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Data Integration: An Overview

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The importance of data has been considerably increased in the recent period and organizations have become more data oriented. Since the dawn of business systems, combining diverse data sources has been a hurdle. Computer scientists didn’t start creating solutions to allow the compatibility of diverse or distinct databases until the early 1980s. Business in today’s world is impossible without data and analytics processes. They receive data from various sources and this information needs to be unified to gain useful insights.

The study conducted by AIxOutlook stated that nearly 50% of organizations do not have a proper business strategy, resulting in catastrophic effects. Data integration in this scenario will be able to resolve the issue by providing instant data analysis.

The data that is received and collected by organizations may be of various kinds and formats. This data might be of high complexity and stored in various locations. Organizations will not be able to gain insights from these kinds of unsorted data. Without proper data, businesses won’t be able to make accurate decisions based on insights, and inaccurate insights will lead to wrong decision-making. As organizations depend upon various data silos, data obtained may be spread across various systems, which may result in miscommunications between various departments. This may also make an organization unable to meet both its own goals and those of its clients.

In a data-oriented business world, data integration will help in business growth by delivering useful information to businesses. Data integration enables businesses to make better decisions, enhancing customer experience, optimizing business processes, anticipating customer demands, and increasing productivity.

Data Integration

Data integration helps in combining data from different sources to produce a unified view that can be stored for future use. To transform data into meaningful and valuable information, integration of data is necessary. The integrated data will help organizations work efficiently as the process of analysis becomes simpler. The data integration that helps in reducing error will help to provide a holistic view of data and better insights. In the entire data governance process, the data integration process has evolved as one of the most important components. With the rise of big data integration and the necessity to transfer existing data rapidly, businesses are increasingly depending on data integration.

Usually, there are three methods for data integration. They are:

1. Manual data integration – The labor-intensive process that takes hours to complete the process through copy-pasting and CSV uploads.
2. Automated data integration – The data integration process that is done with the help of application programming interfaces (APIs).
3. Engineered data integration – For generating dataflows and to enable integration, a technical team develops and maintains custom APIs and webhooks.

Data integration allows you to assess and act on a single, dependable source of governed data. Data integration connects various disparate data sources to provide a reliable, unified source of controlled data that is complete, accurate, and relevant. This enables analysts, data scientists, and businessmen to utilize BI and analytics tools to explore and analyze the entire information set to find trends and then act on those insights to enhance performance. Data integration also provides accurate and reliable data that can be trusted. Apart from that, it also enables data-driven decision-making. These will enhance the organization’s productivity and future growth.


From the business point of view, as already said, everything is dependent on data. The collected data will be of no use if it is not properly integrated. The process of data integration is not an activity or process that can be done one at a time. The process of data integration is continuously evolving, and several new approaches are also being introduced into the process.

Data integration is typically used as a prelude to more advanced data processing, most notably analytics. Data integration can help you incorporate data from obsolete systems into more modern contexts. A fair analogy for data integration is to produce once and utilize it many times.