Lesson 1: Enforcing Data Quality According to Business Requirements in Visual Basic .NET

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Lesson 1: Enforcing Data Quality According to Business Requirements
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Establishing Business Requirements for Data Quality
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Problems with data quality are usually expensive and difficult to eradicate. You have a problem if data in your system does not mean what your users think it does (or should) or if the data does not meet its specification because of mistakes in user entries, errors in transmission, glitches, and so on. Data can also be difficult to understand and categorize because of complexity or lack of metadata. Resolving data quality problems is often one of the most time-consuming tasks that a DBA performs. Traditionally, to meet quality standards, data needs to meet the following criteria:
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Accuracy and completeness The data is recorded correctly, and all relevant data
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is recorded.
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Uniqueness Timeliness Consistency
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Entities are recorded once. The data is kept up to date. The data agrees with itself.
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However, these criteria require further investigation and qualification. Accuracy and completeness are extremely difficult to measure, and accuracy is not an absolute measurement. If I am looking for a figure for the population of the United States, I would probably be happy with 300 million, whereas if I am balancing a set of accounts, I need data accurate to the dollar, or even to the cent. If you are computing aggregates, you can tolerate a degree of inaccuracy. The conventional definitions provide no guidance of what is important; how you should define the keys to your data; how you measure interpretability, accessibility, and metadata; and how suitable your data is for analysis. Nor do they provide guidance about practical improvements to the data. You cannot define data quality in isolation, outside the context of the organization that is using the data. A DBA needs to consult with business analysts, managers, and users to determine the business requirements for data and to generate a definition of data quality that is measurable, reflects the use of the data, and leads to improvements in the processes used. As a starting point, the DBA must have a very clear idea of how to standardize data elements, establish metrics for measuring data quality, and use these metrics to monitor the quality of the data. The first step toward improving data quality is to formulate a clear understanding of how and where data quality problems occur. Typically, data is not static but instead flows in a data collection and usage process during the following operations:
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Data gathering
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Business Requirements
Data delivery Data storage Data integration Data retrieval Data mining and analysis
Data Gathering
Data can be transferred or replicated from other systems, or gathered from measuring devices. However, a large proportion of data is entered manually. This can lead to different people using different standards for content and format, to two people entering the same information at the same time, and to approximations and surrogates you say Mr. and I say Mister. You can use built-in integrity checks to tackle data-gathering problems. Sometimes the solution is managerial for example, rewarding staff for accurate data entry can bring about genuine improvements. Techniques such as duplicate removal and field value standardization, and the use of data validation and fuzzy logic, can help address the problems associated with badly entered data.
Data Delivery
When data is delivered to your system, inappropriate pre-processing can corrupt or destroy it. Inappropriate aggregation, nulls converted to default values, buffer overflows, and transmission glitches can cause problems. You need to verify data by using data verification, checksums, and verification parsers. Data verification can check whether uploaded files fit an expected pattern and whether dependencies exist between data streams and processing steps. If your organization has a datastream supplier, this supplier should have made a formal commitment to data quality.
Data Storage
Problems in physical storage can exist, but disk storage is relatively inexpensive, and you can solve most physical problems by installing faster or larger disks and disk arrays that provide failover protection. You should also formulate and enforce a sound backup and restore policy, and place your transaction files on a separate disk from your database files. Data storage problems more typically arise from poor metadata
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