Building a Repeatable Data Integration Framework

According to Precisely’s definition of the term, data integration refers to the requirement to combine data from multiple separate business systems into a single unified view, often called a single view of the truth. 

As this definition suggests, the role of data integration is to:

• Collect data from varied sources throughout an organization’s IT infrastructure.
• Validate, correct, reformat, and deduplicate the data as necessary to maximize its quality.
• Blend it into a single, unified pool of information into which business intelligence and analytics applications can natively tap.

With companies today generating and receiving an ever-growing flood of information on a daily basis, data integration has become an indispensable element of a modern enterprise data architecture.

Why you need to create a repeatable data integration framework

Data integration is never a once-and-done process because data and data sources are constantly changing. To keep up, companies need a data integration framework with a basic structure that can be extended, repeated, and scaled as new sources and types of data are added to the mix.

Let’s take a look at what it takes to implement such a framework.

1. Understand and iterate the business case.

What are the business goals you want your data integration framework to support? What degree of flexibility must be included to future-proof that framework so that it can adapt as business goals evolve or new technology must be integrated into your infrastructure?

2. Determine what data sources to include.

Traditional systems like mainframe and IBM i still play a huge role in the day-to-day operations of large enterprises and smaller companies alike. Such platforms are home to a wealth of valuable transaction data that can be key to making your business case successful and that your framework can’t afford to neglect.

3. Minimize data transformation complexities.

Transforming data from disparate sources into standardized formats is fundamental to the data integration process. However, finding staff members skilled in such transformations isn’t easy. For example, over the past five years, 37 percent of the workforce with mainframe expertise has been lost. Similar skills gaps are occurring even for newer technologies such as the cloud and Hadoop.

A successful data integration framework must integrate different data sources without requiring specialized expertise or coding. It should feature a simple visual user interface that allows your current staff to employ a design once, deploy anywhere approach.

4. Determine how changes to data will be communicated.

How quickly must data changes be communicated to meet your business goals? Can it be done by batch processes, or must it be in real time? Will increasing data delivery speeds overstress your current data pipeline structures?

Your data integration framework must handle ever-growing data volumes gracefully, transparently, and with minimal manual intervention, even when new data sources are incorporated. That means only change data, rather than entire datasets, should be transmitted.

5. Ensure reliable data delivery

Even the best-designed systems sometimes have hiccups. Your data integration framework should ensure that business operations are minimally affected when data delivery is interrupted. It should, in fact, guarantee that data will be reliably delivered, without loss, once any disruption is resolved.

Precisely: Tools to build a modern data integration framework

Connect offers high-performance ETL and real-time data replication capabilities that can help you implement all the components of a state-of-the-art data integration framework. With Connect:

• Legacy system data is captured and delivered in real time.
• Integration is highly scalable.
• Data delivery is resilient.
• A design once, deploy anywhere approach is enabled.
• Current staff can work with a user-friendly interface without requiring new skillsets.

For more information, read our eBook How to Build a Modern Data Architecture with Legacy Data