How to Implement a Successful Data Integration Strategy | RudderStack

neub9
By neub9
5 Min Read

Data integration is a major pain point as companies seek to become increasingly data-driven. Even simple cases are subject to data integration challenges. These challenges only multiply as complexity grows, amounts of data increase, and the desire for real-time functionality is introduced. The reality for most companies involves data silos, brittle APIs, and struggles to build and maintain the data pipelines required to provide good data to the right end users to drive better business decisions.

Data integration often involves a combination of legacy systems and new technologies. Plus, as business needs evolve and require new use cases, the number of different sources and destinations can rapidly grow and change. Building and maintaining a clean architecture with robust automations and connectors to handle these challenges in a scalable manner requires a thoughtful data integration process.

A successful data integration strategy leads to effective and accurate data analysis when it comes to data warehouses.

The Four Components of Data Integration

Let’s highlight a few important components of data integration:

  • Data Migration
  • Enterprise application integration
  • Master data management
  • Data aggregation

Data Migration

Data migration refers to transferring data from one location, application, or system to another. It includes storage migration, cloud migration, and application migration.

Enterprise Application Integration

Enterprise application integration (EAI), is an approach commonly used to manage the interoperability between the separate systems that businesses utilize to manage big data. This enterprise data process requires solving problems related to the organization’s modular architecture.

  • Interoperability: Management of different languages, data formats, and operating system components so that a connection is established without any hiccups.
  • Integration: Setting a standard process to allow data flow management between different source systems and applications while ensuring consistency.
  • Stability, Scalability, & Robustness: Ability to adjust to the implemented solution seamlessly.

Master Data Management

MDM, or Master Data Management, is a discipline focused on the cooperation between IT and the business to achieve accuracy, uniformity, accountability, stewardship, and semantic consistency of shared data sets.

Challenges regarding the implementation of an MDM strategy usually refer to the complexities, overlaps, governance, and setting up the required policies and ownership standards.

Data Aggregation

Data aggregation combines disparate data sources and is bifurcated into two major aspects: data warehousing and data federation.

Three Tips for a Successful Data Integration Strategy

Find the best data integration providers

There are many vendors out there with various data integration platforms that are both efficient and resourceful. Therefore, choosing the most suitable data integration solution for your business should be your number one priority.

Finding the right vendor who can overcome all the data integration challenges while implementing the right data management strategy with timely delivery and speed is the most important piece of the puzzle.

Establish a data governance process

To unlock your data’s full value, you should establish and implement a set data governance process in your organization. This process needs to prioritize and include managing risks, data quality, business processes, and data management as a whole.

Having a set data governance policy in place will help you improve your operational processes and ensure that your data is present in the right format, with the right quality and availability for your stakeholders.

Implement data security

Businesses in touch with the latest data integration trends also need to find a way to safely and securely connect on-premise data using different cloud applications and systems.

Taking action on this subject should be a priority, considering the large volume of data that keeps growing.

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