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What is data integration?

Learn what data integration means, why it's an integral part of your software development and IT processes, and how new data connections impact relationships across tools and teams.

Data integration definition

Data integration is the process for 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 all 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.

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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 of 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 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, 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.

Another alternative is extract, load, transform (ELT), designed to push processing down to the data for improved performance.

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 development 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. By integrating data into your application strategies and gaining insights through the process, this helps you stay current and accurate.

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

With a bird's eye view of the business, your team can strategize how your data integration findings will contribute to your success. But there are a few situations where data integration might run into issues.
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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 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:

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Manual

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

Application-based

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.

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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

Data integration platforms typically include the following tools:

Data catalogs

Helping businesses find and inventory data assets throughout multiple silos.

Data cleansing

Tools that detect and rectify data through replacement, modification, or deletion.

Data connectors

Moving data from one database to another and handling transformations.

Data ingestion

This allows you to gather and import data to use immediately or save for later.

Data governance

Tools that ensure the availability, security, usability, and integrity of data.

Data migration

Moving data between computers, storage systems, or applications.

ETL tool

As previously mentioned, the most common integration method.

Master data management

Helping businesses stick to standard data definitions, classifications, and categories through taxonomy to help establish a single source of truth.

Creating an integration plan

To ensure your integration implementation goes as smoothly as possible, you’ll need to follow these five steps:

Clean your data

Before doing anything, clean up your data. If your data isn’t clean, it isn’t usable. Look at your existing applications and remove duplicates, make sure you don’t have outdated or invalid data, and optimize the channels you collect your data from.

Introduce easy to understand processes

You’ll need company-wide standards for data entry and maintenance. You can assign one team or person the responsibility of keeping the quality and management processes in place. If you can’t choose a person or team, designate processes for everyone to follow to ensure data is kept clean, updated, and organized—and document how your applications are connected for total transparency.

Back up your data

As an additional safety precaution, make sure to back up your data to the cloud or a physical drive. Keeping your transformed information in a data factory helps drive your strategies.

Choose the right software

Automating your data management tasks to sync automatically reduces the need for manual data entry, unifies your data formats, and reduces errors. When choosing your tool, you need to ask yourself:

  • What data needs to be integrated?

  • Which applications need to be integrated?

  • What organizational data flows do you need? Does it need to be a one-way communication or a two-way flow of information?

  • Do you need data to sync in real-time or due to a particular action?

Manage and maintain your data

Clean data is an ongoing process. Having the right tools in place working as they should, with the ability to grow with your business, solidifies your success strategy. Ensuring you have up-to-date and consistent data will give your team better data-driven insights into what your users need.

While data integration began with organizations realizing they would need more than one solution to collate and manage all the data they’d received, we’ve since discovered how to manage the complexities and challenges of linking multiple datasets. Using techniques that consolidate operations and support your business’s technical and analytical needs is at the heart of any successful data integration solution.

With data integration, you’re able to connect software to establish a continuous and effective data flow from end-to-end across your organization, ensuring all key players have access to the data they need, whenever they need it.

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