Guide to Data Integration

Introduction to Data Integration

The key to business longevity and scalability is access to big data in which management can base informed decisions. However, without data integration, pulling information from different sources can quickly become unorganized. By implementing integration software, organizations can optimize data management and promote business expansion.

Companies need access to a collection of internal and external data, known as big data, to gain a broader scope of their business and overall performance. This information can be qualitative or quantitative and ultimately contributes to a bigger picture of how a company is running and how it can improve.

With modern data integration tools, advanced software automates data exchange and gathering between departments and third parties, eliminating the need for manual consolidation.

What is Data Integration?

Data integration is the business process of aggregating data from disparate sources into one database, allowing quick and easy access to information. This process enhances overall data management by streamlining the exchange of accurate information from multiple sources.

While there are many integration methods available to businesses, advanced technology has automated data consolidation. Data integration software is programmed by developers to automate the integration process by connecting various services and routing data to and from target systems. This integration solution gives users quick access to information, further improving accuracy and communication.

The data integration platform was created to respond to the booming popularity of technological databases and the need to transfer information between the linked systems, known as data at rest. Data at rest is information that is not actively moving to and from solutions, such as information stored on a hard drive, spreadsheet, or in an archive.

Types of Data Integration

1. Consolidation
Data consolidation reduces the number of information storage locations by physically gathering data from separate systems and establishing an aggregated database. To successfully do this, the extract, transform, and load (ETL) technology is used. ETL pulls information from sources, translates it into a standardized format, and transfers it to a centralized database.


  • Single Interface Access
  • Time Savings
  • Enhanced Compliance
  • Optimized Data Control


  • Need Software Expertise
  • Security Risks
  • Extensive Implementation Period

2. Propagation
Also known as application-based integration, data propagation uses event-driven applications to copy information from one service to another whenever new data is introduced. Many models, such as the enterprise application integration (EAI) and enterprise data replication (EDR) solutions, utilize the data propagation method.


  • Simple Process
  • Seamless Data Exchange
  • Limited Human Intervention Needed


  • Limited Access
  • Complex Setup
  • Difficult System Maintenance

3. Virtualization

Data virtualization uses a single interface to collect information from multiple sources and offer data in a uniform fashion. While the data can be viewed in one location, it can also be stored at another site. Virtualization does not require a standardized format or single point of access, but rather retrieves and interprets data within its own processes.


  • Minimized Repetitive Data
  • Centralized Database
  • User-Friendly


  • Complex Setup
  • Inefficient Large Batch Processing

4. Federation
Data federation is a virtualization model, in which it uses a virtual database to display information in one location. However, while virtualization uses a single point of access for similar processes, federation uses a standard data model that allows data from disparate sources to integrate.

Enterprise information integration (EII) applies federation to abstract data from different sources and presents it in a unified view. This allows different applications to interact with the integration solution to analyze the data with various tools.


  • Short Development Time
  • Simplified View of Different Data
  • Minimized Storage Requirements
  • Allows Tailored Data Protection
  • Promotes Scalability


  • Must Adhere to All Data Sharing Policies
  • Unpredictable Response Time

5. Warehousing
Also known as common storage integration, data warehousing filters, reformats, and stores data in designated locations. Similar to the virtualization and federation models, warehousing provides users with centralized uniform access. However, this method creates and stores additional copies of the information in a data warehouse.


  • Optimized Data Analytics
  • Enhanced Data Version Control
  • Organized Appearance


  • Increased Storage Costs
  • Extensive Maintenance

6. Manual

Manual data integration requires individuals to collect, filter, and aggregate information without the help of automation.


  • Low Cost
  • Complete Freedom


  • Limited Access
  • Hinders Expansion
  • High Risk for Human Error
  • Requires Extensive Resources, Time, and Money
  • Inefficient Data Gathering

Benefits of Data Integration

1. Optimized Business Intelligence

Business intelligence (BI) is a blanket term that describes the overall process of utilizing technology to analyze enterprise data to help management make educated decisions. With integration, data quality is enhanced by the various tools that pull, structure, and prepare information for further analysis. The integrated data can then be generated into detailed reports and actionable insights.

2. Enhanced Decision Making
Advanced BI ensures decision makers have adequate information from all business processes to improve performance, functionality, and scalability. Without an integration system, data is unorganized, housed separately, and has limited access. However, by determining the proper data integration tool that addresses a company’s needs, management can easily pull accurate information from multiple sources to make data-driven decisions.

3. Efficient Data Management
Master data management (MDM) occurs after the initial integration process, where policies and principles from the various data source administrators are enforced. This procedure is essential because it ensures all of the data on the user’s end is compliant. Therefore, all low-quality and unreadable data is filtered out of the database, improving decision making and system response time. With MDM, users do not have to worry about manually converting or standardizing data, saving time and effort.

4. Improved Customer Relationships
Customer data integration (CDI) enables businesses to access consumer information in a structured fashion rather than having to sift through silos of documentation. With customer information readily available, companies can improve their customer service and relationships. Additionally, employees can quickly request personal and business information to answer questions and provide support.

More Insights

Need Help To Maximize Your Business?

Reach out to us today and get a complimentary business review and consultation.