Data integration is the process of combining data from several disparate sources to provide users with a single, unified view. Integration is the act of bringing together smaller components into a single system so that it’s able to function as one. And in an IT context, it’s stitching together different data subsystems to build a more extensive, more comprehensive, and more standardized system between multiple teams, helping to build unified insights for all.

Data integration helps significantly consolidate all types of data, considering its growth, volume, and its varying formats. Combining these to work from one set of data allows businesses to help internal departments see eye-to-eye on strategies and business decisions, and produce actionable and compelling business insights for short- and long-term success. As an integral part of the data pipeline, bringing together integration plus data ingestion, processing, transformation, and storage will help your business aggregate data regardless of type, structure, or volume.

How do you integrate data?

Understanding how data integration works will be crucial in understanding how it benefits your people, processes, and technology. As organizations become more data-driven, achieving a single access point for data storage, access, availability, and quality becomes increasingly tricky. To move data from one system to another, you’ll need to create a defined pathway.

One common type of data integration is a data ingestion, where data from one system is integrated on a timed basis into another system. Another type of data integration refers to a specific set of processes for data warehousing called extract, transform, and load (ETL). ETL consists of three phases:

  • Extracting data from multiple sources and moving it to a staging area.
  • Transforming or converting the data, then reorganizing it into a suitable format for loading into a data warehouse.
  • Loading the transformed data into an analytical data warehouse environment.

Data integration may also include cleansing, sorting, enrichment, and additional processes to make the data ready for use. There are a few different ways to integrate data it all depends on the need, company size, and available resources. In addition to ETL and ELT, some other strategy types are:

  • Data replication
  • Data virtualization
  • Change data capture
  • Streaming data integration

The benefits of data integration

You may not realize it, but data integration is a process many software developments and IT operations (DevOps) teams use. One example of this is how you think about your technology for the future. Constantly thinking of how your team can build, test, and deploy applications is key to a successful DevOps program. From experimentation to tactical operational deployment, you need programs and applications that cater to your audience or you risk losing them to your competitors. Integrating data into your application strategies and gaining insights through the process, helps you stay current and accurate.

Data integration can serve your organization both in the short and long term. Some benefits include:

Better data

Delivering more valuable data, both in integrity and quality.

Better collaboration

Improving collaboration with a seamless knowledge transfer between systems, meaning reduced errors.

Fast connections between data storage

Adding an effective data integration system with seamless connections ensures you’ll always be able to reach your data when you need it.

Increased efficiency and ROI

Because you’re able to access data quickly, you’ll cut down on errors.

Better customer and partner experiences

When you’re able to retain your customers’ wants and needs, you can deliver them to them. For example, in a manufacturing setting, you’d be able to order from vendors when you need to replenish your inventory.

A comprehensive view of your business

This includes a complete picture of business analytics, insights, and intelligence as well as a complete overview of processes and performance.

The challenges of data integration

The explosion of data, data sources, and data structures combined with changes to infrastructure services compute power, analytics tools, and machine learning have transformed how companies integrate data.

One of the biggest challenges you’ll encounter when learning how to integrate data within your current systems is the inherent difficulties in linking a diverse set of systems into one. This can lead to:

Not being able to find your data quickly

When you can’t find what you need, you and your team will end up wasting a lot of time. This affects productivity as you may have groups of data inaccessible to others who also need it or could use insights from the data to build better strategies.

Low-quality or outdated data

Constantly collecting data means you have a lot of it at all times and if there aren’t standards for data entry and maintenance, you could be collecting a lot of inaccurate, outdated, duplicate, and insufficient data. You’ll need an option that helps organize inconsistent data.

Data coupled with other applications

Having data coupled with, and dependent on, other applications especially legacy applications can make it difficult to use elsewhere.

Disparate formats and sources

You’ll inevitably have applications for many different teams, including sales, marketing, customer service, and logistics. As these tools are accessed, organized, and maintained through several teams, data formats might not be consistent through them all. Even something as simple as writing a phone number domestically and internationally could cause your data to be out of alignment.

Your team’s using the wrong software

Even if you’re already using an integration solution, that doesn’t mean you’re using the right type of solution or even the solution itself the right way. Make sure to explore what you’ll need your data integration solution to accomplish and when.

Too much data

Yes, you can have too much data. If you don’t have a plan for when and how you collect data, you could end up with a lot of info you don’t need while burying the info you do.

Data integration tools and technology

There are many data integration techniques available across all levels of your organization from manual to fully automated. Some typical methods include:


As there’s no unified view, all users can access any data they need through all source systems.


Best for small teams, this method requires each application to implement integration.

Middleware data

This method acts as a mediator, normalizing the data to add to the master pool. Middleware can help transfer data from legacy applications when they cannot connect to other newer applications.

Uniform access

Data stays in the source systems with several defined views that offer a unified view to all users.

Common data storage

This method creates a new system that copies data from the primary source while managing additional data outside of the original source.

Data integration tools are software-based tools that ingest, consolidate, transform, and transfer data from its originating source to a destination, performing mappings, and data cleansing.

The tools you add have the potential to simplify your process. But first, you need to identify the attributes that make a good data integration tool. Some of the features you’ll need in your data integration tool are:

  • Easy to learn and use
  • Many pre-built connectors for adaptability
  • Open source for more flexibility
  • Portability
  • Cloud capability for all levels