Data integration is the process of collecting data from disparate sources and bringing it together to provide users with a single, unified view. Data integration is meant to help end users in an organization meet their information needs and produce data sets that are consistent and clean. With big data integration being a big component of the data management process, it is important that data integration technology be advanced enough to assist in further operational and analytical uses.
The development of data integration software applications and platforms includes automating processes to connect source systems to target systems.
Manual Integration – Organizations would initially prefer to use their data analysts to manually integrate the data spread across multiple sources. This involves accessing source systems, analyzing and/or exporting data, and creating reports. The immediate challenge faced while manually integrating data is that it is time-consuming and prone to security risks, especially while giving access to analysts. Furthermore, data in the data stores tend to change frequently, making manually generated reports outdated quickly.
Middleware data integration – This strategy uses a middleware application to transfer data from multiple applications and sources into a single, unified source. Validation and formatting can be done before the transfer begins, which can potentially reduce the chances of compromising the integrity of the data and mitigate disorganized data. The most vital benefit of using this technique is integrating older systems into the new ones in a format that the newer systems can understand.
Data Warehousing – One of the popular data integration techniques is data warehousing, commonly referred to as common data storage. Data is essentially replicated from the source and then stored in the data warehouse. This strategy includes cleansing, formatting, and transforming the data before the process of storage is carried out. The data warehouse acts as a single source of all information promoting better integrity.
Application Integration – Directly linking multiple applications to each other and allowing data to move directly between them. The linking in application integration is done through point-to-point communication or an application tool. The drawback associated with this technique is that application integration results in multiple copies of the same data.
Benefits of data integration
The benefits associated with integrating data into a unified destination are that it provides a single source of truth, facilitates transforming data into a single destination and improves security.
- A single source of truth – Business intelligence platforms are more effective than before due to the movement of data from source systems to a centralized location. The entire data of an organization can be viewed at once which helps in making decisions quicker, identifying hidden patterns and opportunities, and understanding consumer behavior.
- Time-saving and improved effectiveness – The time it takes to prepare and analyze the data is significantly reduced when a company invests in data integration. Additionally, automation of integration eliminates the need for manual gathering of data and thus, employee efficiency is also increased.
- Reduces frequency of error – Employees need to gather information from various locations and accounts to ensure that the data is complete and accurate. Data integration ensures that all the data can be accessed from one place and any additional information added can be found there. Keeping track of data becomes easier and errors are reduced throughout.
- Improved data quality for decision making – Insights can be reached faster because the data quality is often higher through data integration and automated data transformations. It includes data cleansing and applying other data quality measures to rectify errors, inconsistencies, and other issues that may arise in the data set.
Challenges to data integration
While data integration offers significant benefits, organizations must navigate certain challenges, including:
- Data from legacy systems – Integrating the data within a legacy system or mainframes is a huge challenge for data integration. This category of data may have missing markers or other formatting discrepancies.
- Exponential growth of data – Integration of data that is growing rapidly requires that the target storage location is scalable. Rather than having to add physical infrastructure to support fresh data sets, it is essential to utilize a cost-effective storage system.
- External Data – To stay ahead of the competition, organizations cannot depend solely on internal data sources. Integrating external data proves to be a challenge because this data may not be as detailed or conform to the formatting requirements of the organization.
Evolving Landscape of Data Integration Solutions
With advancements in technology, data integration solutions have evolved significantly. Traditional extract, transform, and load (ETL) tools have been supplemented by modern approaches, such as data virtualization, data lakes, and cloud-based integration platforms. These solutions offer real-time data integration, self-service capabilities, and enhanced flexibility to adapt to changing business needs.
Conclusion
By unifying disparate data sources into a single, comprehensive view, organizations can harness the power of their information assets, make informed decisions, and unlock valuable insights. While data integration poses challenges, leveraging evolving integration solutions and best practices can help organizations overcome these hurdles and harness the full potential of their data.
