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Data is stored in many different systems across an organization. When those systems hold overlapping information—like customer records or inventory levels—keeping them aligned is important. This is where synchronization methods come into play.
Two types of sync are commonly used: one-way and two-way. One-way sync pushes data in a single direction, while two-way sync allows data to move back and forth between systems.
This article explains what two-way synchronization is, how it works, and what is required to implement it. The goal is to provide a clear, technical understanding of the process for teams exploring how to keep their systems in sync.
Two-way synchronization, also called 2 way sync or bidirectional sync, is a process where data updates flow in both directions between two connected systems. When a change is made in either system—such as adding, editing, or deleting a record—that change is automatically reflected in the other system.
This method keeps both systems continuously aligned. For example, if a contact's phone number is updated in a CRM, that change will also appear in the email marketing platform connected to it. Similarly, if the email address is changed in the marketing platform, the CRM will automatically reflect that update.
The flow of data in two-way sync can be visualized like this:
System A ⇄ System B
Each system can send and receive updates, and both remain current without manual intervention. This approach is used when both systems are considered sources of truth and need to stay consistent.
Two-way sync solves the problem of disconnected or outdated information across systems. When teams use different tools—like a CRM and an ERP—data changes in one system often do not appear in the other, causing delays and reporting errors.
Bidirectional synchronization is valuable when both systems need to stay updated with each other. This is common when both systems are used to update customer records, inventory counts, or task statuses.
Examples where two-way sync is essential include:
CRM and ERP integration: Customer and order data stays aligned between sales and finance systems
Support and engineering tickets: Support tools and development platforms both track issue status
Marketing and sales platforms: Contacts and campaign data are updated in both systems
Key benefits include:
Real-time consistency: Changes in one system immediately reflect in the other
Reduced manual work: Eliminates duplicate data entry and manual updates
Better collaboration: Teams can use their preferred tools while working with the same data
More accurate reporting: Consistent data supports better analysis and decision-making
One-way sync and 2 way synchronization serve different purposes. One-way sync sends data from one system to another without expecting updates back. Two-way sync allows both systems to send and receive updates.
The choice depends on how systems interact. If one system is the single source of truth and the other only consumes data, one-way sync is often sufficient. If both systems create and update records independently, two-way synchronization helps prevent data conflicts.
Scenario | One-Way Sync | Two-Way Sync | Key Factor |
|---|---|---|---|
CRM → Data Warehouse | Good choice | Unnecessary | Data flows only for reporting |
CRM ↔ Marketing Platform | Limited | Good choice | Contacts updated in both systems |
Support ↔ Engineering | Limited | Good choice | Both teams update ticket status |
ERP → CRM Products | Good choice | Unnecessary | ERP controls all product data |
Implementing a bidirectional sync requires planning and ongoing management to ensure data moves correctly between systems.
Start by identifying which systems will connect and what data needs to move between them. Review how each system stores and uses information.
Key questions to answer:
Which specific records (contacts, orders, tickets) need to sync?
Which fields within those records matter for synchronization?
How often do these records change in each system?
Are there privacy or compliance rules that affect data sharing?
This process helps define the scope and reveals any technical constraints that might affect implementation.
Once you identify the relevant data, create a mapping between fields in each system. Field mapping aligns similar data points, even when they have different names or formats.
For example, a simple mapping might look like:
System A Field | System B Field | Transformation |
|---|---|---|
|
| None |
|
| Convert to 2 decimals |
|
| Format as YYYY-MM-DD |
This mapping serves as a reference for how data moves between systems and how values convert during transfer.
Data synchronization can happen in real time or at scheduled intervals. The method affects how quickly systems exchange updates.
Real-time syncs use webhooks or events that notify the integration platform as soon as a change occurs. Scheduled syncs check for updates at set times.
The choice depends on:
How many changes happen per day
How important immediate updates are
API rate limits of each system
Network stability between systems
Real-time sync works well for frequently changing data, while scheduled syncs suit low-frequency updates.
Data conflicts occur when the same record changes in both systems before the next synchronization. Without clear rules, it's hard to determine which version is correct.
Common conflict resolution strategies include:
Timestamp-based: The most recent change wins
System priority: One system is designated as authoritative
Field-level rules: Different fields can have different priorities
Manual review: Some conflicts require human decision
The right method depends on how each system is used and the importance of the data.
Before full deployment, test the synchronization with a subset of data. Testing helps verify that mappings, triggers, and conflict resolution work as expected.
After implementation, monitoring helps catch issues early:
Watch for failed sync attempts
Track how long syncs take to complete
Check for data that doesn't match between systems
Review system performance during sync operations
In two way sync implementations, complex situations require more advanced configuration.
Field transformation rules
Systems often store the same data in different formats. One system might save dates as "MM/DD/YYYY" while another uses "YYYY-MM-DD". Transformation rules convert values between formats so both systems understand the data.
This also applies to:
Currency formats ($100 vs 100.00)
Status values ("Active" vs "1")
Boolean fields ("true/false" vs "yes/no")
Conditional sync logic
Not every record needs to sync. Conditional logic allows syncing only when certain criteria are met. For example, a record might sync only if:
Its status is "Active"
It belongs to a specific category
It was modified after a certain date
This reduces unnecessary data transfers and aligns with business rules.
Error handling protocols
Errors happen during synchronization. A record might be missing a required field, or an API may time out. Error handling defines what happens when sync fails:
Retry the operation
Skip the problematic record
Log the error for review
Send alerts to administrators
Recovery procedures
If a system goes offline during maintenance or an outage, synchronization may pause. Recovery procedures define how to catch up after reconnection:
Queue changes made during downtime
Replay queued changes when systems reconnect
Verify no data was lost or duplicated
Two-way synchronization involves moving data between systems. These transfers affect system performance, data security, and compliance with regulations.
Data volume management
Systems syncing many records require efficient processing. Best practices include:
Syncing only changed data, not entire datasets
Processing records in batches rather than individually
Using database indexes to speed up lookups
Scheduling large syncs during off-peak hours
Security protocols
Data in transit requires protection through:
TLS encryption for all data transfers
OAuth 2.0 or similar authentication
API keys and access tokens
IP restrictions where appropriate
Compliance requirements
Two-way sync often involves regulated data. Organizations follow frameworks like:
GDPR for personal data in Europe
HIPAA for healthcare information in the US
SOC 2 for service organizations
Compliance includes data residency controls, audit logs, and consent tracking.
Resource utilization
Sync operations use server resources:
CPU usage increases when processing complex transformations
Memory requirements grow with batch size
Network bandwidth scales with data volume and frequency
Database connections may reach limits during high-volume syncs
Monitoring tools help track these resources and optimize sync operations.
Organizations increasingly use multiple systems to manage operations, customer data, and internal workflows. These systems require consistent data for reporting, automation, and communication.
Two-way synchronization supports this need by allowing systems to exchange data bidirectionally. When information changes in one platform, it automatically updates in the other. This process plays a central role in digital strategies where teams rely on interconnected tools.
Current trends in bidirectional sync include:
Higher volumes of data events
More granular control over sync logic
Stronger alignment with compliance standards
Hybrid architectures combining cloud and on-premise systems
Platforms like Stacksync simplify two way synchronization between databases and CRMs, eliminating complex infrastructure while providing security and performance.
Ready to explore how real-time two-way sync can transform your data strategy? Talk with a cloud architect to learn more.
Modern synchronization platforms use field mapping and transformation rules to match data between systems with different schemas. This allows data to be formatted and aligned correctly during transfer.
Data updates in a two way sync based on the configuration. Real-time syncs update as soon as changes occur, while scheduled syncs run at set time intervals.
Yes, with middleware or integration platforms, legacy systems can exchange data with modern cloud applications through two way synchronization.
When a system is offline, synchronization platforms store changes in a queue. Once the system reconnects, the sync resumes and applies the queued changes.
