Characteristics of goodquality According to

Characteristics of goodquality According to Redman in 1998, data quality is lying at the root ofinformation quality where poor data quality resulting information in losingquality and vice versa. The poor quality of data will produce the poor qualityof information which adversely affected an organization at operational,tactical, and strategic levels. There are six characteristics of good dataqualities mentioned by The Audit Commission:1.      AccuracyA data base is presumed to be perfect as the data should be precisely accurate byrepresenting what was intended and should becaptured at the point of activity that data are free ofidentifiable errors. The data that is accessed needto be reliable because if flaws faced in the data collection, datastorage, or data retrieving resulted in unreliable or inaccurate information.

Havingan accurate and reliable data base will impact positively to the new businessdevelopment, promotion and also help achieving organizational goals. For example, the customer’s personal information recorded in adatabase should be the same as what the customer has filled up the individualform on paper for bank purposes.  2.      ValidityRecordingand using data should be in line with relevant requirements including the proper use of any rules or definitions in orderto make sure there is a consistency between periods and with similar organisations,measuring what was intended. For example, onsurveys, data such as gender, religion and nationality are usually limited to a set of choices and open answersare not allowed. According to survey’s requirement, any answers other thanthese would be considered invalid where this is the case of most data andshould be carefully considered when determining its quality. In theorganization, staffs in every department understand what data is valid orinvalid to them, therefore requirements must be utilized when accessing dataquality. 3.

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      ReliabilityIf the data source is reliable the organisation doesnot need to spend as long or as much money checking the validity of the data. Itwill be from a valid and trusted source. For example, Wikipedia is notconsidered a reliable source as the information is not checked and anyone canadd to the site. 4.     TimelinessData should not be areflection of an earlier state of affairs and shouldbe up to date as possible at or near the time of the eventTimeliness is determined as a very context oriented as the data attributes andvalues should be detailed at the correct level, so the more timely the data,the more costly and difficult to produce.

Nowadays, living inhigh technological advances, by having out-of-date information will lead acompany to achieve their goals and help them survive in a competitive area. Forexample, broker who buy and sell shares need to have very up to date informationbecause share prices change so rapidly.  5.     RelevanceData need to be relevant to its intended use whereit is directly related to the business need. For example, an organizationpurchases market research results from a survey execute in Malaysia.

Althoughthe information gathered may be accurate and up to date it may not be relevantto Singapore, where the company does most of its business, because trends,buying habits and spending pattern are different.  6.     CompletenessCompleteness is as necessary as accuracywhen inputting data into a database where datarequirements need to be clearly specified due to the information needs of theorganisation and data collection processes matched to these requirements. Incompletedata can be damaging as inaccurate data as it may give an organisation amisleading picture on which they base management decisions.

For example, salesdata taken from only three quarters of the year may give distorted picture ofan average business performance.  


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