Data Integration: What It Is and How to Achieve It

Successful organizations use data to make big decisions. That's why you'll see successful leaders at those organizations reviewing charts with big data that are transformed into applicable information.

But what's the most effective way to leverage all of your data to come up with the most informative and effective applications possible? Data integration is the first and perhaps most important step in this process.

Data Integration

The scale and reach of data integration within your business depend on how willing you are to automate processes and unify teams. In this article, we'll review how all businesses with varying amounts of data can achieve data integration.

What is data integration?

Data integration is the process of combining data from different sources to transform it into useful and applicable information. It has the power to improve your business strategies and, therefore, positively impact your bottom line.

There are two aspects of data integration to consider:

1. Data and information are not the same.

Call it raw data, big data, or simply data. Without the right process, data are just random elements on a massive scale. Once the data is processed, structured, and presented in the right context, we can talk about information.

2. There must be a data strategy in place before starting the integration process — this goes beyond the technicalities required to combine two sources.

Why is data integration important?

The treasure at the end of this data integration rainbow is that you'll have the ability to make data-driven decisions that lead to scalable and maintainable business growth 📈 ➡️ 🌈💰.

Challenges of Data Integration

If data integration is so important and influential on business success, you may be wondering why more businesses haven't completely adopted the process quite yet. Well, that answer may live within the challenges that result from the process of data integration.

Since there are so many data integration methods, the technical challenges your IT team will come across are unique to each scenario.

However, most problems your team faces are due to the combination of external and internal sources, and the use of cutting-edge or legacy systems.

Additionally, in the past, many businesses relied on intuition when it came to making strategic decisions. These days, businesses should be more focused on making highly data-driven decisions. Intuition is of course still important, but when you have the numbers to prove or disprove a strategy or support a hypothesis, that's a safe bet to take.

The key is knowing when to tap into data integration.

When is data integration necessary?

1. Your data is not available where it should be.
2. Your data is not up-to-date.
3. You have duplicate data.
4. You have conflicting data between systems.
5. Your data is not in the right format.

Now that you know when to focus on data integration, let's talk about how to actually achieve it.

How is data integration achieved?

To understand how data integration is achieved, let's first review the different types of data integration:

1. Propagation takes data from one place and places it in another system.

2. Consolidation brings data from different sources together in a single data warehouse.

3. Federation brings data from different sources together in a single location but doesn't combine that data.

You can think about how data integration is achieved from one of two perspectives: technical and business.

Data Integration From a Technical Perspective

Today, your company has more data than ever, and the complexity of integration increases by the minute.

That's why the mission of IT teams working with integration and iPaaS (integration platforms as a service) is important.

Many businesses follow the ETL (extract, transform, and load) data integration model to combine all of their data.

Within the ETL process are three agents:

1. Source systems (to extract data).
2. Data integration (to transform data).
3. Target database (to load data).

Among the source applications, you can have external applications, data services, enterprise applications, unstructured files, and/or cloud applications.

Cloud applications are also popular because it's easier to create integrations with them through their application programming interface (API). For instance, imagine every cloud app has a locked room with data.

The API is a copy of the key to open that door. You can deliver that copy of the key to an iPaaS or to another application for them to access that data and create software integration.

Data Integration From a Business Strategy Perspective

Regardless of the size of your business, if you've determined you need data integration, considering these steps will help you come up with a strategy:

1. Set well-defined data integration goals.

These goals should be part of a wider business objective. For example, having a complete view of your customers could be a business goal. To achieve it, your integration plan should set a goal to integrate the customer data in your service, sales, and marketing tools.

Defining your goals will also give your IT team clues on which type of data should be integrated and which method to use.

2. Grant data access to your team.

If you need to build in-house data integration software, you'll need a strong IT team.

But next to the IT team members, who else do you need onboard? Make sure you hire and give the right people on your team access to your data and integration strategy.

3. Manage your data securely and lawfully.

Security and data are inseparable. And when integration implies the movement of personal data, especially in the era of GDPR (general data protection regulation), it's important to identify all of the systems in which you store data about a person or company.

4. Choose your data integration method.

When it comes to data integration, enterprises and SMBs face different challenges but all of them will have to choose between a variety of integration methods.

Using third-party integration software, consolidating data, or building in-house integrations are very different when it comes to budget and resources.

So work with your team during the planning stage of your data integration strategy to select the right method.

Data Integration Software

When selecting your business's data integration software, think about the type of platform you need first.

Types of Data Integration Software

Here are some of the most common categories of data integration tools.

1. On-Premise Data Integration

On-premise data integration brings data together from different local and on-premise sources. They can be installed on a local network or private cloud.

2. Cloud-based Data Integration

Cloud-based data integration includes either iPaaS or integration solutions that bring multiple data sources together (whether within private or public clouds, or cloud-based or on-premise systems).

3. API Data Integration

API data integration connects two or more applications via API (application programming interface) to those applications that can share data sources.

3. Proprietary Data Integration Software

Proprietary data integration tools are ideal for large businesses with unique use cases for their data. They're often categorized as enterprise solutions.

4. Open Source Data Integration Software

Open-source data integration software is free and ideal if you and your team want total control of all of your in-house data.

Additionally, you may see data integration tools categorized by the type of business they're meant for:

5. Enterprise Data Integration (Enterprise Data Integration Platform)

Enterprise data integration tools are those meant for big companies with big data. Tools within this category are tailored towards businesses with an array of resources and an established IT team that may be building and using on-premise data integration and systems.

6. SMB Data Integration

SMB data integration tools are those that are ideal for small to mid-sized businesses. Meaning, their features, pricing, setup, and installation are tailored to businesses that may not have the time, resources, or amount of employees an enterprise business has.

Data Integration Tools Your Business Needs

Now let's look at some popular data integration tools.

1. HubSpot Operations Hub

Best data integration tool for: Data integration and sync that's powered by your CRM data for all scaling businesses.

HubSpot's Operations Hub allows any scaling business to sync customer data and automate businesses processes to keep teams aligned on a single source of truth when it comes to your customer data.

It syncs apps and data without code — you can use bidirectional sync, custom field mapping, filtering, and historical syncing, too.

These features — along with the fact that Operations Hub is powered by the HubSpot CRM (plus with programmable automation and data quality tools) — mean your team will have one view of every customer interaction as well as a unified understanding of the customer experience.

HubSpot offers over 600 integrations that are powered by Data Sync — meaning, your team will have two-way sync of your data in real-time to ensure your databases are aligned (for new and historical data).

As a result, you'll have the information needed to create customized experiences and personalized filters and field mappings for customers.

Pro Tip: Use Operations Hub to sync customer data, automate business processes, and align your team with clean, connected customer data powered by your CRM.

2. Dell Boomi

Best data integration for: Integrating on-premises and cloud applications as well as different data sources and devices to break down data silos.

Source

Dell's Bloomi data integration platform is a cloud-based, iPaaS, and API solution for connecting all of your applications and data. It offers data integration and data quality management and connects both SaaS and on-premises apps.

Use Bloomi's drag-and-drop, low-code editor for intelligent integration mapping and configuration as well as error testing and resolution to avoid data and app silos. Connect your systems, applications, and people to bring all of your data together and sync and enrich it to help your team offer delightful customer experiences.

Bloomi will provide your team with recommendations that can simplify and expedite data integration through operational intelligence, business logic, and data flow — as a result, your team will be able to increase productivity and spend more time focused on your customers.

3. Segment

Best data integration for: SMBs that want to integrate all of their data via a CDP that also collects, cleans, and, and routes data to any system.

Segment is a customer data platform (CDP) that allows your team can collect, clean, organize, and route all of your customer data into any system. The tool pulls data from your web and mobile apps to give you a single and accurate view of your customer.

There are real-time data flows that tell you what your users are doing with your product. Additionally, with Segment, your engineering team only has to use one API to collect analytics data across any platform.

Segment connects to over 300 tools so your team can get a deep understanding of your customer across all of their experiences (app, sales, support, payment, etc.).

4. Talend

Best data integration tool for: Cloud integration with the option for open-source, ETL data integration.

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Talend is a cloud data integration software that offers multiple data integration solutions. Use the drag-and-drop editor to create data pipelines without the need for any code and feel confident that the tool is maintaining high-quality data throughout the integration process.

Talend integrates your data no matter the source — whether in cloud or on-premises — by using over 1,000 connectors. Talend also partners with a number of data warehouses, cloud service providers, and analytics platforms (e.g. Amazon Web Services, Microsoft Azure, Google Cloud Platform, etc.) to make large data set management easy.

One of Talend's more specific data integration solutions is their open-source, ETL integration platform, Open Studio. If your system meets Open Studio's requirements, then your team can create basic: data pipelines, ETL, and data integration tasks.

5. Informatica

Best data integration for: AI-powered and cloud-based data management for enterprises.

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Informatica is a cloud-based data management solution for enterprise businesses. It makes it easy to connect on-premises applications to the cloud and use APIs. Manage and integrate your data on any platform or cloud for any user in multi-cloud and multi-hybrid environments.

Informatica partners with some of the most popular cloud technology platforms including Microsoft Azure, Snowflake, and Google Cloud to help you maximize your data and cloud solutions.

Grow Better With Data Integration

Regardless of business size or available resources, accurate and effective data processing and management enrich your view of the customer experience and your business's overall health.

The process enables you to roll out informed and timely business strategies to improve performance. Begin using data integration to empower your business to make data-driven decisions.

Editor's note: This post was originally published in June 2020 and has been updated for comprehensiveness.