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Understanding Data Integration

Given the increased abundance of data that is available from a variety of sources, finding every piece of relevant information on a given subject matter should not be a difficult job for people that are tasked to do such work.

But the challenge though lies in creating something cohesive and comprehensive out of all these data. This can be especially challenging if the data gathered are conveyed in different formats and styles, notwithstanding the format and style used by the organization gathering the data which may vary wildly from the data that were gathered.

In order to create this cohesive and comprehensive of all these disparate data, one important step needs to be conducted: integration.

Data integration is the process of combining data from different sources into a single, unified view that will facilitate the ease of analyzing in order to gain critical insights that will drive better business decisions. It is part of the entire data management process that begins with data extraction, with integration being the final step in the process.

The data integration process

In truth, there is no standard process or methodology for data integration. Instead, what we have are. Typically, the client contacts the primary server with a data request. The server then extracts the required data from both internal and external sources and combines it into a single, cohesive view before being returned to the client. How the server does the processing is done by either of these two methods:

ETL (Extract, Transform, Load) – It is the traditional method used in data integration, wherein data is fetched from its sources, moved to a staging data repository, where it is subjected to cleansing and conversion, and then loaded into a target source. This is accomplished with the help of data warehouses or data marts.

ELT (Extract, load, transform) – It is one of the more recent methods of integrating data, in which data is loaded first into a target source before it is extracted and transformed. This is accomplished through a data lake or a cloud data warehouse and it provides ample room for the user to adapt their approach to the data in real-time.

Benefits of data integration

Given that there is no standard data integration process in place, each enterprise, ultimately, would have its own methodologies for accomplishing this process. As such, the efficiency rate varies with each enterprise’s iteration of data integration. But a well-thought-out data integration process offers a great deal of benefits.

For one, it ensures that your datasets are complete by automatically importing the data from a comprehensive list of source systems into a centralized location. This reduces the chance of errors arising from incomplete datasets while ensuring that the data is always up to date. At the same time, this saves considerable time and resources on the part of the organization as users no longer have to spend time searching for data and can immediately do their analyses and generate reports in a shorter timeframe.

Collaboration is also greatly emphasized and encouraged as data integration enables team members to access the broad range of data sources available. Thus, a user can access vital data and use it to improve business processes, regardless of their department.

Data integration also facilitates an enhanced customer experience by delivering the data needs of the customer in a swift and efficient manner, which in turn leads to higher profits.

More importantly, a well-thought-out data integration process helps businesses become more competitive and be assured of further growth in the future as it provides an increased level of efficiency for the business in multiple areas.


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