are the business costs or risks of poor data quality?



                                  From past few
decades Information technology has been growing so fast due to rapid changes in
business requirement. Every business needs to maintain a data structures
including past and present to handle the business day to day transactions. Data
is a important keyword in business world and to arrange that all data in a
formal order is another burden. To maintain quality data is very important if
data mismatched then business may get fail in planning and decision making. If
the data volume increased then there is a risk of data complexity.

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                                 In recent days
organisations are preferring larger and complex set of information this means
that risk of poor data quality increases. Poor data maintenance can imply huge
sequence of negative consequences which may harm the reputation or goodwill of
the company. Poor data quality of the organisation also increases the
operational costs because time and efforts are spent detecting detecting and
correcting errors. Data are the critical and very important inputs almost to
the all business, it helps in planning and decision making.


                                  In most of
the cases corporate leaders are always busy to capture useful business data as
a priority. The challenges imposed by taking the quality data can be daunting
the advantages and chances that qualitative data enable and help to improve the
quality so that business can directly use that information in decision making
process. Poor data quality is a huge problem for many organisations.


Benefits of good quality data:


·       Better
the data quality, the more confidence management will get in decision making

·       Good
quality data allows more productivity to the staff

·       Help
to meet targets as per the business requirements

·       More
accurate output


Costs of poor data quality:


·       Companies
reputation may damage

·       Lost

·       They
will missed greater opportunities

·       Low


is data mining?


                                      Data is a
formal way of gathering the information depends on the business needs. To
arrange that informal information into a meaningful and formal way is called as
information. Data is a set of raw materials in unsystematic order.
Organisations needs to convert that data into a meaningful information. Data
mining is a technique which helps the business how to find the useful knowledge
or information in all that data preprocessing pattern. Data mining is nothing
but the classification and gathering of data.


mining is a gathering of data which helps the business in various applications.
Data mining helps in discovering knowledge from various data collected by
organisations. The primary objective of data mining is gather the useful and
effective information from large data sets and provide better decision making
policy for business.


is text mining?


mining is a process to convert unstructured information into a meaningful
format so that enterprises are readily capture the available data. Text mining
is a analysis of data stored in natural language format. It helps to solve
business problems by using various techniques. Text mining use text analytic
software which helps by transposing words and phrases in unstructured data into
numerical values. The major objective of text mining is to process the textual
information into a meaningful indices from the text so that it will be easily
accessible to all.

motivation behind Text Mining is to process unstructured (literary) data,
separate significant numeric lists from the content, and, accordingly, make the
data contained in the content open to the different information mining (factual
and machine learning) calculations. Data can be removed to determine synopses
for the words contained in the archives or to register outlines for the reports
in view of the words contained in them. Consequently, you can dissect words,
groups of words utilised as a part of reports, and so forth., or you could
examine records and decide likenesses between them or how they are identified
with different factors of enthusiasm for the information mining venture.