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Characteristics of good
quality

 

According to Redman in 1998, data quality is lying at the root of
information quality where poor data quality resulting information in losing
quality and vice versa. The poor quality of data will produce the poor quality
of information which adversely affected an organization at operational,
tactical, and strategic levels. There are six characteristics of good data
qualities mentioned by The Audit Commission:

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1.      Accuracy

A data base is presumed to be perfect as the data should be precisely accurate by
representing what was intended and should be
captured at the point of activity that data are free of
identifiable errors. The data that is accessed need
to be reliable because if flaws faced in the data collection, data
storage, or data retrieving resulted in unreliable or inaccurate information. Having
an accurate and reliable data base will impact positively to the new business
development, promotion and also help achieving organizational goals. For example, the customer’s personal information recorded in a
database should be the same as what the customer has filled up the individual
form on paper for bank purposes.

 

2.      Validity

Recording
and using data should be in line with relevant requirements including the proper use of any rules or definitions in order
to make sure there is a consistency between periods and with similar organisations,
measuring what was intended. For example, on
surveys, data such as gender, religion and nationality are usually limited to a set of choices and open answers
are not allowed. According to survey’s requirement, any answers other than
these would be considered invalid where this is the case of most data and
should be carefully considered when determining its quality. In the
organization, staffs in every department understand what data is valid or
invalid to them, therefore requirements must be utilized when accessing data
quality.

 

3.      Reliability

If the data source is reliable the organisation does
not need to spend as long or as much money checking the validity of the data. It
will be from a valid and trusted source. For example, Wikipedia is not
considered a reliable source as the information is not checked and anyone can
add to the site.

 

4.     
Timeliness

Data should not be a
reflection of an earlier state of affairs and should
be up to date as possible at or near the time of the event
Timeliness is determined as a very context oriented as the data attributes and
values should be detailed at the correct level, so the more timely the data,
the more costly and difficult to produce. Nowadays, living in
high technological advances, by having out-of-date information will lead a
company to achieve their goals and help them survive in a competitive area. For
example, broker who buy and sell shares need to have very up to date information
because share prices change so rapidly.

 

5.     
Relevance

Data need to be relevant to its intended use where
it is directly related to the business need. For example, an organization
purchases market research results from a survey execute in Malaysia. Although
the information gathered may be accurate and up to date it may not be relevant
to Singapore, where the company does most of its business, because trends,
buying habits and spending pattern are different.

 

6.     
Completeness

Completeness is as necessary as accuracy
when inputting data into a database where data
requirements need to be clearly specified due to the information needs of the
organisation and data collection processes matched to these requirements. Incomplete
data can be damaging as inaccurate data as it may give an organisation a
misleading picture on which they base management decisions. For example, sales
data taken from only three quarters of the year may give distorted picture of
an average business performance.

 

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