The ability to create massive amounts of data is mind-blowing. If only the ability to harness insights from this data kept pace with the ability to create it. Now, with exciting advancements in data integration, that gap is narrowing. 

But how is data integration helping companies generate business intelligence? We’ll answer that question by explaining the five types of data integration, listed below, and how cloud computing is impacting this growing field. 

  1. Manual data integration: Data managers must manually conduct all phases of the integration, from retrieval to presentation. 
  2. Middleware data integration: Middleware, a type of software, facilitates communication between legacy systems and updated ones to expedite integration.
  3. Application-based integration: Software applications locate, retrieve, and integrate data by making data from different sources and systems compatible with one another. 
  4. Uniform access integration: A technique that retrieves and uniformly displays data, but leaves it in its original source.
  5. Common storage integration: An approach that retrieves and uniformly displays the data, but also makes a copy of the data and stores it.

What is data integration? 

Data integration is the process of combining data from different sources to help data managers and executives analyze it and make smarter business decisions. This process involves a person or system locating, retrieving, cleaning, and presenting the data. 

Data managers and/or analysts can run queries against this merged data to discover business intelligence insights. With so many potential benefits, businesses need to take the time to align their goals with the right approach. 

To get a better understanding of data integration, let’s dive into the five types (sometimes referred to as approaches or techniques). We’ll discuss the pros and cons of each type and when to use each one. 

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1. Manual data integration

Manual data integration occurs when a data manager oversees all aspects of the integration — usually by writing custom code. That means connecting the different data sources, collecting the data, and cleaning it, etc., without automation. 

Some of the benefits are: 

  • Reduced cost: This technique requires little maintenance and typically only integrates a small number of data sources.
  • Greater freedom: The user has total control over the integration. 

Some of the cons are:

  • Less access: A developer or manager must manually orchestrate each integration.
  • Difficulty scaling: Scaling for larger projects requires manually changing the code for each integration, and that takes time. 
  • Greater room for error: A manager and/or analyst must handle the data at each stage.

This strategy is best for one-time instances, but it quickly becomes untenable for complex or recurring integrations because it is a very tedious, manual process. Everything from data collection, to cleaning, to presentation is done by hand, and those processes take time and resources. 

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2. Middleware data integration

Middleware is software that connects applications and transfers data between them and databases. It’s especially handy when a business is integrating stubborn legacy systems with newer ones, as middleware can act as an interpreter between these systems. 

Some of the benefits are: 

  • Better data streaming: The software conducts the integration automatically and in the same way each time. 
  • Easier access between systems: The software is coded to facilitate communication between the systems in a network. 

Some of the cons are: 

  • Less access: The middleware needs to be deployed and maintained by a developer with technical knowledge. 
  • Limited functionality: Middleware can only work with certain systems.

For businesses integrating legacy systems with more modern systems, middleware is ideal, but it’s mostly a communications tool and has limited capabilities for data analytics.

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3. Application-based integration

In this approach, software applications do all the work. They locate, retrieve, clean, and integrate data from disparate sources. This compatibility makes it easy for data to move from one source to the other. 

Some of the benefits include: 

  • Simplified processes: One application does all the work automatically.
  • Easier information exchange: The application allows systems and departments to transfer information seamlessly. 
  • Fewer resources are used: Because much of the process is automated, managers and/or analysts can pursue other projects. 

Some of the cons include:

  • Limited access: This technique requires special, technical knowledge and a data manager and/or analyst to oversee application deployment and maintenance. 
  • Inconsistent results: The approach is unstandardized and varies from businesses offering this as a service.
  • Complicated setup: Designing the application(s) to work seamlessly across departments requires developers, managers, and/or analysts with technical knowledge. 
  • Difficult data management: Accessing different systems can lead to compromised data integrity

Sometimes this approach is called enterprise application integration, because it’s common in enterprises working in hybrid cloud environments. These businesses need to work with multiple data sources — on-premises and in the cloud. This approach optimizes data and workflows between these environments. 

4. Uniform access integration 

This technique accesses data from even more disparate sets and presents it uniformly. It does this while allowing the data to stay in its original location. 

Some of the advantages are: 

  • Lower storage requirements: There is no need to create a separate place to store data.
  • Easier data access: This approach works well with multiple systems and data sources. 
  • Simplified view of data: This technique creates a uniform appearance of data for the end user. 

 Some of the difficulties are:

  • Data integrity challenges: Accessing so many sources can lead to compromising data integrity
  • Strained systems: Data host systems are not usually designed to handle the amount and frequency of data requests in this process. 

For businesses needing to access multiple, disparate systems, this is an optimal approach. If the data request isn’t too burdensome for the host system, this approach can yield insights without the cost of creating a backup or copy of the data. 

5. Common storage integration (sometimes referred to as data warehousing) 

This approach is similar to uniform access, except it involves creating and storing a copy of the data in a data warehouse. This leads to more versatility in the ways businesses can manipulate data, making it one of the most popular forms of data integration. 

Some of the benefits include: 

  • Reduced burden: The host system isn’t constantly handling data queries. 
  • Increased data version management control: Accessing data from one source, versus multiple disparate sources, leads to better data integrity. 
  • Cleaner data appearance: The stored copy of data allows managers and/or analysts to run numerous queries while maintaining uniformity in the data’s appearance. 
  • Enhanced data analytics: Maintaining a stored copy allows manager and/or analysts to run more sophisticated queries without worrying about compromised data integrity. 

Some of the cons include:

  • Increased storage costs: Creating a copy of the data means finding and paying for a place to store it.
  • Higher maintenance costs: Orchestrating this approach requires technical experts to set up the integration, oversee, and maintain it. 

Common storage is the most sophisticated integration approach. If businesses have the resources, this is almost certainly the best approach, because it allows for the most sophisticated queries. That sophistication can lead to deeper insights. 

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Which data integration strategy is right for your business?

The race to the cloud has left systems scattered in on-premises, hybrid, and cloud-based environments. Data integration is a smart way to connect these disparate systems so businesses can effectively analyze their data. 

Deciding which strategy is right for any business means understanding the complexity of the systems that need to integrate. If all you need is to integrate ony a handful of systems, a manual approach may be sufficient. 

Enterprise businesses, however, will likely need to integrate multiple, disparate systems, which requires a multi-functional integration strategy. 

To give you some guidance, we’ve outlined the best scenario for each approach: 

Data integration approach

When to use it

Manual data integration

Merge data for basic analysis between a small amount of data sources

Middleware data integration

Automate and translate communication between legacy and modernized systems

Application-based integration

Automate and translate communication between systems and allow for more complicated data analysis

Uniform access integration

Automate and translate communication between systems and present the data uniformly to allow for complicated data analysis

Common storage integration

Present the data uniformly, create and store a copy, and perform the most sophisticated data analysis tasks

There are many aspects to consider in your choice of a data integration strategy. Along with the above benefits, consider the following when choosing your data integration strategy: 

  • Create a data governance strategy. Take stock to understand the quality of the data, how you want to analyze it, and make sure the governance strategy aligns with business objectives.
  • Understand which cloud service provider is best for you. With so many providers and platforms, it’s wise to take the time to understand which provider/platform meets the business’ needs now and in the future.
  • Choose a data integration provider carefully. If you’re going to hire a data integration firm, research which ones have the breadth and depth of tools to provide a comprehensive service.
  • Decide which systems to update. Updating every system is the best option, but that’s expensive. Consider which ones are essential to update and which ones aren’t. 

The cloud and the future of data integration 

The breathtaking growth of cloud capabilities will continue to transform businesses in exciting ways. As these advancements march on, data integration strategies will become more complex. 

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We can’t see the future, but we do know that as the relationship between mobile technologies and cloud computing intensifies, managers, analysts, and execs will be less tied to workplaces. They’ll be able to access data, run complicated queries across disparate systems, and retrieve the results in real-time on a hand-held device — anywhere they want. This ability means data integration tools will need to work seamlessly across devices and on different networks. 

Businesses will also start sharing their data. That requires data integration approaches that work not just within a business, but between organizations. The need for this increased access will drive data integration architects to develop even more robust capabilities. And cloud-based platforms will enable this sharing on even larger scales, across businesses, and at ever-increasing speeds. 

Data integration tools 

From manual to common storage, we’ve covered the main types of data integration. Businesses best implement these strategies by adopting data integration tools, but how do you know which tool to use? 

A good integration tool has the following characteristics: 

  • Portability: Movement between on-premises and the cloud is essential. Portability allows organizations to build data integrations once and run them anywhere.
  • Ease of use: Tools should be easy to understand and easy to deploy. 
  • Cloud compatibility: Tools should work seamlessly in a single cloud, multi-cloud, or hybrid cloud environment.

The best tools are compressive and combine the capabilities above. Talend Data Fabric, for example, is a single suite of apps that collects, governs, transforms, and shares data by offering a host of features like self-service apps, pervasive data quality, and smart governance. These services span all data sources from end-to-end so that you can conduct your data integration quickly and comprehensively. 

While some businesses are still producing more data than they can effectively analyze, data integration strategies are helping close that gap. As these strategies become more refined and elaborate, it can be challenging to pick the right one for your business. The stakes, however, have never been higher. 

The right data integration strategy can translate into insights and innovation for years to come. Consider your needs, your goals, and which type of approach matches both, so you make the best decision for your business.