How to ensure data integration?  

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Artificial intelligence opens up enormous opportunities for companies but also brings new challenges. For AI to function properly, it needs access to information, which is often inconsistent or comes from various sources. Therefore, data integration may be essential.  


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Think of your company’s data as puzzle pieces. Information from each department or system represents separate sets of elements. AI needs all of them to create a complete picture. The problem is that the pieces often don’t fit together, come in different formats, or some are missing. That’s why a process is needed to collect these scattered elements and ensure they fit together. This process is called data integration.  


What is data integration?  


Data integration is the process of bringing information together into a single domain and making it accessible through a unified interface. Information from different sources— databases, analytics tools, CRM systems, spreadsheets — must be able to “communicate” with each other.  

Thanks to data integrity, AI can analyze information, work out relationships, and draw conclusions. Without integration, AI only sees fragments of the puzzle. That’s why integration is the foundational to working with data—it enables AI to provide valuable insights and support you in making informed business decisions.  

Data integration is just one piece of the puzzle. Learn how to prepare your data for the AI era in the article: How to prepare data for the AI era? . 


What are the most common data integration techniques?  


Data integration is a complex process that requires the right techniques depending on the nature of the data and the organization’s needs. Here are the most commonly used methods:  

Integration based on publicly available data  

When data is publicly available and we have no control over changes to it, integration involves retrieving and processing it. In such cases, implementing an early warning system for potential changes is crucial to avoid errors, ensure accurate data analysis, and maintain workflow continuity.  

ETL  

ETL (Extract, Transform, Load) is a traditional and widely used technique. It consists of three steps:  

  1. Extracting data from various sources  
  1. Transforming data into a uniform format  
  1. Loading the data into a target database or data warehouse  

ETL is particularly useful for integrating large volumes of data and building data warehouses.


ELT  

In the more modern ELT (Extract, Load, Transform) approach, data is immediately loaded and then transformed within the target system, usually in a data warehouse. This method is more suitable for large datasets where timeliness is crucial, as loading is often faster.  
 

API  

APIs (Application Programming Interfaces) enable systems to communicate and exchange data in real time. APIs allow applications to “talk” to each other and fetch or send data without direct access to databases. APIs are particularly popular for integrating web and mobile applications.  

Data virtualization  

This technique involves creating a virtual data access layer that connects different data sources into a single logical view. The data remains in its original locations while users can access it through the virtual layer. Data virtualization is useful when physical data movement is not desired, but unified access is needed.  

What is system integration?  


Data integration and system integration are two different concepts. Data integration focuses on merging multiple data sources to create a complementary dataset that describes a specific domain. System integration, on the other hand, is about automating processes that span multiple systems.  

If one part of a process is executed in one system and another part in a different system, and we want an action in one to automatically trigger a response in the other, the systems must be able to communicate. This ability to communicate between two systems is called system integration.  

One of the simpler methods of system integration is file-based integration. This involves collecting data, exporting it from one system into a file (e.g., CSV, XML), and importing that file into another system. This technique is often used in smaller companies or when real-time integration is not required.  

Examples of integration  

Having a single, consistent version of data available to the entire organization is essential for its effective use and management. How does this work in practice?  

Data integration  

Job listings repository  

We are currently designing a solution for a job listing portal. Instead of manually browsing hundreds of websites, our system scans the web and collects job postings into a central repository. We then provide an API that allows our clients to retrieve selected job listings in a standardized format. This is an example of automated integration, where systems communicate with each other without human intervention and exchange data in real time. 

System integration  

Payments in a sports club  

Efficient payment management is crucial in sports clubs. From the online banking system, we generate a file containing information about members’ payments. This file is then imported into the payment management system. As a result, club administration has a continuous overview of who has made a payment and who has not. This is a classic example of data integration using file exchange, enabling system communication.  

Data exchange between teams  

In one of our projects, we encountered a challenge related to the collaboration between two teams: budget planning and construction planning. These two teams used different systems, making data flow and coordination difficult. To improve their cooperation, we implemented advanced data integration.  

Integrating dispersed systems always comes with higher requirements. Establishing a common “language,” or data exchange contract, is more challenging.  


Challenges and practices for data integration  

The data integration process is not always straightforward. Companies often face multiple challenges that complicate the process.  

One of the main problems is the variety of systems and software used within an organization. These systems are often incompatible or have incomplete interfaces, preventing smooth data exchange. As a result, data is processed in intermediate steps, often manually, such as in Excel spreadsheets. Manual data integration approach is time-consuming and error -prone.   

Another challenge is creating a centralized solution that reduces manual work related to data management, visualization, and reporting, while remaining flexible enough to adapt to changing business needs. How can this be achieved?  

  1. Strategic Approach – Designing and implementing a high-quality data management solution requires vision, experience, and expertise. Data integration is not just a technical issue but also a strategic one that requires understanding business needs.  
  1. Flexible Technology – The technology must be flexible enough to adapt to company processes without causing excessive disruption. It is essential to avoid overly complex solutions that require drastic changes to existing systems.  
  1. User-Friendly Interface – The solution should be easy to use for end users. An intuitive interface and simple data integration processes minimize unnecessary user activity, increase work efficiency, and reduce the risks of error. 


Conclusion  


Integrating data is essential for AI to function effectively in a company. It eliminates data silos and combines scattered information into a cohesive whole. Various techniques enable this, from file exchange to API-based communication—the choice of method depends on the data specifics, business processes, and organizational needs. However, companies must also tackle challenges such as system incompatibility.  

It’s important to remember that data integration is a process—it requires careful planning, selecting the right technologies, data integration tools, and consideration of the organization’s specifics.


Would you like to improve:  

  • data quality,  
  • data integration strategy, 
  • data security  

in your organization? Schedule a meeting with Jacek!   



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