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The matchit® Methodology

Matching Framework

The foundation of our matching software, matchit® is designed specifically to deliver results that mirror human perception – automatically, at scale and without preprocessing.
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An AI Matching Engine Designed for Contact and Business Data

Using purpose-built Artificial Intelligence, proprietary phonetic and fuzzy matching algorithms, context-sensitive lexicons, and a scoring engine made for contact data – matchit defeats the errors, inconsistencies and challenges commonly found in contact and business data. Like these…

Advanced phonetic data matching for contact records
Legacy Solutions

The Problem with Conventional Solutions

Conventional matching solutions require a user to define matching logic by using a combination of functions and off-the-shelf fuzzy algorithms, used to produce an alphanumeric value. This alphanumeric value, or ‘match key’, forms the basis for comparing two records together and ultimately finding matches.

Relying solely on extended match keys to find matching records presents two major problems:

  • Match keys require unusually clean data that has been standardised, validated, and conforms to a consistent layout. An expert user or data scientist might be required to prepare messy data – these people are scarce and waiting for them can cause significant delays.
  • The errors commonly found in contact and business data will cause your matching logic to break all too often, regardless of how much cleaning, standardising, and restructuring you do.

What Makes matchit Different

Unlike conventional matching solutions, matchit doesn’t rely on extended match keys to find a match. Instead, matchit compares larger groups of records contextually, using all the relevant attributes of your data to get a highly granular match score which reflects the similarity between records.

Sound familiar? It’s the same process humans use to make comparisons.

Where's the AI?

Just inventing a Matching Engine that thinks like you do but at scale, wasn’t enough

To enable matchit to produce answers faster, we knew we had to remove the need for manual preprocessing and focus on accessibility for people who don’t live and breathe data. To achieve this, we tapped into Artificial Intelligence methods.

Natural Language Processing (NLP) refers to AI methods concerned with understanding human language as it might be spoken or heard. Using NLP techniques like lexical semantics, matchit develops an understanding of your data based on what it is and not where it resides in a table.

Like you, matchit knows about short forms of names (like Tony for Anthony) and acronyms (such as BBC). It also understands that job titles, company names etc. are often entered in the address lines and the myriad of other data entry issues that often arise.

Artificial Intelligence

How matchit Succeeds where Conventional Matching Solutions Fail.

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THE MATCHING PROCESS

A Smarter Way to Match

01 / 04

Making Sense of Your Data

NORMALISATION

It all starts as your data enters matchit, where it is first normalised. By understanding the language of contact data, matchit breaks down complex, concatenated fields into their constituent parts. It handles foreign character sets and relocates misplaced data as necessary. The quality of fields like name, address, and email are assessed and the resulting quality scores give matchit guidance on the trustworthiness of individual data attributes. It may be garbage in, but that doesn’t mean it has to be garbage out!

Toekenisation

02 / 04

How Do You Pronounce That?

A TRUE PHONETIC ALGORITHM

After normalisation, matchit uses intelligent phoneticisation. Once its isolated values like first name, company name, street name, and town name, it can generate phonetic tokens for these fields to help cope with errors in your data.

Instead of using off-the-shelf algorithms like Soundex, or Metaphone 3 that were never designed for the nuances of contact and business data, matchit uses a proprietary phonetic algorithm that breaks each word into syllables and works out the sound of each syllable.

While picking up names that sound the same but are spelt quite differently like Shaw and Shore, matchit’s phonetic algorithm rejects different-sounding names which these other algorithms equate, like Mason and MacEwan.

03 / 04

Finding Potential Matches

CANDIDATE GROUPING

After matchit has made sense of the data, it uses the normalised and tokenised values to seek out potentially similar records. It isn’t finding matches yet, but simply identifying groups of records that are worth comparing.

Unlike conventional solutions, this doesn’t depend on any single data point being reliably accurate, consistent, or even present. Using the values generated from the previous steps, matchit is able to compare two records that may have nothing exactly the same.

Why candidate groups? The short answer is scale. matchit can run efficiently on over a billion records and perform real-time lookups on massive datasets. If it had to compare every record in the file with every other record, this wouldn’t be possible even on much smaller files.

Candidate Grouping

04 / 04

Reflecting Human Perception at Scale

CONTEXTUAL SCORING

Unlike humans, matchit doesn’t get tired – its job is to enable you to accept the vast majority of matches without having to review them, so there are very few in the grey area that you might want to check. Here’s how it does this…

As candidate groups are created, matchit’s scoring algorithms compare the records contextually. All the relevant data is graded for similarity and assigned a component score for each aspect of the data.

Where conventional approaches rely on the comparison and scoring of match keys, matchit is comparing and scoring the contact name, company name, address, postcode, telephone, email, website, and even custom fields individually – so you can score things like account numbers, date of birth, car registrations or any other information you hold.

Finally, the component scores are brought together into a single composite score which establishes the overall similarity between the two records.

Job done? Not quite, there’s more going on under the hood of matchit – grouping of matches, bridging prevention, master record identification etc. To read more about this, just click the button below!

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Enterprise-Grade Architecture

Built for a variety of deployments and use cases, matchit easily scales with the growing needs of a business.

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