The world of data revolves around collecting, extracting, storing, processing, and analyzing information to guide business strategies and decisions. The validity and usefulness of the data collected, processed, and stored vastly depends on its accuracy and consistency across all devices, applications, and platforms, and this can only be achieved through data synchronization.
Data synchronization or data sync for short, refers to the process of consolidating data or information across different systems, ensuring data consistency, accuracy, and privacy. It is a continual process that ensures that new and existing data across all systems are fused for coherence and updated automatically. Depending on your approach and tool, data can be synchronized in real-time, near real-time, or in batches.
For example, data sync is like modifying information in an organization’s database on one system and having the same modifications and updates across all software and systems used by other employees with the same access.
Before synchronization, all data collected must first be processed and integrated. So, in essence, proper data integration makes for easier synchronization.
Why is Data Synchronization Important?
Data synchronization is crucial in data management and the smooth operation of any organization. The amount of data consumed by an organization daily requires that all the information disseminated within the organization is in sync to avoid conflicts and communication gaps in the data. It is essential for data integrity, enhancing system security and access control, cultivating team collaboration, preventing data loss, and improving data availability.
The Challenges of Unsynchronized Data
There are so many challenges posed by unsynchronized data to businesses. Some of them are: data silos, extensive data entry, data inaccuracy, operational inefficiency, reduced productivity, lack of real-time data, data conflict, data duplication, data loss, collaboration and communication issues among employees (especially from different departments, e.g., sales vs customer service), and loss of customer trust.
For example, when the data of different departments within an organization are inconsistent, it can cause setbacks like authorization and access issues, inefficient data management, etc., which can consequently lead to a decline in sales or profits.
The Impact of Data Synchronization
When data is properly synced, it can enhance performance and impact an organization positively. For example:
- Executives will be supplied with up-to-date data for crucial strategic decision-making.
- Distributors can access the latest product and marketing information.
- Customers will receive tailored product details and services.
- Employees will collaborate across departments with real-time information.
- Customer service teams will be empowered with better, faster information to enhance customer satisfaction and loyalty.
The Importance of Data Synchronization: Cloud Computing and Mobile Devices
Organizations deal with large data collected and stored from numerous sources and distributed across different applications and devices. These devices rely on data for various functions, including personal information on websites, emails, and apps.
To ensure data security and accuracy, constant updates to user-generated (source) data and the destination (target) data are essential.
With more accessibility to cloud-based data and mobile devices and BYOD (Bring Your Own Device), data synchronization must be done effectively to maintain high data quality and up-to-date information and to ensure uniformity of data.
How Data Synchronization Works
Data synchronization works according to predefined settings established in the tool being used for the data synchronization process. It could be done manually using Python scripts or through the setup of automated data pipelines within an ETL solution. Irrespective of the method adopted, data synchronization can only work along these processes:
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Update Event: Once an update trigger has been set, any modification or changes made to one dataset automatically update in all datasets. The system constantly checks and initiates an update when it detects any changes. To spot these changes in the target database, it can use various methods, like flags or scripts that check when files were last modified.
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Change Identification and Extraction: Considering that data synchronization is not a complete redo of your data set, it simply identifies the modifications made and the areas in which they were made by checking the different versions or flags that show a new or different value, then it executes those changes in the same areas of other applications and systems.
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Data Transfer: This can occur through a file transfer or the web. Depending on the scheduled parameter for the data movement, data synchronization happens synchronously or asynchronously.
When it is synchronous, changes and updates take place in real-time, which leaves no room for errors and inaccuracies, but when it is asynchronous, the affected changes are scheduled. E.g., hourly, biweekly. The synchronous method is more cost-intensive but better to use than the asynchronous, as it mitigates data discrepancies.
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Parsing Incoming Changes: Some new information may be unidentical to others in the existing data sets. In such instances, the new data goes through formatting and cleaning to harmonize it with the existing one to maintain data consistency.
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Apply Changes to Existing Data: This is done mainly to avert any data loss during the update process and can be done in either of these ways - Transactional, Snapshot, and Merge.
- The transactional method ensures that all data changes are applied similarly, one after the other, and in the order that they occurred.
- The Snapshot method makes all data the same, but only the first version keeps the complete history of changes.
- Merge method, as the name implies, combines modifications from new and existing data without taking either as the absolute one. It rather updates both data versions accordingly.
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Confirm Successful Updates: Upon successful completion of an update, the system’s API will send an update confirmation message. If the process is unsuccessful, it will try to reinitiate the update again and if that fails, return an error message instead.
Types and Methods of Data Synchronization
There are two types of data synchronization and they are the one-way data sync and the two-way data sync.
One-Way Vs. Two-way Data Synchronization
The One-way or unidirectional data sync updates changes from the source data to the target data. This process can only be initiated by the source and not the other way around, as changes made in the target data don’t affect the values in the source data. This type of data sync requires more data security for the source data, as any breach endangers the entire data on both ends (source and target).
The Two-way or Bi-directional data sync makes it possible for changes initiated in either data set to reflect on the other (source or target) as communication goes both ways. The priority of data security is equal for both data. Some examples of this data sync type are Dropbox, Google Drive, and Google Docs.
Data Synchronization Methods
There are various methods of data synchronization, applicable in their own areas. Let's list some of the more known ones:
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File Synchronization: This is mostly used for home backups, external hard drives, and updating portable data via flash drives. It is fast in updating data across different locations and automatically removes duplicate files.
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Version Control: This makes for easy modification of data shared by multiple users. All modifications are effected across systems and applications simultaneously. For example, granting someone access (Editor) to a Google Docs file.
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Distributed File Systems (DFS): Here, data synchronization can only be done on connected systems (from the source to other systems). If the other devices are not connected to the source during the process, they cannot be synchronized.
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Mirror Computing: This method is more effective with just two locations -source and target- as it duplicates the exact data meant for just one location (target), which makes it handy for backup.
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Database Synchronization: This method is widely used for syncing data that have tabular structure and are stored in relational databases. However, similar techniques can be used for data stored in other systems with data having tabular structure. Usually, this method involves ETL tools.
Database Synchronization and Its Types
Below are the types of database synchronization:
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Insert: This database synchronization involves copying newly added source table records to the target table to ensure they align with primary key values. It includes inserting any missing rows into the target tables. By inserting missing rows in the target table, this database sync process replicates the data in the source table into the target table, ensuring that the values in the primary key match.
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Update: This synchronization ensures that the data in the two tables are consistent and identical. It tracks the values in the source table rows and implements changes from it to the target table. This process keeps both databases updated and in sync at all times.
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Drop: When data is erased from the source database, the drop sync ensures that the same is removed from the target database.
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Mixed: This synchronization combines the functions of the other three. It handles the adding, updating, and erasing of data from the target database.
Data Synchronization vs. Other Data Processes
Data synchronization is what guarantees that the data stored in multiple locations are consistent and up to date. It is a continuous process that ensures the constant communication of databases. Highlighted below are other data processes that are worthy of note:
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Data integration: combines data from different sources into a unified view (as a single data set).
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Data replication: a process that makes copies of the same data and stores them in various locations for data availability and backup.
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Data pushes: an unidirectional type of data integration that automatically transfers data after its creation from one location to the other - usually Point A to Point B (source to destination).
Challenges of Data Synchronization
As with any technical process, data synchronization is not entirely hassle-free. Below are some of the challenges of data sync.
Security and Confidentiality Concerns
Considering the rate of work flexibility – job hybridization, remote and on-site roles – and the use of mobile devices as well as multiple systems across organizations, it is commonplace that data security issues will be predominant. If the on-site or third-party (external) systems that handle synchronization solutions are compromised, the organization stands the risk of data loss and/or exposing sensitive business information that may give rise to data compliance and governance issues, lawsuits, and fines.
Data Quality Issues
When different people within an organization use different systems, data can easily be mixed up. To ensure the accuracy and security of data, ongoing updates and validations must be incorporated from all sources. Without a good synchronization system in place, data transactions and access may be slow or even break down, resulting in low data quality.
Data Complexity and Compatibility
When an organization experiences growth, the data it stores increases. The more staff, customers, business partners, vendors, and services are recorded, the larger the data becomes. This growth makes the data more complex as the formats keep changing with new records and technological advancement. Synchronizing data can be quite challenging when dealing with large amounts of data, multiple systems, and the need to transform complex new data to fit into older systems.
Real-time Updates and Performance Requirements
The process of data synchronization is very demanding and requires real capacity planning before execution. If this is overlooked, real-time syncing of very large data sets can cause system downtime that affects other applications and processes.
Maintenance and Management Difficulties
For data synchronization to work perfectly, it requires constant follow-up on maintenance and proper management. If not properly managed and constantly updated, can lead to rejected or obsolete data.
Benefits of Data Synchronization
Some benefits of data sync include:
Removal of Data Silos
This is achieved through the constant sharing and updating of data across different systems. It nurtures collaboration and eliminates trust and authorization issues among employees.
Prevention of Extensive Data Entry
Data sync makes the flow of data between systems seamless, thereby reducing the need for manual data input as per changes or updates. This saves time and reduces errors.
Ability to Perform Multiple Data Operations
Data sync can perform functions like updating, adding, and deleting data automatically (where necessary) across all systems, making for data consistency.
Real-time Data Syncing
This is one of the most important benefits of data sync - its ability to update changes made in one system immediately across other systems, ensuring user access to the latest information.
Prevention of Data Loss
The replication and backup of data across systems help in preventing data loss. Should one system fail, the same data can be retrieved from another synchronized system, reducing data loss.
Data Synchronization Use Cases
Data synchronization use cases include:
1. Data Harmonization - Healthcare Industry
In hospitals, for example, the data of patients are collected from various sources like the laboratory, the radiologist, the gynecologist, the dentist, the general consultant, etc. Data synchronization makes for accurate, consistent and up-to-date data on patients for proper diagnosis and treatment.
2. Distributed Computing - E-Commerce Platforms
E-commerce platforms use distributed computing to manage their websites. Data synchronization is used to keep their product information, inventory status, and prices updated in real-time across all systems. This is to ensure that customers are furnished with the same information irrespective of location and device access type.
3. Storage and Analysis - Financial Industry
Financial institutions deal with lots of money movements every day. Data sync helps them gather data from different branches and ATMs to a central data storage (database) for analysis. This helps the bank monitor customer transactions, detect fraudulent activities and make informed financial decisions.
4. Distributing Updates - Software Development
Application updates need to be sent by the developers to millions of users per time. Data sync guarantees the even distribution of the same updates/versions to all the users at the same time, regardless of their locations.
5. Other Use Cases
Other use cases include maintaining data availability, consolidating business units, and creating a holistic view of business processes from different angles.
Data Synchronization Tools
Data synchronization tools ensure that changes or modifications made to your data are automatically updated according to the standards of your security needs across all systems simultaneously in near real-time.
Some of them are:
- An integration platform as a service (iPaaS) that links apps through their interface or APIs
- RPA (Robotic Process Automation) software that uses bots to imitate & automate human tasks
- Enterprise automation platform that combines API-based app with end-to-end workflow automation
Depending on your data synchronization needs, data sync solutions like Skyvia, Talend, HubSpot, and DryvIQ can get the job done.
Summary
Data synchronization is the heartbeat of accurate, consistent, and up-to-date information across systems. The need and importance of data sync solutions cannot be overemphasized, as wrong or obsolete data can affect business decisions and hamper the growth and operations of an organization.