The computer revolution has created, over the course of several decades, a climate in which businesses and other organizations produce increasing amounts of data. One of the goals for the creation of new types of data sets is the extraction of valuable information and lessons from this mass of data so that companies can become more data driven, basing decisions and plans on objective fact rather than intuition about the economic or market conditions that might influence profitability.
This explosion in data, however, has created a strong need for new tools that can help businesses make sense of the thousands or even millions of data points that they might create or collect. One such tool is known as data integration. This technique involves making use of data from different sources, combining the data so that users can have a more unified understanding of the larger patterns that may indicate correlations or even causes that managers and business owners should take into account.
A simplified example of data integration may help make the technique clearer. Consider a teacher who is puzzling over the test scores of her students. This data set alone will indicate who is passing or failing, but shed no light on the question of why. Data integration could be used to assist by combining information from the attendance records with the test score grades. New patterns may jump out at the teacher, now, such as students absent more than 30 percent of the time in the two weeks prior to a cumulative test tend to fail that test in much greater proportions.
Data integration systems can be installed and maintained as part of an overall managed programs approach to IT support.