Data Integration: What, Need, Advantages, Approaches of Data integration

26/10/2022 0 By indiafreenotes

Data integration involves combining data residing in different sources and providing users with a unified view of them. This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories, for example) domains. Data integration appears with increasing frequency as the volume (that is, big data) and the need to share existing data explodes. It has become the focus of extensive theoretical work, and numerous open problems remain unsolved. Data integration encourages collaboration between internal as well as external users. The data being integrated must be received from a heterogeneous database system and transformed to a single coherent data store that provides synchronous data across a network of files for clients. A common use of data integration is in data mining when analyzing and extracting information from existing databases that can be useful for Business information.

The data integration approaches are formally defined as triple <G, S, M> where,

G stand for the global schema,

S stands for the heterogeneous source of schema,

M stands for mapping between the queries of source and global schema.

Advantages of Data integration

Increased Revenue Potential

Data integration can help you create new revenue streams or expand into new markets by allowing access to more information in your organization faster.

Cost Reduction

Data integration reduces the need for manual tasks by allowing more steps in your process to become automated. Manual processes are time-consuming, expensive, and prone to human error so using automated data integration can reduce costs by eliminating these workflows.

Improved Efficiency

Through data integration, you are able to automate the steps in your existing processes, which allows employees to focus on more complex work. This increased efficiency can allow you to provide better results for your customers while also increasing employee satisfaction.

Improved Decision Making

Having access to all relevant information about your company or industry can help you make better decisions as a manager, marketer, or entrepreneur.

Improved Data Quality

With the right data integration software, you are able to automatically validate incoming information as well as make changes to existing records. This approach provides more accurate data without requiring employees to spend a lot of time on data entry.

Improved Customer Experience

A simple way to increase customer satisfaction is through providing them access to the information they need as quickly as possible without requiring employees to manually respond to each request.

By integrating your data you can automatically provide customers with deeper insights in a way that’s personalized to their needs. This approach makes the customer experience more efficient and satisfying for both parties.

Increased Innovation

Innovation is an important part of any business strategy but it’s difficult to introduce new concepts when you don’t have access to the information you need.

By integrating your data and allowing employees the ability to easily create new reports, dashboards, and visualizations you are able to provide a faster platform for innovation that allows employees to be more creative.

In addition, because everyone has access to this information it can be used as the basis for discussion, which increases collaboration across departments.

Stronger Customer Relationships

In today’s business world, it is important for companies to have a strong understanding of customer needs and preferences in order to provide them with better products and services.

With data integration, you are able to automatically create a historical record that can be used in conjunction with demographic information to gain a deeper understanding of your customer base and provide them with more personalized insights.

Improved Security

Data integration helps to improve security by streamlining the process of managing user permissions and access to those who need it.

Approaches of Data integration

Middleware Data Integration

In this method of data integration, middleware or software is used to connect applications and transfer the data to databases. It is very handy while integrating legacy systems with newer ones.


  • Better data streaming
  • Easier access between systems


  • Less access
  • Limited functionality

Manual Data Integration

Manual data integration is the process of integrating all the different data sources without any automation. This is usually done by data managers using custom code and is a great strategy for one-time instances.


  • Reduced costs
  • More freedom


  • Greater room for error
  • Difficult to scale

Application-Based Integration

In this method, software applications do all the work – locate, retrieve and integrate data from different sources and systems. This strategy is great for businesses that work in hybrid cloud environments.


  • Easier information exchange
  • Simplified process


  • Limited access
  • Inconsistent results
  • Complicated setup

Uniform Access Integration

This method integrates data from multiple, disparate sources and presents it uniformly. Another useful feature of this method is that it allows the data to stay in its original location while doing this. This technique is an optimal approach for organizations that need access to multiple, disparate systems without the cost of creating a copy of the data.


  • Low storage requirements
  • Easier access
  • A simplified view of data


  • Strained systems
  • Data integrity challenges

Common Storage Integration

This method is similar to uniform access integration, except that it creates a copy of the data in a data warehouse. This is certainly the best approach for businesses who want to make the most out of their data.


  • Increased version control
  • Reduced burden
  • Enhanced data analytics
  • Cleaner data


  • High storage costs
  • High maintenance costs