By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    football analytics
    The Role of Data Analytics in Football Performance
    9 Min Read
    data Analytics instagram stories
    Data Analytics Helps Marketers Make the Most of Instagram Stories
    15 Min Read
    analyst,women,looking,at,kpi,data,on,computer,screen
    What to Know Before Recruiting an Analyst to Handle Company Data
    6 Min Read
    AI analytics
    AI-Based Analytics Are Changing the Future of Credit Cards
    6 Min Read
    data overload showing data analytics
    How Does Next-Gen SIEM Prevent Data Overload For Security Analysts?
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Adventures in Data Profiling (Part 5)
Share
Notification Show More
Aa
SmartData CollectiveSmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Mining > Adventures in Data Profiling (Part 5)
Data Mining

Adventures in Data Profiling (Part 5)

JimHarris
Last updated: 2009/08/29 at 4:58 AM
JimHarris
8 Min Read
SHARE

In Part 4 of this series: You went totally postal… shifting your focus to postal address by first analyzing the following fields: City Name, State Abbreviation, Zip Code and Country Code.

Contents
Postal Address Line 1Postal Address Line 2Postal Address ValidationRelated Posts

You learned when a field is both 100% complete and has an extremely low cardinality, its most frequently occurring value could be its default value, how forcing international addresses to be entered into country-specific data structures can cause data quality problems, and with the expert assistance of Graham Rhind, we all learned more about international postal code formats.

In Part 5, you will continue your adventures in data profiling by completing your initial analysis of postal address by investigating the following fields: Postal Address Line 1 and Postal Address Line 2.

Previously, the data profiling tool provided you with the following statistical summaries for postal address:

More Read

analyzing big data for its quality and value

Use this Strategic Approach to Maximize Your Data’s Value

7 Data Lineage Tool Tips For Preventing Human Error in Data Processing
Preserving Data Quality is Critical for Leveraging Analytics with Amazon PPC
Quality Control Tips for Data Collection with Drone Surveying
3 Huge Reasons that Data Integrity is Absolutely Essential

Postal Address Summary

As we discussed in Part 3 when we looked at the E-mail Address field, most data profiling tools will provide the capability to analyze fields using formats that are constructed by parsing and classifying the individual values within the field. 

Postal Address Line 1 and Postal Address Line 2 are …

In Part 4 of this series: You went totally postal… shifting your focus to postal address by first analyzing the following fields: City Name, State Abbreviation, Zip Code and Country Code.

You learned when a field is both 100% complete and has an extremely low cardinality, its most frequently occurring value could be its default value, how forcing international addresses to be entered into country-specific data structures can cause data quality problems, and with the expert assistance of Graham Rhind, we all learned more about international postal code formats.

In Part 5, you will continue your adventures in data profiling by completing your initial analysis of postal address by investigating the following fields: Postal Address Line 1 and Postal Address Line 2.

Previously, the data profiling tool provided you with the following statistical summaries for postal address:

Postal Address Summary

As we discussed in Part 3 when we looked at the E-mail Address field, most data profiling tools will provide the capability to analyze fields using formats that are constructed by parsing and classifying the individual values within the field. 

Postal Address Line 1 and Postal Address Line 2 are additional examples of the necessity of this analysis technique. Not only are the cardinality of these fields very high, but they also have a very high Distinctness (i.e. the exact same field value rarely occurs on more than one record). Some variations in postal addresses can be the results of data entry errors, the use of local conventions, or ignoring (or lacking) postal standards.

Additionally, postal address lines can sometimes contain overflow from other fields (e.g. Customer Name) or they can be used as a dumping ground for values without their own fields (e.g. Twitter username), values unable to conform to the limitations of their intended fields (e.g. countries with something analogous to a US state or CA province but incompatible with a two character field length), or comments (e.g. LDIY, which as Steve Sarsfield discovered, warns us about the Large Dog In Yard).

Postal Address Line 1

The data profiling tool has provided you the following drill-down “screen” for Postal Address Line 1:

Field Formats for Postal Address Line 1

The top twenty most frequently occurring field formats for Postal Address Line 1 collectively account for over 80% of the records with an actual value in this field for this data source. All of these field formats appear to be common potentially valid structures. Obviously, more than one sample field value would need to be reviewed using more drill-down analysis.

What conclusions, assumptions, and questions do you have about the Postal Address Line 1 field?

Postal Address Line 2

The data profiling tool has provided you the following drill-down “screen” for Postal Address Line 2:

Field Formats for Postal Address Line 2

The top ten most frequently occurring field formats for Postal Address Line 2 collectively account for half of the records with an actual value in this sparsely populated field for this data source. Some of these field formats show several common potentially valid structures. Again, more than one sample field value would need to be reviewed using more drill-down analysis.

What conclusions, assumptions, and questions do you have about the Postal Address Line 2 field?

Postal Address Validation

Many data quality initiatives include the implementation of postal address validation software. This provides the capability to parse, identify, verify, and format a valid postal address by leveraging country-specific postal databases. 

Some examples of postal validation functionality include correcting misspelled street and city names, populating missing postal codes, and applying (within context) standard abbreviations for sub-fields such as directionals (e.g. N for North and E for East), street types (e.g. ST for Street and AVE for Avenue), and box types (e.g. BP for Boite Postale and CP for Case Postale). These standards not only vary by country, but can also vary within a country when there are multiple official languages.

The presence of non-postal data can sometimes cause either validation failures (i.e. an inability to validate some records, not a process execution failure) or simply deletion of the unexpected values. Therefore, some implementations will use a pre-process to extract the non-postal data prior to validation.

Most validation software will append one or more status fields indicating what happened to the records during processing. It is a recommended best practice to perform post-validation analysis by not only looking at these status fields, but also comparing the record content before and after validation, in order to determine what modifications and enhancements have been performed.

What other analysis do you think should be performed for postal address?

In Part 6 of this series:  We will continue the adventures by analyzing the Account Number and Tax ID fields.

Related Posts

Adventures in Data Profiling (Part 1)

Adventures in Data Profiling (Part 2)

Adventures in Data Profiling (Part 3)

Adventures in Data Profiling (Part 4)

Getting Your Data Freq On

Link to original post

TAGGED: data profiling, data quality
JimHarris August 29, 2009
Share This Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

football analytics
The Role of Data Analytics in Football Performance
Analytics Big Data Exclusive
smart home data
7 Mind-Blowing Ways Smart Homes Use Data to Save Your Money
Big Data
ai low code frameworks
AI Can Help Accelerate Development with Low-Code Frameworks
Artificial Intelligence
data Analytics instagram stories
Data Analytics Helps Marketers Make the Most of Instagram Stories
Analytics

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

analyzing big data for its quality and value
Big Data

Use this Strategic Approach to Maximize Your Data’s Value

6 Min Read
data lineage tool
Big Data

7 Data Lineage Tool Tips For Preventing Human Error in Data Processing

6 Min Read
data quality and role of analytics
Data Quality

Preserving Data Quality is Critical for Leveraging Analytics with Amazon PPC

8 Min Read
data collection with drone use
Data Collection

Quality Control Tips for Data Collection with Drone Surveying

9 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive
AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?