What is Data Integration - Popular Methods And Applications

What is Data Integration?

Data integration can be considered as one of the main components in the data management process. It is the process of collecting and consolidating data from all sources into one single dataset or data warehouse. The ultimate goal of data management is to provide users with consistent access and delivery of data and to meet the different needs of all business applications and processes. 

Why is Data Integration Important? 

With the market becoming more competitive than ever, organizations need to embrace big data and all its benefits. Data integration helps in managing all of these giant datasets to provide complete and accurate information. One of the most common use cases of data integration is in the management of business and customer data. It helps to support business intelligence and advanced analytics with a complete picture of financial risks, key performance indicators (KPIs), supply chain operations, and other important business processes.

Another important role of data integration is in the IT environment to provide access to data stored on legacy systems. There are a number of modern big data analytics environments (eg: Hadoop) that are not compatible with the data in legacy systems. Data integration can help bridge that gap between valuable legacy data with popular business intelligence applications.

Challenges to Data Integration

With so much data out there in the world today, there are a multitude of challenges to data integration. Gathering data from multiple data sources and turning them into a unified structure is a big challenge in itself. While data integration methods can provide a number of benefits in the long term, they can also be hindered by a number of challenges:

Data From Legacy Systems

Perhaps the greatest challenge to data integration methods is to integrate the data stored in legacy systems or mainframes. These data often have missing markers, such as date and time for activities, which most modern systems would usually have.

Data From New Systems

There are a number of new systems today generating different types of data from a multitude of sources - IoT devices, cloud, sensors, etc. Now, this data can also be real-time data or unstructured data, which provides another challenge. Figuring out how to quickly adapt to these new demands becomes extremely critical for any business to win. 

External Data

For any organization to flourish, it cannot always depend on its own internal data. There are a number of external sources that organizations have to take in in order to stand out from their competition. However, most of these external sources of data may not have the same level of detail or format as internal data, making it very difficult to integrate them. There are also a number of contracts that may be signed with external vendors which make it difficult to share the data across the entire organization.

Wrong Integration Software

Although you may already be using data integration solutions for your organization, there is the unfortunate trap of using the “wrong” type of software. With so many different solutions in place, it can be hard for organizations to choose one that best fits their needs. Or worse, even with the right software, you could be using it the wrong way.

Data Integration Techniques

There are five main data integration techniques. Below are the advanatges and disadvantages of each one and when to use them:

1. 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.

Pros:

• Reduced costs
• More freedom

Cons:

• Greater room for error
• Difficult to scale

2. 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.

Pros:

• Better data streaming
• Easier access between systems

Cons:

• Less access
• Limited functionality

3. 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.

Pros:

• Easier information exchange
• Simplified process

Cons:

• Limited access
• Inconsistent results
• Complicated setup

4. 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.

Pros:

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

Cons:

• Strained systems
• Data integrity challenges

5. 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.

Pros:

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

Cons:

• High storage costs
• High maintenance costs

Data Integration Tools

There are different data integration tools for different data integration methods. A good integration tool should have the following characteristics - portability, ease of use, and cloud compatibility. Here are some of the most popular data integration tools out there:

• ArcESB
• Xplenty
• Automate.io
• DataDeck
• Panoply

Data Integration Examples

Data integration plays a key role in the healthcare industry. The integrated data from patient records can provide a unified view of the complete information regarding a patient and help doctors in diagnosing their medical conditions and diseases. Effective data acquisition and integration can also provide accuracy for medical insurers through accurate records of patient names and their contact information.

Another important example is in the finance industry. With fraud becoming a growing concern, it can help banks identify and eliminate any instances of fraud. If the data is siloed and fragmented, AI cannot mine the data for anomalies and outliers. An integrated database will help to catch fraud cases more easily.

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Ready to Take the Next Step?

Simply saying that data integration helps companies have all their information in one place is an understatement. It is, in fact, the first and foremost step that businesses need to perform to unleash their full potential. Unless you dive deep into the depths of this topic, it is hard to imagine its many benefits. Are willing to learn more about data integration, you can enroll in Simplilearn’s Data Engineering Certification Program that will help you master all data engineering skills. Get started with this course today and upgrade your skills to stand out from the rest.