What is Data Integration?
Data integration is a core component of the broader data management process, serving as the backbone for almost all data-driven initiatives. It ensures businesses can harness the full potential of their data assets effectively and efficiently. It empowers them to remain competitive and innovative in an increasingly data-centric landscape by streamlining data analytics, business intelligence (BI), and, eventually, decision-making.
But what exactly does data integration mean?
Data Integration Definition
Data integration is a strategic process that combines data from multiple sources to provide organizations with a unified view.
The data integration process
The ultimate goal of integrating data is to support organizations in their data-driven initiatives by providing access to the most up-to-date data. In other words, data integration means breaking down data silos and providing enterprises with a single source of truth (SSOT). The concept of SSOT implies that data must be accurate, consistent, and readily available for use across the organization, a critical requirement for making effective business decisions.
Data integration is not merely a technical endeavor. Instead, it transcends the domain of IT and serves as the foundation that empowers business users to take charge of their own data projects.
Data Ingestion vs Data Integration
Application integration is another concept that’s frequently used in this space. It’s important to differentiate between application integration and data integration, especially since the two often complement each other in achieving seamless operations.
While application integration focuses on enabling software applications to work together by sharing data, data integration focuses on consolidating and harmonizing data from disparate sources to provide a holistic view of data for analysis and decision-making. Once again, we have a table below to summarize application integration vs data integration:
| Data Ingestion | Data Integration | |
| Definition | Imports data into a storage or processing system. | The process of combining data from diverse sources into a unified and cohesive view. |
| Objective | To bring data into a storage or processing environment as quickly as possible. | To create an accurate and comprehensive representation of data for analysis, BI, and decision-making. |
| Focus | The initial stage of data acquisition. | Encompasses the broader process of data standardization. |
| Data Movement | Data movement from source to destination, with minimal transformation. | Data movement involves data transformation, cleansing, formatting, and standardization. |
| Data Quality Consideration | Emphasis is on data availability rather than extensive data quality checks. | Enforces data quality standards through transformations and cleansing as part of the integration process. |
| Use Cases | Use cases include data lakes and data warehouses for storage and initial processing. | Use cases include creating data warehouses, data marts, and consolidated data views for analytics and reporting. |
| Example | Collecting log files from multiple servers and storing them in a data lake. | Extracting, transforming, and loading customer data from various CRM systems into the central customer database for analytics. |
Application Integration vs Data Integration
Application integration is another concept that’s frequently used in this space. It’s important to differentiate between application integration with data integration, especially since the two often complement each other in achieving seamless operations.
While application integration focuses on enabling software applications to work together by sharing data, data integration focuses on consolidating and harmonizing data from disparate sources to provide a holistic view of data for analysis and decision-making. Once again, we have a table below to summarize application integration vs data integration:
| Application Integration | Data Integration | |
| Definition | Connecting and coordinating software applications and systems for data sharing and process automation. | Combining data from various sources into a unified and accurate view for analysis and decision-making. |
| Scope | Enable applications to work together seamlessly. | Data consolidation and harmonization from multiple sources, focusing on data movement and transformation. |
| Business Objective | Enhancing business process efficiency, automating workflows, and improving user experiences through seamless application interactions. | Providing a holistic view of data across the organization, supporting data-driven decision-making, reporting, and analytics. |
| Data Flow | Managing data and process flow between applications, ensuring real-time communication and collaboration. | Involves data extraction, transformation, and loading processes, among others. |
| Use Cases | Integrating CRM with marketing tools, connecting e-commerce websites with inventory management systems, etc. | Creating centralized data warehouses, consolidating customer data, merging data for financial reporting, etc. |
| Tools and Technologies | Middleware, APIs, message queues, ESBs, integration platforms, and API gateways. | ETL tools, data integration platforms, data warehouses, data lakes, and database management systems. |
How Does Data Integration Work?
Integrating data can be a challenge, especially if you deal with multiple data sources. Each source may have its own format, structure, and quality standards, making it essential to establish a robust data integration strategy.
Additionally, you’ll need to plan your data integration project to ensure data accuracy and timeliness throughout the integration process. Overcoming these challenges often involves using specialized data integration tools that streamline the process and provide a unified, reliable dataset for informed decision-making and analysis.
As far as the data integration process is concerned, it can be done in real time, in batches, via streaming, etc. Generally, though, it 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 databases, spreadsheets, cloud services, APIs, etc.
- Data Extraction
Once you have your data sources in mind, you’ll need to devise an efficient data extraction plan to pull data from each source. Modern organizations use advanced data extraction tools to access and retrieve relevant information. These tools are powered by artificial intelligence (AI) and machine learning (ML) algorithms and automate the entire extraction process, including document data extraction.
- Data Transformation
Transforming the extracted data is the next step in the data integration process. You may have data in various formats, structures, or even languages when your data sources are disparate. You’ll need to transform and standardize it so that it’s consistent and meets the requirements of the target system or database.
Organizations use specialized data transformation tools since the process can become tedious if done manually. Data transformation typically includes applying tree joins and filters, merging data sets, normalizing/de-normalizing data, etc.
- Data Quality Improvement
When integrating data, you’ll find it often comes with errors, duplicates, or missing values. A robust data quality management framework 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 Mapping
Data mapping involves defining how data from different sources correspond to each other. More specifically, it is the process of matching data fields from one source to data fields in another. Therefore, it’s a step of significant importance in data integration. Data mapping tools automate this step as they provide intuitive, drag-and-drop UI, ensuring that even non-technical users can easily map data and build data pipelines.
- Data Loading
Once you correctly map your data, the next step is all about loading it into a central repository, such as a database or a data warehouse. 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.
Different Types of Data Integration
Types of data integration generally refer to the different data integration techniques useful in different scenarios. They are also referred to as data integration strategies or methods.
It’s worth noting that data integration techniques differ from data integration technologies, which refer to the platforms, tools, or software solutions that facilitate data integration.
Data Integration Techniques and Strategies
These are the different ways of integrating data. Depending on your business requirements, you may have to use a combination of two or more data integration techniques. These include:
Extract, Transform, Load (ETL)
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. Consider using automated ETL tools to accelerate data integration and unlock faster time-to-insight.
Extract, Load, Transform (ELT)
Similar to ETL, except for the sequence of the rest of the process, data extraction is the first step in ELT, which is a fairly recent data integration technique. Instead of transforming the data before loading it into, say, a data warehouse, data is directly loaded into it as soon as it’s extracted. The transformation takes place inside the data warehouse, utilizing the processing power of the storage system.
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 data integration 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 data integration 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 sits in the middle and 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.
For example, let’s say you have a database of product prices, and you make changes to these prices in one central location. Now, suppose you want to automatically update these new prices across all the places where this data is needed, such as your website, mobile app, and internal sales tools. In this case, data propagation can be a viable solution.
Data Integration Technologies
Consumers have many choices today when it comes to data integration technologies. From basic ETL tools to full-fledged data integration platforms, a data integration 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 Platforms: 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. These solutions provide the infrastructure and tools necessary to build and maintain data warehouses used as target systems for data integration.
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 data 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. Not only that, but data integration also 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 data cleansing steps, such as data 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 of data integration that greatly benefits organizations. End-to-end data integration platforms enable you to secure company-wide data in various ways, such as applying access controls, using advanced encryption and authentication methods, etc.
Data Integration Challenges
Before proceeding, let’s take a moment to realize that combining several data sources in itself is a significant challenge. Here are the challenges you’ll face, if not when formulating your data integration strategy, then certainly when executing it:
Rising Data Volume
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 data loads and new data sources is also an ongoing challenge. The sheer volume of data you may need to integrate can strain your organization’s infrastructure and resources if it lacks a scalable solution.
Compatibility
Dealing with data coming in from various sources and in different formats is the most common issue that data teams encounter. Integrating such heterogeneous data requires careful transformation and mapping to ensure that it can work together cohesively. It also involves reconciling disparate data structures and technologies to enable seamless interoperability.
Data Quality
Maintaining data quality can also be a challenge when integrating data. You might face issues like missing values, duplicates, or data that basically doesn’t adhere to predefined standards. Cleaning and transforming data to resolve these issues can be time-consuming, especially if done manually. These issues create bottlenecks in the data integration pipeline, potentially impacting downstream applications and reporting.
Vendor Lock-In
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.
Maintenance
Maintaining the data integration 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. It’s one of those challenges that don’t get as much limelight as some of the others. Over time, data sources may change, new data may become available, and business requirements may evolve. Such circumstances necessitate adjustments to the integration process, hence the importance of maintenance.
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
Data integration often involves complex processes, diverse data sources, and significant resource investments. So, before embarking on the data integration project, it’s essential to define clear objectives from the outset. Doing so provides a roadmap and purpose for the entire effort. It also helps in setting expectations and ensuring that the data integration project delivers tangible business value.
Select the Right Integration Approach
There are various data integration methods to choose from, including ETL, API-based integration, and real-time data streaming. Select the approach that best aligns with your organizational objectives and data sources. A financial institution, for example, needs to aggregate data from various branches and systems to detect fraud in real time. In this case, real-time data streaming will ensure prompt detection, protecting the institution from financial losses and reputational damage.
Take Data Quality Seriously
Your integration efforts will only yield the desired results if the integrated data is healthy. It’s a simple case of “garbage in, garbage out.” Implement data quality checks, cleansing, and validation processes to maintain data consistency and accuracy.
Make it Scalable
Consider the scalability and performance requirements of your organization. As data volumes grow, your integration architecture should be able to handle increased loads without degradation in performance. 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.
Pay Attention to Security and Compliance
Implement robust security measures, encryption, and access controls to ensure data privacy and compliance with relevant regulations, such as GDPR and HIPAA. Ensure that your organization complies with industry and regulatory standards when integrating data.
Streamline Enterprise Data Integration With Astera
Astera is an end-to-end data integration platform 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.