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Data integration combines multiple data sources into one, but it can be complex due to incompatible formats. Databases are the most common storage method, but differences in presentation and content can hinder integration. Business and research are the main areas requiring integration, and the prevalence of free online databases has increased its importance.
Data integration is the union of multiple data sources into a single data source. This practice is often very time-consuming and complex, as the different data sources are likely to be incompatible with each other. Simple things like different column names on a spreadsheet are enough to prompt the date to be reformatted. This process is most common in situations where two groups have started out with no connection, but are brought together after working independently. Data integration has become a bigger topic due to the prevalence of free data sources and online databases.
The data part of data integration can be almost anything as long as it is stored in a computer system. The actual content of the data is rarely as important as how the data is stored. Most often, data is stored in databases, organized information systems. These systems contain unique entries and fields that allow users to find information quickly.
The biggest obstacle to any data integration process is the data itself. In many cases, when the data was first set up, there was no intention to ever merge one dataset with another. This means that even though two datasets may refer to the same thing, they are totally incompatible.
Almost everything will make databases incompatible. Something as simple as a difference in presentation, like field order or column width, can be enough to prevent an easy merge. When the data is significantly different, such as a database that contains more or less information, merging is much more difficult.
The two situations that require data integration more than any other are in business and research. In the business world, merging departments or companies requires combining previously separate information into a single structure. This form of integration is generally very difficult unless the original groups use similar software and have similar information goals.
When data integration is done for research purposes, it is generally much smoother. When one researcher gives access to his information to another, the two parties are generally looking at the same process. This means that they will use similar methods to catalog and store their data.
In the past, data integration was a relatively minor area of data studies, but that has changed since the early part of the 21st century. With free online databases becoming more popular and more accurate, businesses are looking to get their information into a sharable format. This allows them both to release their information in public form and to integrate private versions of well-known public interfaces into their systems.
