Match Data Pro’s Data Profiler gives you instant visibility into your data’s health. It examines columns for quality issues like null values, duplicate entries, invalid formats, and outliers—analyzing across the four dimensions: Accuracy, Uniqueness, Conformity, and Precision. This sets the stage for powerful cleansing and matching workflows.

Each column is scored across 11+ detailed statistical measures, including:

• Pattern detection—identifies expected vs. unexpected formats (e.g., state codes vs. state names)

• Null values—percentage of missing or blank entries

• Uniqueness—tells you how many repeated values exist, hinting at duplicates

• Precision—finds outliers in string length, date range, etc.

• Accuracy – checks formatting and spacing issues

• Uniqueness – exposes duplicate values

• Conformity – reveals mismatches with expected data types

• Precision – detects outlier length or value entries

Yes! You can use regex-based pattern detection and custom dictionaries to flag inconsistencies like uppercase/lowercase discrepancies, misplaced delimiters, or seamless abbreviations—especially helpful for names, addresses, phone numbers, and emails.

The MDP Score aggregates key quality metrics to provide a high-level health snapshot of your data. It sits alongside total record count and runtime, helping you quickly gauge data readiness.

Profiling first lets you identify root causes—like inconsistent address formats or mixed phone structures—so AI or manual cleansing steps can be truly targeted. It ensures better accuracy and speeds up fuzzy matching while reducing wasted effort.

Whether you’re a data analyst, marketer, or business leader, this tool gives you quick actionable insights—without needing technical expertise. It streamlines data quality checks, enabling fast, confident matching and decision-making.