Imagine achieving visibility over the various systems operating throughout your organization, where you could pull up the latest numbers on marketing leads from an offshore subsidiary without going through multiple managers and platforms. With the proper data synchronization and relevant tools and strategies, you can create a BI environment that allows you to do just that.

But before we dive into the topic, let’s take a closer look at what data synchronization is.

What is data synchronization?

Data synchronization, in simple terms, is the process of making sure that data is consistent across multiple locations or systems. In practical terms, data synchronization means automatically propagating any changes you make to data in one place to all the systems in other specified locations, regardless of their storage models or architectures.

Data synchronization definition

A more formal data synchronization definition would be:

Data synchronization is the process of ensuring that data across source and target systems, databases, or devices is consistent, accurate, and up-to-date.

Imagine you have the same information stored in different places—like on your phone, your laptop, and a cloud server. Data synchronization is what keeps these copies of the data identical, so that when you make a change in one place, it’s automatically reflected everywhere else. It’s like having all versions of a document updated to be the same, so you always have the latest and most accurate information, no matter where you access it.

The goal with data synchronization is to have every system reflect the same information at any given time. When talking about building a truly modern BI architecture, this sort of enterprise-wide harmonization is critical. Of course, there are a few key elements you need to have in place before you can reach that goal.

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What is needed for successful data synchronization?

First, robust data ingestion pipelines are necessary to capture and process data in real time because a business pulls information from many different places such as sales systems, customer service platforms, social media. This ensures that every update is promptly recorded and transmitted and that business intelligence dashboards are always showing the most up-to-date picture.

Second, for data to be truly useful, it must be synchronized in a way that everyone in your organization understands it in the same way. Standardized data definitions can be used to maintain a consistent view of data across the enterprise. These are like a common dictionary for your business data, ensuring that business critical terms mean the same thing across all departments and systems. Complementing this, metadata management creates a detailed catalog of your data—essentially, data about data. This includes information on where data comes from, what it means, how it’s used, and its quality.

Third, implementing advanced conflict resolution strategies—often leveraging machine learning—helps reconcile discrepancies when multiple systems attempt to update and synchronize the same information simultaneously. In complex business environments, data is often updated from multiple places at once, which can lead to conflicting information.

Finally, for all the pieces of a business intelligence system to work together smoothly, they need to be able to talk to each other. You can achieve seamless integration via modern data platforms that offer connectivity to various sources and destination. Data and API integration ensures that all systems communicate effectively, supporting a unified and accurate BI environment.

Data synchronization in modern enterprises

Data synchronization is particularly important in environments that rely on real-time analytics and AI-driven decision-making, where even minor discrepancies can lead to errors or delayed insights.

Modern data synchronization methods have evolved well beyond simple data replication. For example, artificial intelligence is now used to enhance conflict resolution. When multiple sources attempt to update the same piece of data simultaneously, AI algorithms help determine which change should be prioritized. Meanwhile, blockchain technology is employed to verify data integrity and ensure that every modification is transparent and tamper-proof.

Another key innovation is the use of streaming change data capture (CDC) to continuously monitor and capture data changes, allowing for immediate updates across systems. Such real-time updating is vital for distributed computing environments where data is spread across various servers and platforms. Advances in edge computing and federated learning have introduced decentralized frameworks that reduce latency as the data is processed closer to the source while still enforcing data security and compliance standards.

As organizations increasingly operate in hybrid and multi-cloud environments, data synchronization now extends beyond traditional ETL pipelines. It incorporates AI-driven architectures and API-first integrations to ensure seamless, bidirectional updates across disparate ecosystems.

How data synchronization works

Let’s continue with the aforementioned marketing leads report example to understand how data synchronization works. Normally, there would be some form of change data capture (CDC) in place between the subsidiary’s database (probably a dedicated platform like HubSpot) and your target systems/s.

When updates are made at the source, i.e., your marketing team adds a new lead, updates contact information, or changes a lead’s status in HubSpot, the CDC object would read these changes and match the current dataset against previously input data stored on linked databases and applications. This comparison is crucial for a couple of reasons.

First, it automatically filters out any duplicate records. Maybe a lead was entered into HubSpot and also manually into another system. CDC recognizes duplication and avoids creating two entries for the same lead. Second, it also identifies any discrepancies between the datasets in HubSpot and your other systems—perhaps a lead’s phone number was updated in HubSpot but not yet in your sales system. These updates and modifications are then applied to records available at the destination.

Similarly, suppose you have two-way data synchronization in effect. In that case, any changes made to the marketing data at the destination would be processed through the differential calculator and reconciled with what’s available in your source system.

To summarize, here’s what the data synchronization process looks like:

Change detection

The system is always watching your source database or application for changes. It could use database triggers, log reading, or check timestamps.

Change capture

When a change happens (new record, update, delete), the CDC system grabs the details. This includes what changed, when, and the new value.

Change staging/queueing

Captured changes are often put in a temporary holding place, called a staging area or queue. This is like a buffer, making sure changes are processed correctly and in order, even if your destination systems are temporarily slow or offline.

Data transformation (optional)

Sometimes, the data format at the source is different from what you need at the destination. Synchronization might include a step to transform the data, converting or mapping it to fit the destination system’s needs.

Change application

Finally, the captured and any transformed changes are applied to your destination systems. This means updating, inserting, or deleting records in the destination database to match the source.

Conflict resolution (if needed)

As we talked about, with two-way sync or multiple sources, conflicts can happen if data is changed in different places at the same time. The sync process has rules to decide which change wins and how to fix any differences, keeping your data accurate.

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Data synchronization strategies

You can synchronize your data in several ways, although one-way synchronization is the most widely used strategy across industries. Here are the different data synchronization strategies organizations use:

Full synchronization (full refresh)

Also called full refresh, full synchronization is where you completely replace the data in your destination system with a fresh copy from your source system. It’s the easiest synchronization method to set up and understand. Essentially, the process includes wiping out the old data at the destination and loading in the entire dataset from the source. Full synchronization is typically used when:

• Data volume is relatively small: If you’re dealing with a limited amount of data, the overhead of transferring the entire dataset will be insignificant.
• Data integrity is critical: It guarantees complete consistency between source and destination, as you always start with a fresh, authoritative copy.
• Initial data load: It’s often used for the very first synchronization to populate a destination system with data.
• Infrequent updates are needed: If your data changes very infrequently, a full refresh is likely sufficient.

That said, full synchronization is inefficient in cases where large datasets are involved as transferring them very time will be resource-intensive and time-consuming. Depending on the data volume, you are also likely to end up consuming a significant bandwidth if synchronizations are frequent.

Incremental synchronization (delta synchronization)

Incremental synchronization only transfers the changes made since the last synchronization. These changes are typically increments, or deltas, hence it’s also referred to as delta synchronization. Incremental synchronization relies on CDC to identify and track changes in datasets and is used when:

• Data volume is large: Incremental sync is much more efficient for large datasets as you only transfer a fraction of the data.
• Near real-time updates are needed: Changes can be synchronized more frequently, providing a more up-to-date view of data in the destination systems.
• Bandwidth is a concern: It significantly reduces network bandwidth usage compared to full synchronization.
• Continuous data integration is a requirement: Incremental sync is deal for scenarios where you need a continuous flow of data updates.

Compared to full sync, incremental synchronization is typically more complex to implement. It also requires overhead to track and capture changes at the source, and if the change mechanism fails, there’s a risk of missing the updates and losing data consistency.

One-way synchronization

As the name suggests, in one-way synchronization, data flows in only one direction, from a designated source (master) to one or more destinations (slaves). This means that changes are made only at the source and are propagated to the destination systems. One-way sync is used when:

• You need centralized data authority: You have a single, authoritative source of data, and you want to distribute or replicate this data to other systems, such as data warehouses, read replicas, or backup systems for reporting, read-only access, or backup purposes.
• Reporting and analytics systems are used: Populating data marts or data warehouses for BI and reporting from operational systems.
• Backup and disaster recovery are critical: Creating backups of a primary database in a secondary location.

One-way data synchronization can be restrictive when it comes to collaboration, as changes are only made at the source systems. Depending on the sync frequency, destinations might not have the absolute latest data at every moment. Finally, one-way synchronization is not the appropriate strategy when multiple systems need to modify the same dataset.

Bi-directional synchronization

Compared to one-way synchronization, bi-directional synchronization allows changes to flow in both directions and between multiple systems. Bi-directional sync normally needs sophisticated conflict resolution, especially in scenarios where the same datasets is modified in both systems concurrently. It is used when:

• Multiple systems need to be equally authoritative: If data can be created or modified in multiple systems and all systems need to reflect the latest state, bi-directional sync becomes necessary.
• Operating in collaborative environments: Used in scenarios where multiple users or teams need to work with the same data from different systems and need to see each other’s changes.
• Distributed systems are used: For keeping data consistent across geographically distributed systems.

Bi-directional sync strategy is significantly complex to implement, especially since the conflict resolution is necessary, which itself is challenging to design and implement. If conflict resolution is not properly implemented, there’s a high risk of in datasets.

Merge synchronization

Merge sync is an advanced form of bi-directional synchronization as it not only synchronizes data in both directions but also attempts to intelligently merge changes made in different systems into a unified, consistent dataset. It is used when:

• Working with complex data models: When you have complex data structures and relationships where simple overwriting in bi-directional sync might lead to data loss or corruption.
• Collaborative editing with complex data is involved: Scenarios where multiple users might be editing different parts of the same complex data object simultaneously.
• Resolving intricate conflicts: When you need sophisticated conflict resolution beyond simple timestamp-based or source-priority rules.
• Integrating data from multiple sources: Can be used to merge data from multiple disparate sources into a single, unified view.

Merge synchronization is the most complex performance-intensive sync strategy to implement. It requires a thoughtful design of merge rules and conflict resolution strategies to ensure data integrity.

Data synchronization advantages

Alright, so now that we’ve covered the basics of data synchronization, here are a few ways your organization can benefit from implementing data synchronization across its systems:

1. You ensure that a single version of truth (SVOT) is in place for all key processes. Whether you’re talking about financial statements, sales figures, or the production details from your manufacturing units, all of your decision-makers will be creating reports and visualization dashboards from the same dataset.
2. You can cut down on duplicates, errors, and other inconsistencies by synchronizing data between two systems or more; as long as the source data is validated, you will have a higher quality of data across your entire enterprise.
3. You have an up-to-date duplicate set of your source data in multiple locations. If you experience critical data loss in one area, it can be quickly rectified through bidirectional data synchronization from a linked database.
4. You can open up avenues for collaboration between different departments by aligning your data infrastructure opens. Suppose the marketing team can reference the same data as the sales team. In that case, they can proactively fix emerging issues by creating more focused campaigns around specific target segments or improve the marketing-to-sales handoff for particular types of leads.
5. You can avoid much of the manual effort involved in moving updated data from one system to another by switching to an end-to-end data integration platform like Astera. This software allows you to start automating data synchronization tasks that would otherwise bottleneck your reporting processes. Remember, even if you’re running workflows manually, you still need to find time to execute, monitor, and troubleshoot these processes. An automated data synchronization solution does away with that effort. 

Data synchronization use cases

Your data synchronization strategy needs to be built around your organization’s data architecture and future requirements. Based on these constraints, you can arrange your data synchronization process in different ways with assistance from data synchronization tools. Here are different data synchronization use cases:

Maintaining data availability

Say you run an insurance company that processes all of its claims through legacy mainframes. Over the past few years, your hardware may have begun to develop faults that cause it to go offline intermittently, leading to the loss of critical data. 

To solve this issue, you may want to set up a cloud data synchronization process so that your OLTP data is backed up to a remote, scalable data warehouse environment like Amazon Redshift or Google Big Query. In this case, you’d want to set up one-way data synchronization on a time-based trigger so that transactional updates are routinely replicated to the cloud. 

Consolidating business units

Consolidating Disparate Employee Tables with Astera

Let’s assume you have several business units operating internationally that all produce the same type of data. You’ll probably want to set up a data synchronization process that can pick up real-time updates from your company’s various regional centers and apply validation rules to ensure inputs are in a standard format. The output could then be loaded incrementally into a centralized database. 

This system would offer an up-to-date view of disparate business units that can then be used to compare performances and make improvements in different regions. 

Creating a 360 view of a business process

Sometimes, one set of data does not provide a complete picture of a business process. Take your sales department as an example. A simple report on your revenue generation over the past quarter may tell you whether your performance has improved or not, but it won’t tell you why. 

To get these insights, you need to bring in data from other sources. So, you might want to pull in traffic and conversion figures from your online channels to get a better idea of how customer engagement contributes to sales. Or, you could look to integrate CSAT surveys from customer support channels into your reporting so that you can analyze which areas of your product are receiving positive and negative feedback. 

A proper data synchronization strategy would allow you to pick up current data from disparate sources such as CRM systems, analytics platforms, and survey tools at defined periods and load these to a data warehouse. 

Key attributes relating to revenue, traffic, engagement, and average customer satisfaction could be loaded to slowly changing dimension (SCD) tables. This table would identify changes in values and add a new row with an effective start and end date field to show which records are active at the moment.

Basic Dataflow Showing Disparate Datasets Loaded to an SCD Table in Astera

Automate your data synchronization tasks with Astera

Astera is an AI-powered, fully automated data management platform. It offers advanced change data capture functionality that allows you to identify updates, deletion, and modifications in source systems based on time or event-based triggers that in turn results in efficient data synchronization. 

Apply these to your selected source table, and Astera will create a changelog that matches its structure. With each subsequent load, changes will be tracked in additional metadata fields. The ETL engine will then pick up these changes and apply them to your destination object. It’s fast, powerful, and efficient. 

Download the free trial to see how our end-to-end data integration platform can handle your data synchronization use case. Or contact our technical team for a personalized demonstration to get a practical look at how we can synchronize data across your enterprise.

Looking for an AI-driven platform for data synchronization? Give Astera a try.

Synchronizing data does not have to be complex. Astera leverages AI-driven automation and offers a 100% no-code platform that enables you to implement your data synchronization strategies without relying too much on IT.

Start Your 14-day Free Trial