5 Common Pitfalls in Data Synchronization—and How to Avoid Them

More than a few headaches can be caused by poor data synchronization. This results in teams receiving wrong reports, broken dashboards, and confused customers. This is common if you have ever had data mismatches between tools. So, let's break down five sync issues and solutions for them.

Mismatch Between Source and Target Data Structures

If the structures of systems trying to sync match and have different ways of embodying it, errors are nearly assured. For example, in a custom field, we could have a flag indicating whether this is an opportunity. We would also have a field that holds a person’s name. If the source has a field customer_id and the target expects id, then the sync data might fail or duplicate data. This typically occurs when an item team changes a schema and forgets to change the sync logic, leading to 8×8 values being pinned to a new object. It can silently break in different field types, such as integer vs. string, giving dirty data. Real example: One retail store changed its ‘Order Date’ in the e-commerce system without talking to the data team. The sync broke, and then weeks of sales data were lost from the analytics dashboard. How to avoid it:

• This assures that you are using tools that provide automatic schema mapping and alerts.
• Write down all schema changes and sync logic on a shared space.
• It is recommended to test the structure of your test field regularly before doing a sync job.

It has to be able to be spoken in a single language. Otherwise, you will force sync to fail. For further information, see how the data synchronization works at https://skyvia.com/learn/what-is-data-synchronization.

Missing or Weak Change Detection Rules

It checks to see which data needs syncing. Syncing everything, or worse, nothing that has changed, without it. This leaves it to massive delays or stale information in reports. The problem is that many systems do not have a good ‘last modified’ timestamp. The timestamp can even change, even when no record has changed. Some teams even perform full table refreshes to live away from this. That’s fine—until your data grows. It’s slow, and it costs money to sync millions of rows daily. For instance, it was noticed that delays with tracking updates were ever present with a logistics company. They were syncing the entire shipment table every hour. There was no proper change flag, so the system couldn’t tell what had changed. Solutions that work:

• Reely on update markers such as last_updated, version columns, or audit tables.
• Prefer systems where records aren’t overwritten without document update time tracked.
• Create logs and set up limits on what is being synced.

Change detection isn’t just a detail—it’s the core of smart synchronization. Robust integration solutions often include built-in change tracking and sync optimization features that eliminate the need for full table refreshes.

Data Conflicts in Two-Way Synchronization

Often, two-way synchronization is considered the ideal setup. However, these two systems will be in sync, updates will flow both ways, and the users will feel like everything is connected. But here’s the catch—conflicts. Most teams expect they to be much more common. For instance, if a customer updates their email in the support portal. At the same time, a sales rep changes it in the CRM. Which one is right? There is no rule if the system flips back and forth on the value or overwrites both. This isn’t theory—it happens. Until recently, I worked with a SaaS company where lead syncing was done between the site and a CRM. If two updates conflicted, tags would go to the lost leads, and sales would fail to get a follow-up. To fix this:

• Establish conflict resolution rules in the form of ‘latest update wins’ or ‘system priority. ’
• Know when and by whom the change was made.
• Avoid it if you don’t need a way to sync. In many cases, one-way is safer and simpler.

Two-way sync without rules is asking for trouble. Plan it right, or data will clash, and trust will vanish.

Delays from Slow or Unstable Connections

Sync logic can sometimes be quite imperfect, but even a slow/shaky connection can break everything not properly “synced.” For example, if you are always behind and your sync reads through long-distance servers or cloud apps, but it takes 40 minutes (or more) to stay current on new records arriving every 10 seconds, you are always out of sync. The exact issue was faced by a finance team that I worked with. It was to sync billing data between a cloud ERP and a reporting dashboard. There was an error in that one region had poor bandwidth, so the sync took a little longer than it should have. The invoices were not in the reports until the following day, causing confusion during weekly reviews. Using an ELT platform could have helped streamline and stabilize the data sync process, reducing such delays and errors. What causes these delays?

• Long physical distance between systems
• Overloaded servers or poor routing
• Lack of retry settings in sync tools

Here’s what helps:

• Use compression to shrink payloads
• Sync in smaller batches during low-traffic hours
• Choose tools with built-in retries and error handling

But remember, no matter how great your data sync setup is, the Internet or system load planning can take you down! Running software where or how you want it is more important than how good it is.

Small Sync Issues Can Lead to Big Data Problems

It’s not just a technical issue—it’s an issue of business risk, which means if your systems are not in sync, there is a negative impact on your business. Reports contain wrong numbers, missing records, and mismatched fields that hurt reports, delay decisions, and confuse teams. We’ve also seen that all of our biggest sync problems (conflicting structures, bad change tracking, conflict errors, poor connections, and no monitoring) are all avoidable. The good news? You don’t need complex solutions. You need a good structure, smart rules, and solid tools so that you don’t need to change the workflow; instead, you desire to do it better. Keep your setup clean. Track changes the right way. Plan for error before it happens. These steps will apply whether you’re syncing two tools or ten. Never be slowed down by sync issues. Take some time to read through this full explanation of how sync really works and, in the end, how to actually do it. It’s clean sync, which equates to clean data, and clean data always succeeds.