One of the most important tasks in the data integration process is to set realistic expectations. The term data integration calls for seamless coordination of diverse databases, software, equipment, and personnel into an alliance that works smoothly, without the persistent headaches that characterize less comprehensive information management systems. Think again. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay The requirements analysis phase offers one of the best opportunities in the process to recognize and assimilate the full scope of data integration complexity. It's possible that paying close attention to this analysis is the most important ingredient in creating a system that will live to see adoption and maximum usage. However, as the field of data integration progresses, other common obstacles and compensating solutions will be easily identified. Current integration practices have already highlighted some family challenges and strategies to address them, as described below. For most transportation agencies, data integration involves synchronizing large amounts of variable, heterogeneous data from internal legacy systems that vary in data format. data. Older systems may have been created from flat file, network, or hierarchical databases, as opposed to more recent generations of databases that use relational data. Data in various formats from external sources continues to be added to legacy databases to enhance the value of the information. Each generation, product, and national system has unique requirements that must be met to store or extract data. Therefore, data integration may involve different strategies to manage heterogeneity. Please note: this is just an example. Get a custom paper from our expert writers now. Get a Custom Essay In some cases, the effort becomes a major exercise in data homogenization, which may not improve the quality of the data offered. Data quality is a major concern in any data integration strategy. The information provided must be deleted before conversion and integration, otherwise an agency will almost certainly face serious problems with the data later. Inherited data impurities have a compositional effect; by their nature, they tend to focus on users of large volumes of data. If this information is corrupted, so will the decisions made. It is not uncommon for previously undiscovered data quality issues to arise in the process of cleaning the information to be used by the embedded system. The problem of bad data leads to procedures for regularly monitoring the quality of the information used. But it is not always clear who has ultimate responsibility for this work.