Topic 47

Social consequences of outdated or incorrect data stored in databases by Simon Ruiz

This essay focuses on HOW outdated or incorrect data stored in databases lead to a social consequence and how it may be solved.

A flat file or spreadsheet – database is a simple method for storing data. All records in this database have the same number of "fields". Individual records have different data in each field with one field serving as a key to locate a particular record. For example, your social security number may be the key field in a record of your name, address, phone number, sex, ethnicity, place of birth, date of birth, and so on. For an individual, or a tract of land, there could be hundreds of fields associated with the record. When the number of fields becomes lengthy a flat file is cumbersome to search. Also the programmers usually determine the key fields and searching by other determinants may be difficult for the user.

Although this type of database is simple in its structure, expanding the number of fields usually entails reprogramming. Additionally, adding new records is time consuming, particularly when there are numerous fields. Other methods offer more flexibility and responsiveness. Other files do exist such as hierarchical file and relational files. All of the files introduced serve easy access to the users.

As understanding the picture of what data are included in shared databases, it is important to assess the credibility of outdated or incorrect data in stored databases. For example, outdated or incorrect data in government agencies that are ultimately distributed to citizens in order to vote, etc, may cause social consequences. Government agencies usually apply hierarchical database system, which are particularly attractive with their objectives. In a hierarchical database system, the act of adding and deleting records is easy, fast data retrieval through higher-level records is possible, and multiple associations with records in different files are also possible. However, the disadvantages seem to deepen the social consequences of the incorrect data in the hierarchical database system. Which are: pointer path restricts access, each association requires repetitive data in other records, pointers require large amount of computer storage. These disadvantages come into role after the problems have risen.

One of the causes of the social consequences is that the incorrect or outdated data simply gives the wrong information about the individual, and therefore many of the legal documents for that individual become distorted. For example, a retired man who is about to receive a pension from the companies he has worked does not get into account for his measure of pension. This is because the transferring of data in the databases among the government officials was incorrect and easily mistaken by the person in charge. The social consequence for this man was fatal in terms of financial life just because of an incorrect data in an easily accessed data. Now this man receives an incorrect pension because of the incorrect data stored in the database. This sort of failure happens when the man has transferred his career time to time and his state officials did not keep track of his job career. Another simple cause can be that his name or other raw data were inserted incorrectly by the secretary or the person in charge.

Another problem can arise with the presence of outdated data in stored databases. For example, a person living in Long Beach, New York moves to Ocean Beach, California. This obviously changes his address to where he is living currently. But does the government keep track of where he is living? Let’s say they don’t. If they continue to do so, when they distribute electronic voting machine passwords to vote, they send it to the wrong address, to Long Beach, New York. Consequently, the person who recently moved to a new location does not get the advantage to vote because of its outdated data stored inside the government’s database. An individual lacking the advantage to vote because of outdated data in government database could lead into a serious social problem within the country.

In these situations, we will want to examine spatial relationships based upon location, as well as functional and logical relationships among geographic features. Spatial relationships include the data of absolute and relative location, distance between features, proximity of features, features in the “neighborhood” of other features, and direction and movement from place to place. This is a solution what the GIS provides, where it has the power of recording more than location and simple attribute information.

Functional relationships among geographic features and its attributes include information about how features are connected and interact in real-life terms. A road network might be classified functionally from the largest superhighway down to the most isolated rural road or suburban cul-de-sac based upon their role in the overall transportation system. Minor roads and suburban streets "feed" major highways, but are not directly connected to them. As another example in assessing wildlife habitats, various environmental conditions function together to define the optimal living environments for certain species. Within cities, ownership is a functional classification of great importance as is land use and zoning classification. Logical relationships involve "if-then" and "and-or" conditions that must exist among features stored in the dataset. For example, no land may be permitted to be zoned for residential use if it lies within a rivers five-year flood plain. Development may not allowed in the habitat of some endangered species.

As a conclusion, databases can be designed to represent, model, and store information about these relationships as needed for particular applications. Discussed broadly, the stakeholders among these situations are with no doubt, YOU. The state officials do not rarely take responsibility for an incorrect or outdated data that has been caused by external tasks supporting you.