how to make barcode in vb.net 2010 Cleansing Data in Software

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The load process is often called upon to clean up source data. Many data cleansing tasks can be sensibly automated as part of the load process. Some, however, are best left for cleanup at the operational source. Some basic principles help distinguish the former from the latter.
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17 Design and ETL 421
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When data is to be cleansed, it is necessary to have a set of rules that describe what must occur. In some cases, it is possible to define simple data-driven requirements in tabular format. These may be instantiated as physical tables, allowing the rules to be maintained separately from ETL code. However, this separation may also hamper processing efficiency. Frequently, the ETL process will encounter a fact for which it cannot identify valid dimensional detail. Failure to load this data may result in inconsistencies between the star schema and the operational systems. Special techniques can be employed that allow it to be loaded in the absence of critical dimensional detail, so that the two systems are reconciled. This will require taking measures that allow the facts to be adjusted once the required detail is available.
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What Should Be Cleaned Up
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Data can be changed, or cleaned up, in a variety of ways. Some kinds of cleansing are best done as part of the process that loads the data warehouse, while some are not. It is reasonable, for example, to standardize the formatting of phone numbers while loading a star schema. On the other hand, it is definitely not a good idea to reassign an order transaction from one salesperson to another. These are black and white extremes, but in between lies a gray area. What should one do, for example, if a salesperson was entered inconsistently How does one sort out the kind of cleansing that is reasonable from that which is not The answer lies in the fundamentals of data warehousing. The purpose of a data warehouse system is to provide operational data that has been restructured for analysis. It provides facts that are consistent with what is represented in operational. Changing the meaning of facts or creating new facts as part of the load process goes beyond this simple mission, providing a view of the business that is inconsistent with that provided by operational systems. This situation calls into question the validity of the data warehouse and erodes user confidence. TIP Creating data is not the province of the data warehouse. All information should be drawn from operational and reference sources. If the data warehouse provides facts that are inconsistent with operational systems, users will lack confidence in its accuracy. This simple guideline can be used to evaluate which kinds of information should be cleaned up as part of the load process, and which types should not. When evaluating any cleansing activity, ask yourself: Will this action result in a fact that is consistent with operational data If so, the proposed cleansing activity is probably reasonable. If not, it should probably be avoided. Consider, for example, the activities that are usually involved in constructing a rich set of dimension attributes. Examples include concatenation of source values, reformatting of phone numbers and addresses, supplementing codes with associated reference text, or transforming Boolean flags into descriptive text. These best practices produce a rich and descriptive set of dimension attributes, as described in 3. They do not change the meaning of facts; the end result presents facts that are consistent with operational systems. This kind of activity is a sensible and useful part of the load process.
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Now consider processing an order that was assigned to the incorrect salesperson. If you know of this error, does it make sense to correct it as part of the ETL process This time, the answer is no. Such an action would result in a fact that is inconsistent with the operational data. The source system would show order dollars associated with one salesperson, while the star would show order dollars associated with a different person. If, on the other hand, the reassignment of the order is also captured somewhere in a source system, then the data warehouse would provide a representation that is consistent. Ralph Kimball and Margy Ross make this important point using a vivid metaphor. They describe the process of loading the data warehouse as one of publishing information, as opposed to operational systems that create information. You can use this metaphor to guide decisions about data cleansing, even if you have not implemented Kimball and Ross s recommended data warehouse architecture. Even in a Corporate Information Factory or stand-alone data mart, your goal should be to publish data, not manufacture it. Staying out of the business of manufacturing data will help ensure user confidence in the data warehouse. As an added bonus, the star schema will shine a light on issues with source data, serving as an impetus for efforts to correct these problems. These activities will range from the simple correction of invalid data to system enhancements that alter the way information is collected. The star schema becomes a tool for managing the cleanup of operational data and a means to view the progress of such efforts.
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