Given the flood of information and emerging cloud and big data technologies, more and more companies are now adopting data integration initiatives to analyze and act on their data more effectively. For modern companies seeking to improve their strategic decision-making and competitiveness, data integration meaning isn’t so simple.
What is data integration? We’ll find out in this material!
Table of contents
- What is data integration?
- How does data integration work?
- Types of data integration approaches
- Data integration in different industries: Examples & use cases
- Challenges in data integration & how to overcome them
- How Devart’s ODBC drivers enhance data integration
- Future trends in data integration
- Why data integration is essential for businesses
What is data integration?
Data integration is the process of bringing together data from multiple, disparate sources into a single view. The process includes ingesting, cleansing, mapping, and transforming the data according to the target system, ultimately delivering meaningful and valuable insights.
The process (as the data integration definition implies) involves collecting data, converting it into a common format, and storing it in a central place like a data warehouse. This can be done using different methods, such as ETL (Extract, Transform, Load) or APIs.
What does data integration mean for the business? Data integration ensures that information is accurate and consistent across all systems. Since it makes data easier to access, it can help businesses make decisions faster and more efficiently.
How does data integration work?
Data integration in the database brings together data from multiple sources. This continuous process from extraction to transformation and loading (ETL) transforms data from disparate sources into valuable, unified information, which can then be used for business intelligence and analytics purposes.
Key steps in the data integration process
How does data integration work? The data integration process includes some main steps:
Step 1. Data collection from multiple sources
Data is gathered from various systems, such as databases, cloud storage, APIs, and spreadsheets. This step ensures all relevant information is available in one place.
Step 2. Data transformation & cleansing for consistency
The collected data is cleaned and standardized to remove duplicates, fix errors, and ensure uniform formatting.
Step 3. Data loading & storage for analysis and reporting
The processed data is stored in a central database, data warehouse, or cloud platform.
Types of data integration approaches
There are different ways to integrate data. Each of them depends on business needs and data sources.
ETL (Extract, Transform, Load) data integration
This is the most traditional data integration method. It extracts data from the source system and transfers it to the warehouse. This continuous process transforms data from disparate sources into valuable, unified information that can then be used for business intelligence and analytics.
ETL is best used when you need to integrate and structure data from multiple, disparate sources for reporting, analysis, or business intelligence. It is ideal for companies with structured data that requires thorough cleansing and transformation before analysis.
ELT (Extract, Load, Transform) data integration
ELT is a variation of ETL designed to handle large amounts of data typically found in cloud environments. With this approach, the data is first extracted and loaded into a target system. Transformation occurs only after the data has been loaded.
In other words, ELT is best suited for companies that need to quickly load large amounts of raw data and later transform it. This gives them more flexibility compared to other data integration types in terms of how and when the data is transformed. If your company works with unstructured data or needs to transform it in near real time, ELT is better than traditional ETL.
Real-time data integration
This method, according to data integration definition, uses APIs and streaming data to update information instantly. It continuously delivers data updates from multiple sources, enabling immediate analysis and action. Real-time integration is ideal for monitoring dynamic processes and making informed decisions with real-time detected insights, like fraud detection or live dashboards.
Data virtualization
This is a more modern approach to data integration. It enables real-time access and query of data without moving or physically altering it. Instead of consolidating data into a single repository, this technique creates a virtual layer that connects to different data sources and provides a unified view of data from multiple systems.
This technique works best when real-time data access is more important than physically moving or consolidating data. Having developed from the initial definition of data integration, this is ideal for companies with disparate data sources that want to create a unified view without the complexity and delay of traditional ETL processes.
Application integration & middleware
This approach connects business applications and APIs, ensuring smooth data exchange between different systems. Middleware acts as a bridge, helping apps communicate.
Each of these data integration approaches has its own benefits. Continuous data integration improves the value of company data. Integrating data into a centralized system allows quality issues to be identified and necessary improvements to be made. This results in more accurate data, which is known to form the basis for high-quality analytics.
There’s no one-size-fits-all solution for data integration. The meaning of the right formula and data integration can vary depending on business requirements.
Data integration in different industries: Examples & use cases
With optimal data integration, companies in different industries can significantly reduce the time required to prepare and analyze data.
Let’s take a look at a typical use case: Without unified data, a single report typically requires logging into multiple accounts or sites, accessing the data within native apps, and then copying, reformatting, and cleansing this information before even beginning analysis. Performing all these processes as efficiently as possible clearly demonstrates the significant benefits of data integration. Below are some real-world data integration use cases:
Data integration in healthcare
- Hospitals and clinics combine patient records from different systems, such as electronic health records (EHRs), lab reports, and insurance databases.
- Doctors and nurses get a full view of a patient’s health history, improving diagnosis and treatment.
- Medical research teams use integrated data to track disease patterns and improve healthcare solutions.
Data integration in finance
- Banks and financial institutions merge transaction data from different systems to detect fraud.
- Real-time tracking helps identify unusual spending patterns and prevent fraud before it happens.
- Financial analysts use integrated data to create accurate reports on market trends and investment risks.
Data integration in e-commerce
- In this data integration example, online stores connect customer data, sales reports, and inventory levels across multiple platforms.
- Automated stock tracking helps prevent overstocking or running out of products.
- Data-driven insights allow for personalized product recommendations and targeted promotions.
Data integration in marketing & customer analytics
- Businesses gather data from websites, social media, and customer service platforms to understand customer behavior.
- Marketers use integrated data to create personalized ads and targeted emails. Customer support teams can track interactions across channels, providing better service.
Challenges in data integration & how to overcome them
At its core, data integration is about data transparency and availability. Instead of storing data in silos across different systems and tools, companies can access all relevant data across an enterprise-wide network and share information seamlessly between systems and departments. Companies that properly use data integration solutions get numerous benefits:
- Higher ROI.
- Higher data quality.
- Better decision making.
- Increased operational efficiency.
- Better business culture.
As you can see from the examples, the data integration process helps businesses set up proper operations. But integrating data isn’t always easy. Here are some common problems and how to fix them.
1. Data silos and inconsistency
Problem: Data is spread across different systems. This way, it is hard to combine. Some of it may be outdated or duplicated. Studies show that over 20% of companies rely on 1,000+ data sources for analytics and business intelligence. This data comes from various applications and exists in multiple formats (structured and unstructured). Even though the data is available, accessing it in a consistent way is a challenge.
Solution: Use data integration tools to connect systems and standardize data formats for consistency.
2. Performance and scalability issues
Problem: As data grows, systems slow down. Reports take longer, and analysis becomes harder. IT and data teams struggle to keep up.
Solution: Cloud-based tools can help. They scale easily and process large amounts of data fast.
3. Compliance and security challenges
Problem: Data security is crucial when transferring between systems. Companies must control user access to protect information. Unauthorized access can lead to financial loss and reputational damage. Data privacy laws like GDPR and HIPAA must be followed. Leaks and misuse are serious risks.
Solution: Use encryption, limit access, and run regular security checks. The right data integration strategy can help tackle these challenges.
As you can see, right data integration management strategies are handy in overcoming even such complex challenges.
How Devart’s ODBC drivers enhance data integration
Devart’s ODBC Drivers are a great solution for easy data integration in database, between different databases, apps, and BI tools.
Easy database connectivity
How does data integration work with Devart’s ODBC Drivers? ODBC drivers allow different databases and applications to work together. Devart’s ODBC drivers make it easy to connect SQL, NoSQL, and cloud databases, and integrate with BI tools for quick data analysis.
High-performance data integration
Devart’s drivers guarantee fast and reliable data integration methods and data access, even with large datasets. Businesses get effective data management without slowing down.
Try It Now: Download a free trial of Devart’s ODBC drivers for smooth database connectivity and ODBC data integration.
Future trends in data integration
New technologies are changing data integration. By 2025, companies will handle more data than ever. The demand for AI, especially generative AI, will grow.
Agility will be a top priority across industries. Leaders need accurate and accessible data to make quick decisions. This helps them act fast on new opportunities and risks. To achieve this, data must be accurate, consistent, and reliable.
AI & Machine Learning for data cleansing
Artificial intelligence is on everyone’s lips due to its ability to automate or accelerate user tasks, leading to greater efficiency and productivity and reducing reliance on manual work. AI and machine learning can clean and organize data automatically. This makes integration faster and accurate.
Cloud-based serverless data integration
Cloud solutions simplify data integration without server management. AI-powered cloud tools speed up data flow development, saving time for architects, analysts, and IT teams.
Self-service AI tools, like step-by-step wizards, make data tasks easy for non-technical users. They remove the need for manual coding, which is costly and time-consuming. AI-driven recommendations can also help cut costs by suggesting the best processes and configurations.
Data mesh & data fabric
These new architectures focus on decentralizing data management. With such technologies, you can access data easier and use it across different systems.
A key issue in the modern approach to data integration is transparency of the data flow, from the source, through any changes, to the point of transmission. Data tracking helps identify anomalies and optimize performance. Furthermore, tools for tracking data along the entire route can help you maintain a competitive advantage.
Why data integration is essential for businesses
Still unsure about what data integration is and how it helps? Data integration helps combine data from different sources into one place. So you can easily access and use it.
Using solid data to inform key decisions can mean the difference between leading the market and falling behind. The ability to effectively integrate data from disparate sources into a central location contributes to a better understanding of the organization and enables conclusions to be drawn based on data. Such insights are the foundation for business agility and innovation.
For efficient data integration management, try Devart’s ODBC drivers!
