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Define the M e a n i n g of Data
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A database contains business rules to support organizational policies Defining business rules is the essence of defining the semantics or meaning of a database For example, in an order entry system, an important rule is that an order must precede a shipment The data base can contain an integrity constraint to support this rule Defining business rules enables the database to actively support organizational policies This active role contrasts with the more passive role that databases have in establishing a common vocabulary In establishing the meaning of data, a database designer must choose appropriate con straint levels Selecting appropriate constraint levels may require compromise to balance the needs of different groups Constraints that are too strict may force work-around solu tions to handle exceptions In contrast, constraints that are too loose may allow incorrect data in a database For example, in a university database, a designer must decide if a course offering can be stored without knowing the instructor Some user groups may want the instructor to be entered initially to ensure that course commitments can be met Other user groups may want more flexibility because course catalogs are typically printed well in advance of the beginning of the academic period Forcing the instructor to be entered at the time a course offering is stored may be too strict If the database contains this constraint, users may be forced to circumvent it by using a default value such as TBA (to be announced) The appropriate constraint (forcing entry of the instructor or allowing later entry) depends on the importance of the needs of the user groups to the goals of the organization
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The importance of data quality is analogous to the importance of product quality in manufacturing Poor product quality can lead to loss of sales, lawsuits, and customer dis satisfaction Because data are the product of an information system, data quality is equally important Poor data quality can lead to poor decision making about communicating with customers, identifying repeat customers, tracking sales, and resolving customer problems For example, communicating with customers can be difficult if addresses are outdated or customer names are inconsistently spelled on different orders Data quality has many dimensions or characteristics, as depicted in Table 21 The im portance of data quality characteristics can depend on the part of the database in which they are applied For example, in the product part of a retail grocery database, important char acteristics of data quality may be the timeliness and consistency of prices For other parts of the database, other characteristics may be more important
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Introduction to Database Environments
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TABLE 21 Common Characteristics of Data Quality
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Characteristic Completeness Lack of ambiguity Correctness Timeliness Reliability Consistency
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Meaning Database represents all important parts of the information system Each part of the database has only one meaning Database contains values perceived by the user Business changes are posted to the database without excessive delays Failures or interference do not corrupt database Different parts of the database do not conflict
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A database design should help achieve adequate data quality When evaluating alterna tives, a database designer should consider data quality characteristics For example, in a customer database, a database designer should consider the possibility that some customers may not have US addresses Therefore, the database design may be incomplete if it fails to support non-US addresses Achieving adequate data quality may require a cost-benefit trade-off For example, in a grocery store database, the benefits of timely price updates are reduced consumer com plaints and less loss in fines from government agencies Achieving data quality can be costly both in preventative and monitoring activities For example, to improve the timeliness and accuracy of price updates, automated data entry may be used (preventative activity) as well as sampling the accuracy of the prices charged to consumers (monitoring activity) The cost-benefit trade-off for data quality should consider long-term as well as shortterm costs and benefits Often the benefits of data quality are long-term, especially data quality issues that cross individual databases For example, consistency of customer iden tification across databases can be a crucial issue for strategic decision making The issue may not be important for individual databases 16 on data warehouses addresses issues of data quality related to strategic decision making
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