/ 5 methods for data integration

5 methods for data integration

Data integration tactics are a crucial element in creating a successful data-driven company. Businesses can surpass competition by using a variety of integration tactics. A data integration strategy takes into account your business’s many data types and sources, the integration use cases and problems you must solve, and the platform and software you’ll use to integrate the data. There are many integration strategies that organizations can employ depending on their needs.

Data integration strategies help identify and put into practice the most effective ways for extracting, storing, and connecting information to business platforms and systems. Modern data integration techniques have evolved to consider ongoing data management and storage advancements, primarily cloud-based.

Contrary to popular assumptions, there is more than one method for integrating data. In reality, there are various methods for integrating data. Each one is distinct and satisfies the general requirements of most enterprises. Business leaders should consider how the business primarily uses data, the company’s long- and short-term objectives, the available resources, and the extent of the integration demand to determine the optimal strategy. Knowing where to start can be challenging, given all of those considerations.

 

 

5 data integration methods

1. Integration based on applications

In this method, software programs handle every task in the data integration process. They identify, retrieve, clean, and combine data from many sources. It is simple to transfer data between sources thanks to this automated strategy.

Because it is popular among businesses operating in hybrid cloud environments, this strategy is commonly referred to as enterprise application integration. These companies must work with numerous data sources, including those that are on premises and in the cloud.

Some of the advantages of this strategy include the following:

  • A simpler interchange of information: Information may be seamlessly transferred across systems and departments using an integration application.
  • Fewer resources are used: The procedure is mainly automated, so managers and analysts can have time to focus on other tasks.

Among the drawbacks of this strategy are the following:

  • Inconsistent outcomes: The method is not standardized and differs between companies that provide this as a service. You must consider the characteristics of your organization’s data to find an integration app that works for you.
  • Restricted access: This method calls for specialized technical expertise and a data manager or analyst to supervise application setup and maintenance.
  • Challenging data management: The integrity of the data can be compromised when combining various sources. Someone should supervise the process to double check the quality of the integrated data.

2. Hands-on Data Integration

A supervisor conducts or delegates every step of the integration process, typically by using code, which is referred to as manual data integration. That entails manually collecting the data, linking its many sources, and cleaning it.

This approach works well, even though it is a highly laborious, manual procedure. But it can quickly become difficult for complicated integrations. Everything is done manually, which requires money and time.

The advantages of this strategy include the following:

  • Lower system costs: It is not necessary to use expensive systems, machinery, or software for manual data integration. Entrepreneurs frequently lack a sizable budget when they first begin to operate. A business can enter the market with a manual management system without having to spend too much money on a robust integration application.
  • Reliability: Since an employee is monitoring each step in a manual integration process, there are fewer chances that mistakes will persist throughout the process. Usually, employees will be able to spot mistakes as they occur.

The drawbacks of this strategy include the following:

  • Slow speeds: Manual data integration is time-consuming. Since machines and computers work more quickly than people do in general, employees have more time to devote to other tasks when they don’t have to spend their time manually integrating data.

3. Integration of middleware data

Middleware is a type of computer program used to link applications and move data between them. Middleware can serve as a bridge between various systems, which is especially helpful when a company is combining obstinate legacy systems with more modern ones.

Among the advantages of this strategy are the following:

  • Better streaming of data: The integration is carried out automatically and consistently by the middleware.
  • Streamlined system access: A network’s legacy and modern systems can connect more easily with middleware.

Among the drawbacks of this strategy are the following:

  • Experience required: A skilled IT employee is needed to deploy and maintain the middleware.
  • Limited capabilities: Certain systems may not be able to integrate with middleware.

 
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4. Data warehousing

In this strategy, data from disparate sources is kept in a data warehouse. Due to this, it has become one of the more common types of data integration since it gives businesses more flexibility in handling data.

Among the advantages of this strategy are the following:

  • Improved storage for analytics: Maintaining data in a data warehouse enables managers and analysts to execute more complex queries without being concerned about overloading transactional databases.
  • Preserved data integrity: Data integrity is better maintained when it is accessed from a single source (a data warehouse) instead of several dissimilar ones.

Among the drawbacks of this strategy are the following:

  • Increased storage costs: You’ll have to pay to store data in a data warehouse, but usually cloud-based storage options are not extremely expensive. The cost may also depend on the amount of data you’re planning on storing.
  • Higher maintenance costs: If the data warehouse is on premises, the server must be set up, managed, and maintained by technical professionals to orchestrate the integration.

5. Integration of uniform access

Using this method, data is accessed from different collections and presented uniformly in a single system, yet the data is stored in its original location.

This is the best course of action for businesses that must access several different systems. This method can produce insights without incurring the expense of making a backup or duplicating the data.

The benefits of this strategy include the following:

  • Less storage capacity is needed: No unique location needs to be configured to store all the data of the organization. This saves money that would be spent on storage.
  • Easy access to data: This method functions effectively with a variety of systems and data sources. Data can be accessed easily using a uniform access system because it presents the data while leaving it in the original sources. The uniform access system’s capacity is not compromised by storing heaps of data.
  • A condensed view of the data: The final user sees the data uniformly, which makes it easier to understand.

The drawbacks of this strategy include the following:

  • System strain: Typically, a high quantity and frequency of data requests in this process may surpass the capacity of some systems.
  • Data integrity issues: Data integrity may be compromised by using so many sources and transforming the data to display it uniformly.

 

Conclusion

For a business that hopes to become more data-driven, data integration is essential, especially if the business utilizes a variety of information systems to conduct business processes. The integration strategy you choose depends on the characteristics of your organization’s data structure and your business needs.

Check out some related resources:

How CIOs Can Turn Their Dark Data Light with Data Integration

Creating apps with no coding in 5 simple steps

How dark data is hurting your business

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