It’s November 4th, and people around the world are glued to their computer screens and smartphones, waiting anxiously to find out the winner of the 2020 US presidential election. Meanwhile, interactive electoral maps set up by the likes of APNews are aggregating vote tallies from across the county. Behind these visualizations, there’s a sophisticated data synchronization system working to retrieve and validate the latest numbers from county clerks, electronic data feeds, and state websites. At the same time, verified counts are updated in near real-time to relevant poll trackers so that anxious viewers can stay on top of the data that matters most.
Imagine if you could achieve this type of visibility over the various systems operating throughout your organization? For example, you could pull up the latest numbers on marketing leads from an offshore subsidiary without going through multiple managers and platforms. With the right data synchronization 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.
Data Synchronization Definition
Data synchronization ensures that changes within one system are reflected consistently and accurately across linked systems. 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.
If we’re talking about the aforementioned marketing leads report, 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, the CDC object would read these changes and match the current dataset against previously input leads data stored on linked databases and applications. During this process, duplicate records are filtered out, and discrepancies between the two datasets are identified. 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.
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:
- 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.
- 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.
- 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.
- 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.
- 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 Centerprise. 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 Strategies and 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.
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 Centerprise
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 Centerprise
Automate Your Data Synchronization Tasks with Astera Centerprise
Astera Centerprise platform 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 Centerprise 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 of Astera Centerprise 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.