Get a clear picture of your data quality before making changes. WinPure’s Data Quality Insights helps analysts assess the accuracy, completeness and consistency of their datasets, making it easier to prioritise corrections and understand the quality of information across different sources.

• Analyse your data quality: Use the built in data profiling tool to examine field values, missing information, formatting inconsistencies and recurring patterns across data sources.

• Measure data quality: Use data quality scores to assess the condition of your records and identify areas that need improvement, based on priority.

• Target problematic fields: Examine column level findings to determine where specific cleaning rules and transformations should be applied.

Apply precise, consistent corrections across complex datasets using WinPure’s CleanMatrix™. With manual configuration and AI assisted cleaning available within the same workflow, analysts can choose how much control to exercise over individual transformations while processing large volumes of data efficiently.

• Clean multiple fields in a single operation: Remove unwanted characters, correct text casing, standardise formatting and transform values across different columns using a configurable cleaning matrix.

• Let AI recommend the appropriate corrections: Analyse your imported data with CleanAI™ to generate suggested cleaning operations for different fields, then review and adjust the recommendations according to your requirements.

• Apply consistent corrections across recurring imports: Save your cleaning configurations and reuse them on new datasets, ensuring the same formatting rules and data standards are maintained without configuring every operation again.

Enterprise datasets come with years of custom structures and terminology that demand careful treatment when scrubbing. WinPure lets analysts build and maintain their own correction libraries, ensuring established data standards can be applied consistently across different sources, projects and future imports.

• Correct recurring spelling and terminology variations: Create your own dictionaries of approved words, abbreviations and replacements, then use them to standardise inconsistent values across large datasets.

• Enforce consistent formats for structured data: Define reusable patterns for reference numbers, identifiers and other formatted fields so records follow the conventions required by your organisation.

• Create advanced rules for complex data errors: Build regular expressions to find, extract or replace specific text patterns, giving analysts greater control over corrections that standard cleaning operations may not cover.

Cleaner, standardised data creates a stronger foundation for identifying records that belong to the same customer, supplier or business entity. The platform’s data scrubbing tool comes with advanced matching and entity resolution, allowing teams to progress from corrected records to consolidated, reliable master data within the same platform.

• Find duplicates even when records don’t match exactly: Compare records using exact, fuzzy, phonetic and AI assisted matching to identify duplicates despite differences in names, addresses, spellings or other field values.

• Connect records that belong to the same entity: Identify and group records referring to the same record across multiple datasets. Assign a persistent unique identifier to each resolved entity to maintain consistent identities with future imports.

• Combine duplicate records into a single master record: Consolidate information from matching records and select the most appropriate values when fields conflict and create a complete, reliable master record with SmartMaster AI™.