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Chun-Ying ChuangChun-Ying Chuang
Deep PatelDeep Patel
Hsin-Ying LiuHsin-Ying Liu
Liang-Yu HuangLiang-Yu Huang
Yi-Chien LeeYi-Chien Lee
Yi-Feng HanYi-Feng Han
THE (OFTEN HIDDEN) COSTS OF POOR DATA
AND INFORMATION
Subprime mortgage meltdown
INTRODUCTION
Incorrect personal dataInaccurate credit scoresUnfamiliar with the
terms of their mortgage
Over relied on incorrect investment grade
Foreclosure proceeding
Subprime mortgage meltdownThe 2000 presidential electionMars spacecraftFort Monmouth closingTrader assistant’s errorMisgraded SATsJésica Santillán and Kaiser
PermananteDepartment of Veteran Affairs
laptopLaw enforcement and 9/11Financial reportingThe 2000 censusIntelligence
THE DIRTY DOZEN
SEVEN COMMON DATA QUALITY ISSUES
People can’t find the data they need
Incorrect dataPoor data definitionData privacy/data securityData inconsistency across sources [data “REDUNDANCY”
Too much dataOrganizational confusion
Data definitionsTechnical details of their physical storageWho may use them and permitted usagesWho may not use them and prohibited usagesTheir original sources Systems that store themDetails for gaining accessDomains that the values may takeDefinitions of what those values may meanConventions for naming the data and file
structures
METADATA
THE COST OF POOR DATA
OPERATIONS
Higher operating costLower employee moraleLower customer satisfaction
Item Unit Cost Item Total
88 “good” order
$1.00 $88.00
12 “bad” order
$10.00 $120.00
Grand Total $208.00
Harder to set & execute Fewer options to derive the required value
from data Distracts management attention Harder to align organization Strategy cannot be executed without full
support
STRATEGY
DECISION MAKING/ TACTICS
Lower trust between organizationsOrganizations tend to make poor
decisionsTend to lose salesTechnology risk increasesHarder to manage risk
DATA & IMPACTS
Current data status
Misinterpreted
30% not yield
10-25% errors
Inconsistent with sources
May be loss & theft.
Immediate consequences
Wrong or delayed decisions
30% of time on searching
Increase operation costs
Redundant or obsolete data
Political battles, failed system
Potential business lost 20%
Feared long-term consequences
Organization cannot:
Trust the data
Find new uses of data
Mine the data
Develop data-specific strategies
WHAT CAN WE DO
Pay attenteion to data in your job
What data are most important?
How good are they? Are theyccurent? Well-defined? How do we know?
How much easier if the data are 100% correct?
What to do about it?
Q & A
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