Test Your Integrations

Start with your highest-priority data flow and build the integration in Make.com. Follow this process for each flow:

• Configure the trigger: Set up the webhook or polling module that detects new data in the source system.
• Transform the data: Use Make.com's built-in functions to reformat dates, concatenate address fields, calculate values, and map source fields to destination fields.
• Handle lookups: Before creating records, look up related data. For example, before creating an order, look up the customer ID in the destination system. If not found, create the customer first.
• Create the record: Use the destination app's module to create or update the record with the mapped data.
• Error handling: Add error handlers for API timeouts, rate limits, and validation errors. Route failed records to an error queue (Google Sheet, Slack notification, email) for manual review.

Test each integration with 10-20 real records before going live. Verify the data matches perfectly between source and destination. Pay special attention to special characters, long text fields, and numeric precision.

Step 5: Monitor, Maintain, and Expand

Integrations are not set-and-forget. Build ongoing monitoring:

• Execution dashboards: Use Make.com's built-in logs to track scenario execution success rates. Set up a weekly summary of total runs, successful runs, and failures.
• Alert on failures: Configure immediate Slack or email alerts for any scenario that fails. Include the error message and the data that caused the failure for fast debugging.
• Reconciliation reports: Build weekly or daily reconciliation checks that compare record counts and key totals between connected systems. For example, compare the number of orders in Shopify vs. QuickBooks for the day.
• API change monitoring: Subscribe to changelogs for every app you integrate with. API changes can break integrations without warning. Check Make.com's app module update notes regularly.

Once your first integration is running reliably, move to the next highest-priority data flow from your audit. Most businesses can eliminate 80% of manual data entry within 4-6 weeks of focused integration work.

Figure 2: Recommended implementation timeline for eliminating manual data entry

What You Will Gain

Companies that systematically eliminate manual data entry report:

• 20-30 hours per week recovered per operations team member.
• Data accuracy above 99.5% compared to 95-97% with manual entry.
• Real-time data availability across all systems instead of end-of-day or next-day updates.
• Employee satisfaction increase as team members shift from data entry to strategic work.

Use our cost calculator to estimate your specific savings, and explore our data entry automation solutions for a deeper look at what is possible. You may also want to start with a specific workflow like purchase order automation or invoice creation as your first integration project.