Operating as a successful data driven-company requires strong data integration tactics. Organizations often have a plethora of data which is housed in disparate applications, platforms, and other sources. The multiple sources impede visibility and make it difficult to get a comprehensive view of an organization’s performance and potential. 

Data integration methods can help increase visibility and provide a competitive-edge. They are most powerful when combined with a data integration strategy that 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.

What is Data Integration?

With data integration, organizations use technical and business processes to bring data from different sources together into one, unified view. Information often comes from distinct databases, platforms, and applications. Unifying these data sets provides an up-to-date accurate set of information that can be used to inform business analysis and drive more informed decision-making.

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.

Types of Data Integration: Techniques & Approaches

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. 

7 data integration methods

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. To help make things more accessible, we are sharing some of the top integration methods organizations can leverage. The following five approaches can be taken to create a comprehensive, unified view of your data. 

1. Integration based on applications

The integration based on applications method requires the use of software programs. In this method, the programs handle every task in the data integration process. They identify, retrieve, clean, and combine data from many sources. Automation makes it simple to transfer data between sources.

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.

The following are pros of the integration based on applications strategy:

• 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.
• Adjustable: As new applications are introduced, the organization can scale to a growing data integration framework. 
• Can be performed by a nontechnical employee: This method does not require a highly technical skillset to perform, making it more accessible to a variety of employees. 
• Customer oriented: This strategy is often used by organizations seeking a unified view of customer data as it can integrate Customer Relationship Management (CRM) systems with other applications easily. 

As with all data integration tactics, this method may have the following cons:

• 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.
• Complex Maintenance: Similarly, there needs to be cross-checks post-integration. These systems will require regular maintenance, especially as change in one system in the network may require additional adjustments to the overall integration framework. 

2. Hands-on Data Integration

The next data integration involves a hands-on approach. Through this method, a supervisor conducts or delegates each step of the integration process. They will typically use code referred to as manual data integration to do so. It involves manually collecting the data, linking its many sources, and cleaning it.

This approach is a manual one and highly laborious but delivers results. It’s best suited to simple integrations as it can grow complex and time-consuming for more involved operations. Here are some reasons you might select a hands-on data integration approach: 

• 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. The low initial cost can be an attractive advantage to organizations with budget considerations. 
• 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. Users can verify and update data as needed. 
• Flexibility: This method is a flexible one as the business can adjust and tailor the process without having to comply with the requirements of an automated approach. 

The drawbacks of this strategy include the following:

• Slow speed: 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.
• Lacking scalability: There’s only so much work the supervisor in charge of the manual data integration process can do at one time. Automated tools can manage larger amounts of data making it a more practical option for organizations with sizable data sets. 

• Lack of Real-Time Updates: It is difficult to update and integrate data in real life through a manual process, making it a poor choice for organizations that need instantaneous updates to data.  Manual integrations typically work in batches. 

3. Integration of middleware data

This approach to data integration leverages middleware software,  a type of computer program used to link applications and move data between them.It can serve as a bridge between various systems, which is especially helpful when a company is combining obstinate legacy systems with more modern ones. It can deliver a comprehensive view of data across the enterprise. 

Among the advantages of this strategy are the following:

• Better streaming of data: The integration is carried out automatically and consistently by the middleware. This allows for the automation and optimization of business processes to improve operational efficiency.
• Real-time Updates: By integrating middleware data, organizations can get real-time access to critical information. This fosters smart decision-making and improves response time to changes in the business landscape. 
• Streamlined system access: A network’s legacy and modern systems can connect more easily with middleware.
• Enhanced Interoperability: This method improves interoperability between different applications and systems. It helps to enhance the exchange of data, increase efficiency, and reduce silos.

Here are some of the reasons you might not like a middleware data integration approach:

• Experience required: A skilled IT employee is needed to deploy and maintain the middleware. This requirement could be a disadvantage to resource-limited organizations. 
• Limited capabilities: Certain systems may not be able to integrate with middleware.
• Costs: Organizations may need to invest in software, hardware, consultants, or employees to perform this method. There is also the potential for downtime of systems, which can have an impact on an organization’s bottom line. 

4. Data warehousing

In this strategy, data from disparate sources is kept in a data warehouse.A centralized repository is used to store data in an optimized format. This approach has become one of the more common types of data integration as 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.
• Enhanced reporting: Business intelligence tools can be used to perform reporting and analysis for interpretations and findings. 

It also has the following drawbacks:

• 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. Money that would have been spent on storage can be saved. 
• 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 and interpret. 
• Reduced costs: Organizations do not need to pay for separate data storage and can access data without incurring costs related to data backup or duplication. 

Here are some cons to keep in mind as well:

• 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.
• Performance woes: Systems will need to be suited for regular, simultaneous access or organizations may encounter downtime and slower response times. The stability of the source systems is vital. 

• Implementation complexity: This tactic involves a more complex implementation and maintenance process, which may not be ideal for companies that do not have the required bandwidth and expertise.

6. ETL for data integration

ETL stands for Extract, Transform, and Load. This data integration process extracts data from various sources (e.g., databases, APIs, flat files) and converts it into a consistent format.  The data is then loaded into a data warehouse or database. Once the data is loaded into the target system it can be used for analysis and reporting as well as to inform decision-making.

ETL for data integration provides the following advantages:

• Centralization: Data is moved from disparate sources into one centralized repository for increased visibility and improved access. 

• Data quality enhancement: The data is converted to a consistent format through cleansing, standardization, and enrichment. 

• Automation: This process removes manual data integration from the process through automation, reducing the risk for errors and repetitive tasks. 
• Better decision-making: With a centralized repository, stakeholders can lean on data to drive strategic decision-making. 

ETL also has some cons, including:

• Expense: Particularly at a large scale, implementing an ETL data integration process can be expensive in terms of hardware, software,and resources.  

• Potential for data loss: It’s possible to lose data due to errors and improper handling during the extraction, transformation, and loading steps. 

• Complexity: Some organizations may not have the internal resources needed to manage the associated complex processes. 

7. API for data integration

Similar to ETL, API-based data integration involves extracting data from different sources, transforming it as needed , and loading it into a target system. With APIs, data can be extracted from sources including databases, web services, and cloud platforms. Once transformed, the data is loaded into a storage system, database or warehouse where it can be analyzed, reported on, and leveraged for insights. 

API-based data integration offers several advantages:

• Real-time Accessibility: Organizations can lean on the most up-to-date information thanks to APIs enabling real-time data access.
• Flexibility: APIs provide a flexible way to integrate data from a wide range of sources, including cloud applications, third-party services, and internal systems.
• Cost-Effective: APIs do not required a costly, complicated infrastructure as with ETL so they are often a more cost-effective option for organizations.  
• Scalability: API-based integration can scale easily with growing data volumes.

API-based data integration also has some potential drawbacks:

• Dependency on APIs: When using external APIs, it’s important to ensure they are available and stable. Downtime can affect data integration processes. 
• Security: To minimize security risks, be sure to have strong authentication and authorization mechanisms especially when transferring sensitive data between systems.  
• Data Consistency: Ensuring consistency and quality of data retrieved through APIs can be challenging. Data formats and structures may vary across different sources.

Conclusion

For businesses that plan to  become more data-driven, data integration is essential, especially when using 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. Ultimately, it is a matter of weighing the pros and cons to select the tactic that will deliver the greatest results for your goals.