What is data integration?
Data integration merges data from various source systems to form a unified view of data for technical and business processes.
Organizations have vast sets of data–both internal and external. Business applications and operations teams might need some of this data to complete a transaction or a task. For example, a loan officer who approves mortgages must review customer’s account records, credit histories, and property values.
With the help of data integration, the loan officer gets all the data pulled together in a central place so that they do not have to combine it manually. Data integration is the most critical component that can lead this process to success.
The correct data at the right time, place, and format is vital for a smoother data operation. Dispersed data can cause inconsistency, inefficiency, and inaccuracy in the entire process.
Many organizations use big data integration software to manage and store big data clusters and use them within cloud applications.
Benefits of data integration
Data integration gives analysts a comprehensive view of key performance indicators and other process-related information. Some of the benefits of data integration are:
- Better quality data for decision making. Business executives and developers get the correct data by using data integration. It also includes data cleansing and other data quality measures to fix errors and issues in the database.
- Easy access to data. Integrating data helps data scientists and other business intelligence (BI) users to access data easily for analytics. Integration-driven data pipelines help to deliver required data directly to the users.
- Fewer data silos. Data silos are a group of data accessible to only one group of people and not any other team members in the organization. Data integration helps break down departmental data silos, allowing professionals to use data for analytical purposes.
- Better efficiency for users. As data is easily found in a shared database, users have the time to work on critical things instead of finding required data from various sources. This improves the efficiency of the team members.
- Data-driven business operations. Better efficiency and easy access make it easier for organizations to be more data-driven in strategic planning and operational decision making.
- Cost reduction. Data integration reduces the need for manual tasks as it automates the integration process. It helps reduce costs by removing redundant workflows.
Data integration process
Irrespective of the type of data integration, the flow remains the same. There are six common steps followed in a data integration process.
- Gather requirements. Collect and cross-check business requirements. This stage helps users to continue with planning and design. Consider the various techniques that might be needed for integration.
- Profile data sources. The next step is to generate data profiling and assess reports that need integration. It helps to uncover any hidden details or current data state.
- Review requirements. Once the assessment report is ready, identify the gap between integration requirements and assessment.
- Design. Analysts must design critical concepts such as architectural design, criteria, data cleansing, standardization, etc.
- Implement. One can begin by integrating low volumes of data, at first, and gradually increase the volumes and sources.
- Verify, validate, and monitor. Test the accuracy and efficiency of the integration process. Ensure there is no or very few data loss. The quality of data should not deteriorate after integration.
Data integration techniques
Data integration is a crucial step in the data management process. These techniques automate data integration processes and consolidate data from multiple sources.
- Extract, transform, and load (ETL). From various sources, copies of datasets are collected, modeled, and loaded into the database or a data warehouse.
- Extract, load, and transform. The data is loaded into a data system and later transformed for various analytical purposes.
- Change data capture. This technique tracks database changes in real time and simultaneously applies corrections in data warehouses.
- Data replication. The replication technique duplicates data in all databases. It helps keep the information synced for backup and other operational uses.
- Data virtualization. Here, data is virtually brought together in one place from different systems instead of forming a new database.
- Streaming data integration. This is a real-time data integration technique where data from various streams are continuously integrated and fed into systems for analytics.
Data integration vs. application integration
Some confuse data integration with application integration, but the two have key differences.
Data integration focuses on handling vast amounts of data. It typically integrates data that's already been processed to ensure its quality.
Application integration deals with smaller chunks of data and supports instant data sharing. It ensures data stays consistent even when different individuals or systems update it from various places. Moreover, application integration processes data faster than data integration does. It lets businesses handle fresh data or tackle performance challenges immediately.
Different teams manage these two integration types within a company. DevOps oversees application integration because it ties into software development. Meanwhile, DataOps takes charge of data integration, focusing on coordinating and handling data.
Learn more about how data integration helps businesses make strategic decisions.