By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    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
    hire a marketing agency with a background in data analytics
    5 Reasons to Hire a Marketing Agency that Knows Data Analytics
    7 Min Read
    predictive analytics for amazon pricing
    Using Predictive Analytics to Get the Best Deals on Amazon
    8 Min Read
    data science anayst
    Growing Demand for Data Science & Data Analyst Roles
    6 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: The Two-Headed Monster of Data Matching
Share
Notification Show More
Aa
SmartData CollectiveSmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Uncategorized > The Two-Headed Monster of Data Matching
Uncategorized

The Two-Headed Monster of Data Matching

JimHarris
Last updated: 2009/06/04 at 3:37 AM
JimHarris
7 Min Read
SHARE

Data matching is commonly defined as the comparison of two or more records in order to evaluate if they correspond to the same real world entity (i.e. are duplicates) or represent some other data relationship (e.g. a family household).

Contents
I Fought The Two-Headed Monster…I Fought The Two-Headed Monster……And The Two-Headed Monster WonThe Harsh Reality is that Monsters are RealAre You Fighting The Two-Headed Monster?Related Articles

Data matching is commonly plagued by what I refer to as The Two-Headed Monster:

  • False Negatives – records that did not match, but should have been matched
  • False Positives – records that matched, but should not have been matched

I Fought The Two-Headed Monster…

On a recent (mostly) business trip to Las Vegas, I scheduled a face-to-face meeting with a potential business partner that I had previously communicated with via phone and email only. We agreed to a dinner meeting at a restaurant in the hotel/casino where I was staying. 

I would be meeting with the President/CEO and the Vice President of Business Development, a man and a woman respectively.

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

I was facing a real world data matching problem.

I knew their names, but I had no idea what they looked like. Checking their company website and LinkedIn profiles didn’t help – no photos. I neglected to get their mobile phone numbers, however they had mine.

The restaurant was …

Data matching is commonly defined as the comparison of two or more records in order to evaluate if they correspond to the same real world entity (i.e. are duplicates) or represent some other data relationship (e.g. a family household).

Data matching is commonly plagued by what I refer to as The Two-Headed Monster:

  • False Negatives – records that did not match, but should have been matched
  • False Positives – records that matched, but should not have been matched

I Fought The Two-Headed Monster…

On a recent (mostly) business trip to Las Vegas, I scheduled a face-to-face meeting with a potential business partner that I had previously communicated with via phone and email only. We agreed to a dinner meeting at a restaurant in the hotel/casino where I was staying. 

I would be meeting with the President/CEO and the Vice President of Business Development, a man and a woman respectively.

I was facing a real world data matching problem.

I knew their names, but I had no idea what they looked like. Checking their company website and LinkedIn profiles didn’t help – no photos. I neglected to get their mobile phone numbers, however they had mine.

The restaurant was inside the casino and the only entrance was adjacent to a Starbucks that had tables and chairs facing the casino floor. I decided to arrive at the restaurant 15 minutes early and camp out at Starbucks since anyone going near the restaurant would have to walk right past me.

I was more concerned about avoiding false positives. I didn’t want to walk up to every potential match and introduce myself since casino security would soon intervene (and I have seen enough movies to know that scene always ends badly). 

I decided to apply some probabilistic data matching principles to evaluate the mass of humanity flowing past me. 

If some of my matching criteria seems odd, please remember I was in a Las Vegas casino. 

I excluded from consideration all:

  • Individuals wearing a uniform or a costume
  • Groups consisting of more than two people
  • Groups consisting of two men or two women
  • Couples carrying shopping bags or souvenirs
  • Couples demonstrating a public display of affection
  • Couples where one or both were noticeably intoxicated
  • Couples where one or both were scantily clad
  • Couples where one or both seemed too young or too old

I carefully considered any:

  • Couples dressed in business attire or business casual attire
  • Couples pausing to wait at the restaurant entrance
  • Couples arriving close to the scheduled meeting time

I was quite pleased with myself for applying probabilistic data matching principles to a real world situation.

However, the scheduled meeting time passed. At first, I simply assumed they might be running a little late or were delayed by traffic. As the minutes continued to pass, I started questioning my matching criteria.

…And The Two-Headed Monster Won

When the clock reached 30 minutes past the scheduled meeting time, my mobile phone rang. My dinner companions were calling to ask if I was running late. They had arrived on time, were inside the restaurant, and had already ordered.

Confused, I entered the restaurant. Sure enough, there sat a man and a woman that had walked right past me. I excluded them from consideration because of how they were dressed. The Vice President of Business Development was dressed in jeans, sneakers and a casual shirt. The President/CEO was wearing shorts, sneakers and a casual shirt.

I had dismissed them as a vacationing couple.

I had been defeated by a false negative.

The Harsh Reality is that Monsters are Real

My data quality expertise could not guarantee victory in this particular battle with The Two-Headed Monster. 

Monsters are real and the hero of the story doesn’t always win.

And it doesn’t matter if the match algorithms I use are deterministic, probabilistic, or even supercalifragilistic. 

The harsh reality is that false negatives and false positives can be reduced, but never eliminated.

Are You Fighting The Two-Headed Monster?

Are you more concerned about false negatives or false positives? Please share your battles with The Two-Headed Monster.

Related Articles

Back in February and March, I published a five part series of articles on data matching methodology on Data Quality Pro. 

Parts 2 and 3 of the series provided data examples to illustrate the challenge of false negatives and false positives within the context of identifying duplicate customers:

  • Identifying Duplicate Customers (Part 2): False Negatives
  • Identifying Duplicate Customers (Part 3): False Positives

Link to original post

TAGGED: data matching, data quality
JimHarris June 4, 2009
Share This Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Data Ethics: Safeguarding Privacy and Ensuring Responsible Data Practices
Data Ethics: Safeguarding Privacy and Ensuring Responsible Data Practices
Best Practices Big Data Data Collection Data Management Privacy
data protection for SMEs
8 Crucial Tips to Help SMEs Guard Against Data Breaches
Data Management
How AI is Boosting the Customer Support Game
How AI is Boosting the Customer Support Game
Artificial Intelligence
AI analytics
AI-Based Analytics Are Changing the Future of Credit Cards
Analytics Artificial Intelligence Exclusive

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.

ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data

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?