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 …
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