TY  - JOUR
T1  - Statistical Data Quality Model for Data Migration Business Enterprise
AU - Manjunath, T.N. AU - Hegadi, Ravindra S. 
JO  - International Journal of Soft Computing
VL  - 8
IS  - 5
SP  - 340
EP  - 351
PY  - 2013
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2013.340.351
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2013.340.351
KW  - Data quality
KW  -statistical methods
KW  -data migration
KW  -business enterprise
KW  -CPU time
AB  - In current information trends, state of decision making is 
  one of the important deeds for any organization or enterprise to identify them 
  in the business market in this connection, the data which is present in data 
  warehouse or decision databases should be very accurate and help them to give 
  proper decisions. When the organizations or enterprises undergo a merger/takeover 
  demands the data migration from legacy systems to modern systems/decision databases, 
  i.e., target systems. If a target/decision databases is very large to ensure 
  quality assurance of the decision database is tedious. The resource utilization 
  required to conduct full data verification is exorbitant. This research proposes 
  a mathematical model using deterministic statistical methods to reduce resource 
  utilization and assures greater data quality. The proposed method validated 
  using various data sets and volumes against man effort, CPU time, defects raised 
  and cost. It also ensures comfortable confidence for end users to rely on the 
  data quality for decision making.
ER  -