What is Data Integration? 

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What is Data Integration? 
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Data is an incredibly valuable asset for any business. When properly leveraged, data can provide an array of insights that help organizations make strategic, informed decisions that drive business growth. However, most businesses have data scattered across different systems and lack a centralized approach to data management. As a result, they’re not harnessing the full potential of their data.  

This is where data integration comes in. As one of the most important aspects of the data management process, data integration involves bringing together data from disparate sources so it can be properly accessed, analyzed, and leveraged by organizations. Understanding how data integration works is the first step towards unleashing the full power of your data assets.  

In this article, we explain what data integration is, why it’s important, and the benefits data integration provides. We also discuss common data integration methods and tools and explore some best practices to help you make the most of this process.  

Definition of data integration

Data integration is the process of combining data from different systems and sources to provide users with a single, unified view. It plays a crucial role in the data pipeline and encompasses data ingestion, data processing, and transformation to combine different types of data into a single format before storing it in a target repository for easy retrieval. The ultimate goal of integration efforts is to allow organizations to gain valuable information that can be used to gain meaningful insights for business intelligence.  

The importance of data integration

Businesses spend a lot of time and money collecting data, however this data is often siloed and stored across departments in different formats. This means that business decision makers don’t have access to the full picture of what’s happening in the organization. Data integration aims to resolve this issue by consolidating data across the organization so that decision makers have access to all the information they need. 

Let’s look at an example of data integration in practice. Without integration, producing a report for analytics could involve logging into multiple accounts, on several websites or apps, copying over and reformatting data, and data cleansing. All of these tasks are done before analysis can even begin. With data integration, this information is automatically pulled from multiple data sources and available for easy access in a single site or data integration platform.  

Benefits of data integration 

Data integration goes beyond just combining data from multiple sources. It offers numerous advantages that allow organizations to unlock the full potential of their data, foster collaboration, improve data quality, and enhance their competitiveness in the market. Below, we discuss a few benefits of data integration that highlight how this process can transform your business and drive growth.  

Save time and money 

When implemented correctly, data integration significantly reduces the time needed to prepare data for analysis. Automatically consolidating data from various sources allows businesses to streamline their processes and eliminate the need for employees to manually gather data. 

This not only saves valuable time but also reduces the costs associated with manual data handling. Manual processes can be time-consuming, costly, and run the risk of human error. Automating data integration can therefore free up resources that can be dedicated to more strategic tasks, such as data analysis.  

Increase collaboration

By breaking down data silos, effective data integration helps foster collaboration within an organization. Below are two examples:  

  • Unified data access: Combining data from disparate sources into a centralized repository allows employees from different teams to access data through a consistent, unified view. When everyone is accessing the same data source, instead of disparate ones, it becomes easier to collaborate and make decisions based on shared information.  
  • Cross-functional insights: Data integration allows teams with diverse functions, such as marketing, sales, finance, and operations, to access and analyze the same unified view of data. This cross-functional view promotes a better understanding of how each department’s actions and decisions impact the organization as a whole. This in turn can foster cross-team collaboration on strategic initiatives. 

Gain holistic insights for better decision-making 

When an organization has merged a number of data sources together in one place, they are able to unlock holistic insights that can drive more informed and strategic decision-making.  

By integrating existing databases with external sources, such as social media platforms and data from business-related sites, a company can draw connections between different data points and spot emerging trends and new opportunities for growth. In turn, this can help fuel new revenue streams. 

Improve data quality 

Data integration often includes cleansing, transforming, validating, and standardizing data, eliminating silos, enforcing data governance, and offering error-handling and logging mechanisms. This approach ensures that integrated data is clean, accurate, and consistent throughout its entire lifecycle, reducing the chance of discrepancies, errors and duplicate records. As a result, organizations can count on high-quality data that’s consistent and reliable.  

Improved data quality can significantly improve company operations and even has an impact on revenue. Let’s use the sales management process as an example. When data from different providers is dispersed and fragmented, it’s difficult to maintain quality and reliability. As a result, companies don’t have full control over the flow of money and settlements with contractors, and can’t assess revenue lost until the next audit.  

One of our clients, an industry giant in animal pharma, was dealing with this issue. We introduced service & system stack solutions for all aspects of their customer and sales transaction processing, including data quality gatekeeping, sales integration, and error correction. The results didn’t just include higher quality data and an improved sales process, but also a significant reduction in administrative costs.  

Data quality and integrity is therefore essential and, without it, revenue can be jeopardized.

Increase competitiveness 

This is perhaps a summary of all the benefits outlined above, but in its essence, data integration allows a company to enhance its overall competitiveness by unlocking the hidden value of its data. With a proper use of data integration, businesses can discover insights and opportunities, identify issues and trends, and make strategic decisions that drive growth while also enhancing customer relationships and collaboration with stakeholders. 

Methods of data integration 

There are several methods that each offer unique ways to integrate data. Below we discuss five methods: manual data integration, middleware solutions, application-based approaches, uniform access integration, and common storage solutions.  

Manual data integration 

This method requires a dedicated data engineer to manually write custom code that moves and manipulates data based on the company’s needs. An advantage of manual data integration is that it offers more freedom and control over data integration and management. However, because there is no automation with this process, it can be time-consuming and difficult to scale as an organization gathers more data. As data must be handled at each stage, there is also an increased risk of human error.  

Middleware data integration 

Middleware is a type of software that connects different applications and databases together to exchange data. It’s useful for businesses transitioning from older legacy systems to more modern ones, and can transform data to be compatible across different applications. The advantages of middleware is that it allows different systems to communicate better and can automatically transform and transfer data on a consistent basis. On the other hand, middleware is not compatible with all systems and sometimes requires a skilled developer to install and maintain the software. It also has limited capabilities for data analytics. 

Application-based integration 

This method uses an application or software system to manage the entire data integration process, including locating, retrieving, cleaning, and consolidating data from different sources. It’s arguably the easiest way to seamlessly transfer data from one source to the other. There are many benefits to using data integration applications. Firstly, the entire process is simplified. Secondly, automation allows your team to free up resources and dedicate their time to other tasks. This method is also easily scalable with the amount of data you consume. On the other hand, setup may be complicated and a dedicated data manager may be required to oversee the deployment and maintenance of applications.  

Uniform access integration 

This technique aims to present data in a consistent and standardized way while allowing data to stay in its original location. Because data remains in its original location, this method has lower storage requirements and can work well with multiple systems and data sources. It also allows for real-time or near real-time data access.  However, it can pose challenges to data integrity and overburden data host systems.  

Common storage integration 

Also known as data warehousing, this is the most sophisticated approach to data integration. Common storage integration involves consolidating data from multiple sources into a central storage system, such as a data warehouse or data lake.  

Data lakes allow organizations to store huge amounts of variously structured and unstructured data in their native form. This data can be pulled from different sources and accessed by many users. Using data lakes allows for democratized access to data, ensures transparency, and allows the entire organization to use one authoritative data source that’s fast and agile.  

Regardless of which storage system you use, there are many benefits to common storage integration, including the ability to run more sophisticated queries to gain deeper insights, easier backup and recovery, and better data integrity. On the other hand, common storage integration is resource-intensive and can be complex to set up and maintain. It’s also more costly, however it offers the best approach to data integration.  

Types of data integration tools  

There are four main types of data integration tools. These tools offer numerous features, including data masking, data quality, data virtualization, management, reporting, big data, and more. Depending on your organization’s size, resources, and requirements, different tools may be more appropriate for your data integration needs. Below, we briefly discuss each of these tools to help you understand how they work and which may be more beneficial for your business goals.  

On-premise data integration tools 

These tools are installed and run within an organization’s infrastructure with optimized native connectors for batch loading from several data sources. They’re ideal for integrating data from different on-premise data sources. On-premise data integration tools provide full control over the data integration process but can require a substantial investment in the right hardware and software.  

Cloud-based data integration tools 

Also known as integration platforms as a service or iPaaS, cloud-based data integration tools move data to a remote cloud-based data warehouse. They’re ideal for scalability and accessibility and, since they’re managed by a third party, can reduce maintenance overheads. Cloud-based data integration tools are good for businesses prioritizing flexibility and looking for a low-cost solution. SnapLogic and Dell Boomi are two examples of iPaaS.  

Open-source data integration tools 

These tools are customizable and allow users to modify source code for a higher degree of flexibility and adaptability. Open-source data integration tools are low-cost when compared to proprietary or enterprise software solutions but allow complete control of data integration in-house. Some open-source tools include Talend and Pentaho Data Integration (PDI).  

Proprietary data integration tools 

Proprietary data integration tools are the most expensive but are purpose-built with specialized features and comprehensive support. These data integration solutions are extremely reliable and can cater to very specific business use cases, making them extremely convenient. Zapier is an example of a proprietary integration tool. 

Data integration best practices  

If you’ve reached this point in the article, you’re likely aware that data integration should be the first step to feed your systems and let them work for you. Feed these systems with top quality data and you’ll get top quality results.  

There are plenty of tools out there on the market that require no IT intervention and provide quick, automated, and reliable ways for your organization to maintain tight control over your data. But in order for your data integration process to be as successful as possible, and for you to make the most of your raw data assets, it’s important you adhere to a set of best practices.  

We’ve outlined a few data integration best practices to help your company receive the full benefits that data integration can provide, based on 20 years of building IT systems to support the operations of big industry players.   

1. Define your goals and objectives 

Before choosing a data integration solution or platform, you should have some clearly defined objectives for the project. Take some time to consider which short and long-term business goals you hope to achieve with data integration. Do you want to optimize operations? Reduce customer churn? Increase efficiency?  

Once you’ve defined your goals and have a clear vision of what you want to achieve with data integration, think about how you’ll measure your success. Having KPIs in place will help you monitor and evaluate your data integration performance and ensure that it aligns with your overall business objectives.  

2. Make it scalable 

It’s essential that you design integration solutions with scalability in mind. As your business needs evolve and data volumes increase, you must ensure your integration infrastructure can handle the extra load and adapt seamlessly. Make sure your data integration system is capable of managing increased data loads, new data sources, and additional users without compromising performance or requiring constant modifications.  

3. Prioritize security 

Security is a critical aspect of data integration and preventing data breaches should be a top priority for any organization. There are several robust security measures you can take to maintain data security during integration, such as encryption, access controls, and authentication protocols. These not only ensure you comply with data protection regulations but also help maintain trust.  

4. Keep it simple 

Simplicity is often key to successful data integration. Overly complex integration processes can be confusing and lead to errors and maintenance challenges. Strive for a straightforward data integration architecture that’s easily maintainable and minimizes complexity without sacrificing functionality. The idea is to allow non-tech savvy users to quickly get started and manage data integration workflows with minimal assistance from IT.  

5. Learn from experts 

Data integration is continuously evolving and it’s important to stay informed about industry best practices by learning from experts in the field. Consider reading blogs, articles, and whitepapers or attending workshops and conferences to continue expanding on your knowledge and skills of data integration. Collaborating with experts in the field can also help you discover new ways to enhance your organization’s data integration process and improve outcomes. 

At C&F, our experts have curated a library of articles, case studies, reports, and recorded webinars that contain our best data insights and advice. You can access these resources anytime to enhance your understanding of the ever-evolving data industry. 

Unleash the full power of your data 

By now, you will have learned how data integration is more than just a technical process. It’s a strategic approach that breaks down silos in your business, improves collaboration, enhances the quality of your data, and boosts your organization’s competitiveness in the market. 

If you’d like to see how your business can harness the power of its data, get in touch with our team. With more than 20 years experience working alongside the world’s largest companies, we can help you discover the right data integration solutions to transform your organization.  

"Data integration is the way to get the most out of your data, foster collaboration, improve data quality, and drive business growth. It transforms fragmented data into a unified asset that enables your organization to make informed decisions, innovate, and compete effectively in the data-driven business world."

Michał Osuch Head of Data Management
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