Each day in the business world, a sheer volume of data is generated all around us. It is in every system and application that people use.
Data drives businesses to make data-driven decisions relevant to increasing business efficiency and providing a better customer experience.
As data grows exponentially and is distributed across multiple sources, it brings new complexities for enterprises to manage and organize efficiently.
Data integration enables companies to combine the collected data from various sources in a unified manner to have in-depth insights into relevant information.
This article will feature data integration, its common techniques, pros and cons, and why it is important for businesses.
How does data integration work?
Data integration refers to the process of combining data from disparate sources into a unified view for efficient data management.
It enables businesses to organize data into valuable assets, which help drive meaningful insights and actionable business intelligence.
Nowadays, data integration is one of the core digital transformation strategies for companies as it is able to:
- Improve operational efficiency
- Boost consumer satisfaction
- Enhance competitive advantage
According to Reportlinker.com, the global data integration market is expected to grow to $19.09 billion in 2026 at a CAGR of 11.99%.
As data comes from various formats, the goal of data integration is to aggregate all data from the data warehouse regardless of its formats, structure, or volume.
Data integration techniques
The following techniques can be used in data integration:
Manual data integration
Manual data integration occurs when integrating different data sources without automation. Data managers individually use custom codes in real-time.
Pros:
- Reduce costs
- Absolute freedom over data management and integration
Cons:
- Scalability can be a challenge
- Greater room for error since it deals with complex data connections or big data queries
Middleware data integration
Middleware data integration is an integration that involves middleware or software applications as an intermediary, connecting applications and transferring data to databases.
The middleware helps businesses move legacy systems to a range of newer applications and creates easy access to data by users in the organization.
Pros:
- Users have easy access to systems
- It offers better data streaming
Cons:
- Middleware work solely with specific systems
- Requires skilled developers to maintain middleware, which leads to increased operational costs
Application-based integration
In this method, application-based integration involves using software to find, clean, retrieve and integrate data from different sources and systems.
Application-based integration often involves automation which creates compatibility between data sources to another system.
Pros:
- Seamless data exchange between different systems and destinations
- Simple process and doesn’t require technical expert
Cons:
- Complicated setup for designing applications that require developers, managers, and/or analysts with technical expertise.
Uniform data access integration (UDAI)
Uniform data access integration (UDAI) involves integrating data from multiple and disparate sets and presenting it in a uniform manner.
This technique is an ideal choice for organizations as they can access data easily while keeping the source data at its original destination.
Pros:
- No high-storage requirement
- Unified view of data
- Easier data access
Cons:
- It might lead to compromising data integrity
- A strained data host system
Common storage data integration
Common storage data integration is an integration technique that involves creating and storing a copy of data in a data warehouse.
With the data in one place, users can access it anytime and run data analytics and business intelligence (BI) tools whenever needed.
Pros:
- Less strain on data host systems
- Improves data analytics
- Better data integrity
- Cleaner data appearance
Cons:
- Higher costs for data storage
- Higher maintenance costs for using third-party warehouse
Why is data integration important for businesses?
Data integration promotes a “single source of truth” since integration turn data into a unified system.
Here are the reasons why data integration is important for your business:
Easy and fast connections
Deploying new connections might be difficult for organizations. Thus, there is a need for data integration.
This makes it possible because of the pre-built adapters, connectors, and the Integration Platform as a Service (iPaaS) architecture. iPaaS enables integration experts to work with different data standards and formats.
Data integration allows businesses to have seamless, easy, and fast connections between data stores.
Integrate data from multiple sources
The ultimate goal of data integration is to integrate data from multiple sources. This is essential for businesses as some of their data sources are disparate and siloed.
That being said, integrated data allows companies to make better sense of all the information, which might help make data-driven decisions.
Improved data integrity, quality, and security
Data integrity, quality, and security are the important elements relevant to data integration practice.
With the right data integration software, it ensures accurate and valid information as well as secured data in a unified system.
Streamlined processes
Most importantly, data integration streamlines business processes. Data integration enables companies to automate the workflows in their existing processes.
Data integration increases operational efficiency, allowing businesses to provide better customer results. This is a great factor for overall business profitability.
