What Are Data Integration Patterns?

Integrated data helps businesses unearth new analytics and eliminate redundant tasks. So, it’s no wonder more and more organizations are integrating core systems like Marketing Automation with CRM or eCommerce with ERP to dig deeper and scale effectively. 

On the surface, integration seems like a simple point-to-point process of connecting systems, but as with most things in life, it’s rarely that straightforward. Every software solution is unique, and the data between systems isn’t always in the same format. Integration forces data compatibility, and a standardized approach gets that data into a usable format even faster. 

This is where data integration patterns come in. 

What are Data Integration Patterns?

Data integration patterns are important because they provide a framework for how to best connect systems. The movement of the data is orchestrated with greater precision and speed by following a logical sequence of steps. 

Which data integration pattern you select depends on your business use case and the problem you’re trying to solve. There are five patterns to choose from, each with its own benefits and uses. In the following sections, we’ll highlight each and when to use them. 

Types of Data Integration Patterns

There are five common types of data integration patterns: migration, broadcast, aggregation, bi-directional synchronization, and correlation. Each differs in how data is synced and shared. Let’s explore. 

Data Integration Pattern #1: Migration

If you’ve ever moved from one software solution to another, the term ‘migration’ is already familiar to you. Commonly, businesses will migrate data from an old CRM/ERP/Marketing Automation to a new CRM/ERP/Marketing Automation. Migration takes data from the source system (ex: an old CRM) and moves it to the destination system (ex: a new CRM). A pattern automates the movement of that data from the origin to the destination at a particular point in time. 

This type of data integration pattern is most common when businesses are moving from one system to another, but it also has uses for:

  • Backing up data sets
  • Consolidating systems
  • Adding nodes to database clusters
  • Replicating database hardware
  • Extending current infrastructure

Data Integration Pattern #2: Broadcast

Broadcast data integration patterns move data from a single source system to multiple destination systems in real-time or near-real-time. Another way to think about it is to imagine a radio station broadcasting a signal. That radio signal leaves the station and feeds into peoples’ radios, but nothing comes back to the radio station. Broadcast data integration is essentially a one-way sync of data from one solution (the origin) to many others (the destinations). 

Like migration, broadcast is only syncing data in one direction. However, unlike migration, it doesn’t move the entire mass of data at a single point in time. Instead, it takes a transactional approach, moving only the data that has changed since the last time it synced. In this way, broadcast patterns are optimized for fast and reliable record processing. 

If you’re trying to keep data across multiple systems updated at once, you’ll need broadcast, bi-directional synchronization, or correlation patterns to do it. For example, you would use one of these if you’re trying to track inventory across your ecommerce site, brick-and-mortar stores, and wholesale partners. But, how do you know if you should use broadcast or one of the other two? Choose broadcast if:

  • You need real-time data, usually updated in intervals of an hour or less
  • You want data to flow automatically, usually through a push notification or scheduled job
  • You don’t need the origin system to sync with the new data in the destination system

Data Integration Pattern #3: Aggregation

An aggregated data integration pattern is the opposite (sorta) of broadcast. Instead of moving data from a single source to multiple destinations, it takes or receives data from multiple sources and moves it into a single destination. Now the data is merged in that one application so it’s always up-to-date and won’t be duplicated unnecessarily. Aggregation is useful for things like:

  • Modernizing legacy systems with an API
  • Featuring data from multiple systems on reports or dashboards
  • Maintaining compliance with data from multiple systems

Data Integration Pattern #4: Bi-Directional Synchronization

If you’re looking to unite multiple datasets in multiple systems, you may want to consider bi-directional synchronization. It’s most useful for two systems that must perform different functions based on the same dataset while still maintaining their own datasets. For example, if you have an order management system and a customer support system, each should work for its intended purpose, but still share datasets so you can easily see the connections between someone’s order and their support request. 

Although the different datasets exist, the two systems behave as one and share a real-time view of the data. Bi-directional synchronization maintains data integrity across these systems while still allowing each to perform its own individual functions. A few use cases for bi-directional synchronization patterns might be:

  • Integrating systems for operational efficiencies
  • Streamlining a quote-to-cash process
  • Syncing support incidents across platforms
  • Syncing lead records between a marketing automation and CRM

Data Integration Pattern #5: Correlation

A bi-directional integration pattern creates new records for information that exists in one system but not the other. A correlation pattern also syncs data between two systems, but, unlike a bi-directional sync, it will only sync data that already exists in both systems. It won’t “fill in the gaps” if one system has a piece of information that the other does not. Instead of uniting datasets like bi-directional syncs do, a correlation pattern simply intersects datasets that already exist.

Correlation allows you to scope only the specific data you want to share between two systems, so you’re not moving the full scope of the dataset in both directions. One real-world example of using correlation integration patterns could be for a hospital group with two locations. Both hospitals must have the same data on patients so no matter which location the patient goes to, their history is on file. 

Choosing the right data integration pattern for your project depends on what you’re trying to accomplish. Work with your integration provider to make the decision that’s best for your business growth. If you need help getting started, contact us for a free consultation. 

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