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Data Integration 101: Definition, Techniques, Benefits, and Best Practices
What is data integration?
Imagine trying to run a business without knowing what’s happening across your teams, systems, or customers. That’s the reality for organizations drowning in fragmented data. Customer details sit in one system, financial metrics live in another, and operational insights? Scattered across spreadsheets and third-party tools. Without a way to connect the dots, your data becomes noise—distracting rather than empowering.
This is where data integration takes center stage. It holds your data ecosystem together. Done right, it transforms chaos into clarity, giving you a single, reliable source of truth. And in a world where every decision hinges on accurate, timely data, the importance of combining data sources cannot be overstated.
In this blog, we’ll break down what data integration is, how it works, its benefits and use cases, as well as all the different techniques and technologies used to integrate data in today’s AI-driven business landscape.
Data integration definition
Data integration is the process of combining data from multiple sources to provide organizations with a unified view for enhanced insights, informed decision-making, and a cohesive understanding of their business operations.
The data integration process
Data integration is a core component of the broader data management process, serving as the backbone for almost all data-driven initiatives. It empowers businesses to remain competitive and innovative in an increasingly data-centric landscape by streamlining data analytics, business intelligence (BI), and, eventually, decision-making.
The ultimate goal of integrating data is to support organizations in their data-driven initiatives by breaking down data silos and providing access to the most up-to-date data.
With the widespread availability of modern data integration tools, unifying data is not longer a technical endeavor. Instead, it transcends the domain of IT and serves as the foundation that empowers business users, also called citizen integrators, to take charge of their own data projects.
Data ingestion vs. data integration vs. application integration
Both data ingestion and data integration are essential processes in data management. However, they serve different purposes. While data ingestion focuses on bringing data into a storage or processing environment, data integration goes beyond and unifies, transforms, and prepares data for analysis and decision-making.
Application integration is another concept that’s frequently used in this space. Compared to data integration, application integration focuses on enabling software applications to work together by sharing data.
How does data integration work?
As far as the integration process is concerned, it can be orchestrated to run in real time, in batches, or continuously via streaming.
Generally, though, the data integration process involves the following key steps:
- Identifying data sources
The first step is to consider where your data is coming from and what you want to achieve with it. This means you’ll need to identify the data sources you need to integrate data from and the type of data they contain. For example, depending on your organization and its requirements, these could include multiple databases, spreadsheets, cloud services, APIs, etc.
- Data extraction
Once you have your sources in mind, you’ll need to pull data from each source and move it to a staging area. Modern organizations use AI-powered tools to automate the data extraction process.
- Data mapping
Data mapping involves defining how data from different sources correspond to each other. More specifically, it is the process of matching fields from one source to fields in another. AI data mapping tools automate this step as they provide intuitive, drag-and-drop UI, ensuring that citizen integrators can easily map data and build data pipelines.
- Data quality improvement
When consolidating data, you’ll find it often comes with errors, duplicates, or missing values. Managing data quality at this stage will ensure that only healthy data populates your destination systems. It involves checking data for incompleteness, inaccuracies, and other issues and resolving them using automated data quality tools.
- Data transformation
You may have data in various formats, structures, or even languages when your data sources are disparate. You’ll need to transform and standardize this data so that it’s consistent and meets the requirements of your target system or database. Organizations use specialized tools to transform data since the process is tedious if done manually. The data transformation process typically includes applying tree joins and filters, merging data sets, normalizing/de-normalizing data, etc.
- Data loading
The next step is all about loading data into a central repository, such as a database or a data warehouse hosted in the cloud. Loading only healthy data into this central storage system guarantees accurate analysis, which in turn improves business decision-making. Apart from data being accurate, it’s also important that data be available as soon as possible. Today, organizations frequently employ cloud-based data warehouses or data lakes to benefit from the cloud’s uncapped performance, flexibility, and scalability.
- Analysis
Once your data is integrated, it’s ready for consumption. Depending on your requirements, you may need to use a combination of various tools like BI software, reporting tools, or data analytics platforms to access and present the integrated data.
The data integration process does not stop here, the insights gained might prompt adjustments in your overall data integration strategy.
Benefits of data integration
Besides providing a unified view of the entire organization’s data, data integration benefits them in multiple ways.
Enhanced decision-making
Data integration eliminates the need for time-consuming data reconciliation and ensures that everyone within the organization works with consistent, up-to-date information. With information silos out of the way and an SSOT at their disposal, the C-level executives can swiftly analyze trends and identify opportunities. Consequently, they make more informed decisions, that too at a much faster rate.
Cost savings
Cost savings are an undeniable benefit of data integration. The initial investment in data integration technologies is outweighed by the long-term savings and increased profitability it leads to. Data integration streamlines processes, reducing duplication of efforts and errors caused by disparate data sources. This way, your organization will be better positioned to allocate and use its resources efficiently, resulting in lower operational expenses.
For example, a retail company not only gains real-time visibility into its inventory by integrating its sales data into a single database but also reduces inventory carrying costs.
Better data quality
The fact that data goes through rigorous cleansing steps, such as profiling and validation, applying data quality rules, fixing missing values, etc., means you can make critical business decisions with higher levels of confidence.
Improved operational efficiency
With disparate data sources merged into a single coherent system, tasks that once required hours of manual labor can now be automated. This not only saves time but also reduces the risk of errors that otherwise bottleneck the data pipeline. As a result, your team can focus on more strategic endeavors while data integration streamlines routine processes.
Enhanced data security
It is much easier to secure data that’s consolidated in one place compared to safeguarding several storage locations. Therefore, security is another aspect greatly benefits organizations. Modern data integration software enable you to secure company-wide data in various ways, such as applying access controls, using advanced encryption and authentication methods, etc.
Data integration techniques
Data integration techniques refer to the different ways of unifying data. Depending on your business requirements, you may have to use a combination of two or more data integration approaches. These include:
Extract, transform, load (ETL)
Extract, transform, and load (ETL) has long been the standard way of integrating data. This data integration strategy involves extracting data from multiple sources, transforming the data sets into a consistent format, and loading them into the target system. Organizations use automated ETL tools to simplify and accelerate data integration tasks.
Extract, load, transform (ELT)
Similar to ETL, data extraction is the first step in the ELT (extract, load, and transform) process. It’s a fairly recent data integration technique. However, instead of transforming the data before loading it, the data is directly loaded into the data warehouse as soon as it’s extracted. The transformation takes place inside the data warehouse, utilizing its processing power.
Change data capture (CDC)
Change data capture is a way to integrate data by identifying and capturing only the changes made to a database. It enables real-time or near-real-time updates to be efficiently and selectively replicated across systems, ensuring that downstream applications stay synchronized with the latest changes in the source data.
Enterprise data integration
When it comes to integrating data across an organization, it doesn’t get any broader than this. Enterprise data integration is a holistic strategy that provides a unified view of data to improve data-driven decision-making and enhance operational efficiency at the enterprise level.
It is typically supported by a range of technologies, such as ETL tools, APIs, etc. The choice of technology depends on the enterprise’s specific data integration needs, existing IT infrastructure, and business objectives.
Data federation
Data federation, also known as federated data access or federated data integration, is an approach that allows users and applications to access and query data from multiple disparate sources as if they were a single, unified data source system. It provides a way to integrate and access data from various systems without physically centralizing or copying it into a single repository. Instead, data remains in its original location, which users can access and query using a unified interface.
However, data federation can introduce some performance challenges. For example, it often relies on real-time data retrieval from multiple sources, which can impact query response times.
Data virtualization
Data virtualization allows organizations to access and manipulate data from disparate sources without physically moving it. It provides a unified and virtual view of data across databases, applications, and systems. Think of it as a layer that abstracts these underlying data sources, enabling users to query and analyze data in real-time.
Data virtualization is a valuable data integration technique for organizations seeking to improve data agility without the complexities of traditional ETL processes.
Middleware integration
In simple terms, middleware integration is a data integration strategy that focuses on enabling communication and data transfer between systems, often involving data transformation, mapping, and routing. Think of it as a mediator that connects different software applications, allowing them to perform together as a cohesive unit.
For example, you can connect your old on-premises database with a modern cloud data warehouse using middleware integration and securely move data to the cloud.
Data propagation
Data propagation is when information or updates are distributed automatically from one source to another, ensuring that all relevant parties have access to the most current data.
Data integration technologies
Data integration technologies refer to the platforms, tools, or software solutions that facilitate data integration. Consumers have many choices today when it comes to data integration technologies. From basic ETL tools to full-fledged data integration platforms, a solution exists for every business.
The following are the most widely used data integration technologies:
ETL tools: ETL tools extract, transform, and load data into the target system. These are mostly standalone tools that specifically focus on the ETL aspect of data integration.
Data integration platforms: Data integration platforms are high-end solutions that provide a suite of products to simplify and streamline data integration from end to end.
Cloud data integration solutions: These are specialized solutions designed to simplify data integration in cloud-based environments.
Change data capture tools: These tools capture and replicate changes in the source data to keep target systems up to date in near real-time.
Data migration tools: Data migration tools allow you to integrate data by moving data sets from one place to another seamlessly.
Data warehousing solutions: Not exactly a technology to integrate data, but a technology used for data integration. Automated data warehouse tools provide the infrastructure and tools necessary to design and build data warehouses used as target systems for data integration.
What are the challenges in data integration
The data integration process can be a challenge, especially if you deal with multiple data sources. Sources can have varying formats, structures, and quality standards, making it essential to establish a robust data integration strategy. Additionally, you’ll need to plan your integration project to ensure data accuracy and timeliness throughout the process. Here are the challenges you can expect to encounter:
- The data sources keep changing—more pop up every now and then— and the volume keeps rising. Just as data integration is a continuous process, ensuring that your systems can handle increased loads and new data sources is also an ongoing challenge.
- Dealing with data coming in from various sources and in different formats is the most common challenge that teams encounter. Integrating such heterogeneous data requires adequate transformation and accurate mapping to ensure interoperability.
- Maintaining data quality can also be a challenge. You might face issues like missing values, duplicates, or data that doesn’t adhere to predefined standards. Cleaning data to resolve these issues can be time-consuming, especially if done manually. These issues create bottlenecks in the ETL pipeline, impacting downstream applications and reporting.
- Vendor lock-in is when an organization becomes heavily dependent on a single service provider’s technology, products, or services to the extent that switching to an alternative solution becomes challenging and costly. The underlying issue with this challenge is that it’s often too late before organizations realize that they have this problem.
- Maintaining the data pipeline is a significant challenge as it includes the ongoing upkeep and optimization of integrated systems to ensure they function efficiently and deliver accurate and up-to-date information. Over time, sources change, new information becomes available, and business requirements evolve. Such circumstances necessitate adjustments to the integration process.
Overcoming these challenges today means using specialized tools powered with advanced technologies, such as artificial intelligence (AI).
Data integration best practices
There’s more to data integration than combining data sources and loading it into a centralized repository—successful data integration requires careful planning and adherence to best practices.
- Define clear objectives before embarking on a data integration project. Doing so provides a roadmap and purpose for the entire effort. It also helps in setting expectations and ensuring that the project delivers tangible business value.
- Select the integration technique that best aligns with your organizational objectives and data sources.
- Implement data quality checks, cleansing, and validation processes to maintain consistency and accuracy. Your efforts will only yield the desired results if the integrated data is healthy. It’s a simple case of “garbage in, garbage out.”
- Always opt for a scalable integration architecture that can handle data growth without performance bottlenecks. This may involve using distributed systems, cloud-based solutions, or data warehousing technologies designed for scalability.
- Ensure that your organization complies with industry and regulatory standards, such as GDPR and HIPAA when integrating data by implement robust security measures, encryption, and access controls.
Data integration use cases
Business intelligence (BI) and data warehousing: Use data integration to bring together information from different sources and operational systems into a central data warehouse. This gives you a unified view, making reporting and analytics more efficient. You can then make better, data-driven decisions and gain insights into your business performance.
Customer relationship management (CRM): Integrate customer data from different touchpoints, like sales, marketing, and support systems. This helps you improve customer service, personalize interactions, and target your marketing efforts more effectively.
E-commerce integration: Connect and synchronize data between your e-commerce platforms, inventory management systems, and other backend systems. This ensures accurate product information, inventory levels, and streamlined order processing.
Supply chain management: Integrate data across your supply chain, from procurement and manufacturing to distribution and logistics. This improves visibility into your entire supply chain process, reducing inefficiencies and optimizing inventory levels.
Healthcare integration: Integrate patient data from electronic health records (EHR), laboratory systems, and other healthcare applications. Healthcare data integration enables you to have a comprehensive view of patient information, leading to improved patient care and treatment outcomes.
Human resources (HR) integration: Integrate HR data from various systems, including payroll, recruitment, and employee management. This ensures accurate and up-to-date employee information, streamlining HR processes and compliance reporting.
Mergers and acquisitions (M&A): When your organization undergoes mergers or acquisitions, use data integration to merge information from disparate systems for a smooth transition. This includes combining customer databases, financial systems, and other operational data.
Internet of things (IoT) integration: Connect and integrate data from your IoT devices to central systems for analysis. This is particularly useful in industries like manufacturing, agriculture, and smart cities, where data from sensors and devices is crucial for decision-making.
Streamline enterprise data integration with Astera
Astera is an end-to-end data integration solution powered by automation and AI. With Astera, you can:
- Handle unstructured data formats seamlessly
- Clean and prepare data for processing
- Build fully automated data pipelines
- Build a custom data warehouse
- Manage the entire API management lifecycle
- Exchange EDI documents with trading partners
Astera empowers you to do all this and much more without writing a single line of code using its intuitive, drag-and-drop UI. Its vast library of native connectors and built-in transformations further simplify the process for business users.
Want to learn more about how Astera can streamline and accelerate your data integration project? Visit our website or contact us to get in touch with one of our data solutions experts and discuss your use case.
Authors:
Khurram Haider
