Building Making It Happen
Establishing Making-it-Happen as ‘Formal & Measurable’ Business Discipline
  Sign-in         Register
    
   Customer Data Challenges Customer Data Searching and Matching  

Execution-MiH Encyclopedia  →   Enterprise Intelligence  →  SECTION -  Data Quality  →  CHAPTER -  Customer Data Quality for Customer Relationship Management  → 

Customer Data Variations

There are unlimited variations in customer data and this sets the back-drop to the solutions for customer data quality

There are unlimited variations in customer data and this sets the back-drop to the solutions for customer data quality. The first step towards achieving customer data quality is to identify:

  • Duplicate/matching records.
  • Defective records.
  • Incomplete records

Examples of Customer Data Variations

The variations that occur in names and addresses are vast. Reasons for customer data issues and variations are covered in Customer Data Impacts

Customer Name

  • Chris, Kris, Christie, Krissy, Christy, Christine, Tina
  • Franc, Frano, Frank, Francis
  • Peter, Pete, Pietro, Piere
  • Johnson, Johnsen, Johnsson, Johnston, Johnstone, Jonson
  • Smith II, Smith jr, Smith 11, Smithjnr
  • De La Grande, Delagrande, D L Grande
  • Henry Tun Lye Aun; Mr Aun Tun Lye (Henry)
  • Frank Lee Adam; A. Frank Lee; Lee Frank
  • Patricia Jane Morris; P J Morriss; S. F. & P.J. Morris

Companies

  • B. Lamond Inc.; A & B La Monde Co; AB Lamond Incorporated; Lamond Inc.
  • International Business Machines; IBM; I.B.M.; Intnl. Bus. Machines.
  • Stanley Rutherford & Assocites; Messrs. Rutherford, Stanley and Assoc; Rutherford Assocs.
  • Abe Goldberg & Sons; Abe Boldberg and Son; Abe Godberg & sons;
  • Aba Din Inc; Mitchell Holdings dba Abadin Inc.
  • Abbotts; Abbots Accounting Services; Abott's Accountancy; Abbots Accntncy Advisory Svcs.
  • Virginia Trust Company; Trust Company of Virginia

Customer Address

  • Jackson Rd. East Hartford; 117-2a Jackson Rd East, Hartfrd;
  • 2a East Jackson, Hartford; 117a Jackson Rd, E. Hartford
  • Ground Floor 192 Aberdeen St South Head; Grd. Fl. 192 Aberdeen St Southhead; 192/1 Aberdeen Sreet Sth. Hd.
  • Suite 9A, The Russell Center, Washington Plaza, New Haven; Room 9-A Rusell Bldg, Newhaven

Dates

  • 12/14/1998; 14/12/98; 14th December 1998; 14th Dec 1998;
  • December 14th 98; 1998-14-12; 98/12/14.
  • 7/2/1996; 7/2/9600; 7/2/96.

Phone Numbers

  • 900-869-1481; 90-08-00-86-91-481; 8691481 ext 67;
  • (0) 7778691481; (+44)777 8691 1481; 869 1481.

Null Values

  • Not Known; Unknown; Missing; DOA; John Doe; Baby Doe; Jane Doe; Corpse; XXXXXX
  • No Middle Name; No Initial; NMI; NMIK; Nomiddlename, 00-00-00; 99-99-99; - - -;

Character Variations in customer data

  • Typos: SMITH~SIMTH, JOHNSON~JPHNSON
  • Noise:THOMPSON~TH9MP2ON
  • Concatenations:ANDREWS~ANDREWSJR
  • Truncation/Initials:IVANOV~IVANOVSKY

Cultural Variations in customer data

  • Titles: Dr., Rev, Haj, Sri., Col.,…
  • Suffixes: -aldin, -oglu, -skii/-skaya, …
  • Prefixes: Fitz, O', De La, Abdul, …
  • Qualifiers: Jr., fils, neto, sobrinho,Ph.D., …
  • Infixes:de, vde., …
  • Nicknames: Johnny, Betty, Alyosha, Paco, …
  • Cultural Variants: Imhemed/Mohamed/Mohd

Phonetic variation in customer data

  • 'sound alikes':Leighton~Layton
  • Cross-transcriptions:Chernobyl~Tschernobil
  • Similar-sounding:Phillips~Billups

Customer Data Variations as a global challenge

The above examples are taken from primarily US context. In the era of globalization, the challenge is exponentially increased with Asian & European names, which carry a greater and different kind of complexities.

Before we get onto the next chapter of how to search for the variations, here is a word of caution on relying too much on automated software. One should be using broad and very high level assumptions & heuristics in deploying these tools for searching & cleansing.

 

   Customer Data Challenges Customer Data Searching and Matching  
 
All Topics in: "Customer Data Quality for Customer Relationship Management" Chapter
 Customer Data Quality Impacts →  Customer Data Challenges →  Customer Data Variations →  Customer Data Searching and Matching →  Customer Data Correction and Techniques →  Customer Data Augmentation and Enrichment → 
 

Was this page helpful?
If you like it ? share it !
Digg
Digg
Reddit
Reddit
Del.icio.us
Delicious
Google
Google
Live
Live
Facebook
Facebook
Slashdot
Slashdot
Netscape
Netscape
Technorati
Technorati
Stumbleupon
Stumbleupon
Spurl
Spurl
Furl
Furl
Blogmarks
Blogmarks
Yahoo
Yahoo
Plugim
Plugim
Squidoo
Squidoo
BlinkBits
BlinkBits
 
CONTENT ZONE
Data Quality

Featured Pages
Metadata Repository Extraction Design
Data Entry Input Form Controls
Fix Business Intelligence at functional level first
Metadata Objective and purpose- Why Metadata

Make 'Executable' Strategy
Maximize Results
Maximize People
Manage Execution

Featured Pages
Data Mining Techniques- Predictive Modeling
BI Competency Centre- Strategize Phase
Data Mart Fact Table Grain Matrix in DW
Data Warehouse Design Phase