Data Hygiene

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Data Hygiene
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Information is a source of learning. But unless
it is organized, processed, and available to the
right people in a format for decision making, it
is a burden, not a benefit.C. William Pollard,
The Soul of the Firm
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CONCLUSION
INTRODUCTION
WHY DIRTY DATA
CLEANING STEPS

• Customer Relationships
• Anomaly Nightmare
• Parsing Matching
• Correcting Consolidating
• Standardizing
• USPS Services

INTRODUCTION
WHY DIRTY DATA
CLEANING STEPS
CONCLUSION
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Talking to your customers
CONCLUSION
INTRODUCTION
WHY DIRTY DATA
CLEANSING STEPS

• Customer Communication
• Face-to-Face
• Telephone
• Direct Mail
• E-Mail
• Variety of Purposes
• Sales
• Marketing
• Billing
• Customer Service

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Why Dirty Data?
CONCLUSION
INTRODUCTION
WHY DIRTY DATA
CLEANSING STEPS

• Multiple data sources
• Compiled Data from marketing, accounting,
  customer service, online, etc
• Survey Data
• Mailing Lists
• Transactional Data
• Registration Data
• Complaints

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Why Dirty Data?
CONCLUSION
INTRODUCTION
WHY DIRTY DATA
CLEANSING STEPS

• Lack of Standard Business Rules
• Multiple formats
• Multiple names within one field
• One name in two fields
• Name and address in same field
• Different addresses for the same customer
• Different spellings (or misspellings) for the
  same customers

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CONCLUSION
INTRODUCTION
WHY DIRTY DATA
CLEANSING STEPS
Anomaly Nightmare
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Cleaning Steps
CONCLUSION
INTRODUCTION
WHY DIRTY DATA
CLEANSING STEPS
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Parsing
CONCLUSION
INTRODUCTION
WHY DIRTY DATA
CLEANSING STEPS
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Correcting
CONCLUSION
INTRODUCTION
WHY DIRTY DATA
CLEANSING STEPS
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Standardizing
CONCLUSION
INTRODUCTION
WHY DIRTY DATA
CLEANSING STEPS
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USPS Services
CONCLUSION
INTRODUCTION
WHY DIRTY DATA
CLEANSING STEPS

• Address Element Correction (AEC)
• Corrects and standardizes address elements.
• Misspellings
• Directionals (e.g. NW, South, etc..)
• Suffixes (e.g. Street, Avenue, Road, etc)
• Nonstandard abbreviations
• Missing Information (e.g. apt or suite )
• Provides reason why an address is incorrect

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USPS Services
CONCLUSION
INTRODUCTION
WHY DIRTY DATA
CLEANSING STEPS

• Delivery Sequence File (DSF)
• Address Validation
• Eliminates undeliverable addresses
• Zip4 Coding
• Carrier Route
• Delivery Sequence
• Enhances Mail Delivery and List Quality
• Business vs. Residential Indicator
• Location Occupancy and forwarding addresses

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USPS Services
CONCLUSION
INTRODUCTION
WHY DIRTY DATA
CLEANSING STEPS

• Locatable Address Conversion Service (LACS)
• Assists 911 Emergency Services
• Replaces Rural Routes and/or Box Numbers with
  Physical Locations
• New Land Development Addresses

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USPS Services
CONCLUSION
INTRODUCTION
WHY DIRTY DATA
CLEANSING STEPS

• National Change of Address (NCOA)
• 40 Million Americans Change Addresses Annually
  17 individuals, 22 businesses
• 3 year rolling file of business, individual, and
  household moves, updated weekly.
• Reduces Undeliverable and Duplicate Mail pieces

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Parsing, Correcting, Standardizing
CONCLUSION
INTRODUCTION
WHY DIRTY DATA
CLEANSING STEPS
TITLE
FIRST
CONC.
LAST
GENER.

NAME LINE
William
Mr. Bill St. John III 101 S.
Main Strete Sant. Louis, MO 63181
HSNO
ST-NM
ST-TYPE
ST-DIR
St.
STREET LINE
CITY
STATE
POST
St.
63118
GEOG. LINE
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Matching
CONCLUSION
INTRODUCTION
WHY DIRTY DATA
CLEANSING STEPS
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Consolidating
CONCLUSION
INTRODUCTION
WHY DIRTY DATA
CLEANSING STEPS
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Consolidating
CONCLUSION
INTRODUCTION
WHY DIRTY DATA
CLEANSING STEPS

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Why is data hygiene important?
CLEANSING STEPS
INTRODUCTION
WHY DIRTY DATA
CONCLUSION

• Reduces Costs
• More automation Less Manual Review
• Maximize Postal Savings
• Eliminates duplication costs (paper, printing,
  postage, storage, data management)

• Enhances Customer Relationships
• Consolidated Customer View
• Facilitates Data Mining CRM
• Increased data accuracy
• Communications reaches your customers
• Improves Response Rates

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The Reality ONE Customer
CLEANSING STEPS
INTRODUCTION
WHY DIRTY DATA
CONCLUSION

Account No.83451234
Policy No.ME309451-2
Transaction B498/97
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Software Companies

• First Logic
• Group One
• Ascential
• Trillium Software