It all Starts with

matchit^®

If you are going to unify customer data - you need an engine built from the ground up for that purpose.

Our Approach to Data Matching is Like Nothing You’ve Seen Before

We completely reimagined how contact data matching should work, and created a unique matching engine built from the ground up specifically for the challenges of working with customer data. We eliminated the complexity, difficulty, and uncertainty of traditional matching processes, to enable users of all skill levels to quickly build and deploy highly accurate contact data matching models with greater accuracy and in a fraction of the time of conventional fuzzy matching methods.

We did this by building an engine that performs an interconnected three stage process, where the AI automatically analyzes, normalizes and tokenizes data- builds matching models, and intelligently Grades, Scores and Groups Matches. Each stage feeds the next, intelligently applying multiple forms of data matching and analysis to the entire contact record.

Why matchit? Because your matching logic can’t handle this!

matchit is amazingly tolerant to the wide variations and nuances specific to customer data – even on large datasets from disparate sources! Unlike ‘traditional’ applications, matchit doesn’t require data extraction, transformation, standardization, correction or manipulation prior to matching, and it doesn’t even require two different data sources to be normalized into a standard format or a target database! It doesn’t even need the same number of address lines in each record to compare the addresses.

How do we do it?

matchit performs an analysis to better understand the data in each contact record.

The AI looks at patterns in the data, the nature and position of words, classifies word types, creates word associations, and identifies where it is poorly formatted, or has incomplete or uncertain information. It provides a description of the data that informs the engine of the trustworthiness of the record. Each result feeds the engine values that are used to measure the quality of every record.

Later during the match scoring stage when record ‘A’ is compared to record ‘B’ this logic helps matchit understand how the data was input, what the overall quality of that input was, what types of issues were identified, and then… how to grade suspect matching records.

One Engine - Multiple Techniques

Phonetics

Bower and Bauer

Hernández and Hernandes

Muhammad, and Mohamed

Non-Phonetic Similarity

Street & St, Straße & Str.

Auto, Motors and Car

1 = One, First , 1st

Abbreviations

Turner Broadcasting Company ~ TBC

LLC = Limited Liability Corporation

Typographic

Wilson, Wislon & Wilsn

95128 ~ 91528

7350 ~ 07350

Misalignment

Jose Gonzalez

Gonzalez Jose

560 Main St Ste 106

Suite 106 560 Main St

Noise

Inc, Incorporation, LLC

Insurance, Assurance

The State Farm Insurance Company of California LLC

Synonymns

Michael = Mike Michel, Mickey, and Mikhael

Jacqueline = Jacklynn, Jaclyn, Jackie

Parsing &Restructuring

|Mr Jose R Gonzalez Jr MMD | =

|Mr | Jose | R | Gonzalez | Jr | MMD |

| AtlantaGA30305 |

| Atlanta | GA | 30305 |

International DataUnicode - Transliteration

أبراج الاتحاد = 'abraj al etihad

ਬਲਰਾਜ = Balarāja
にこらす = Nikkarasu

matchit is designed to be intelligent – applying all forms of data matching to the entire contact record to achieve results that are human-like in perception.

Deploying multiple algorithms, lexicons, and processes, matchit intelligently applies them to ensure that all types of difference are detected. Essentially, taking a multi-dimensional view of the data, never relying on any single item of data being correct or consistent!

Say what? I've got to see this!

The Result

1

Amazing
Accuracy

The scoring logic is light-years beyond what you find in traditional solutions.
Tested to deliver as much as 90% more accurates matches on CRM customer data than traditional solutions.

2

Near Zero
Data Wrangling

Seamlessly Unifies Multiple Data Sources! matchit doesn’t require data to be cleansed, normalized or transformed prior to matching. It seamlessly matches data in different formats, and it doesn’t even require different data sources to be blended into a common format or a target database!

3

Smart
Matching

matchit can recommend the ideal matching configuration and automatically configure itself. As data is read in (either in batch or streamed), the Automated Entity Resolution AI analyzes the inputs to determine the best matching configuration for that data. It can even run different matching models simultaneously ( e.g Business, Individual, Household, etc.).

4

Automatic
Data Normalization

Even though the matchit doesn’t require data extraction, transformation or normalization prior to matching, that doesn’t mean it can’t do it! It’s an incredibly powerful normalization engine built specifically for the challenges of contact data.

5

Surprisingly
Easy to Deploy

No data scientist required! matchit is easy to deploy, and supports multiple use cases. The logic is pre-tuned for most data requirements, and can be up and running in a fraction of the time of conventional solutions.

The 360Science Secret Sauce

For most users and decision makers, how 360Science works isn’t as important as how well it works. However, for IT decision makers, we know you’ll want to learn more about the data science behind matchit to understand better why 360Science is one of the most innovative customer data analytics solutions available today.

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