Question 1

How accurate does identity resolution need to be?

Accepted Answer

It depends on what a wrong answer costs. A false merge in marketing is very different from one in healthcare or finance, and the best way to set thresholds is to test on a hand-checked sample.

Question 2

Who should own the match rules, the data team or the business?

Accepted Answer

The business defines what a match means, the data team builds the rules, stewards review borderline cases, and business owners check a sample before merging.

Question 3

How do you resolve identities when data sits in different systems with no common identifier?

Accepted Answer

Disparate sources are not a barrier. WinPure imports each one into a single project, whether it is a CSV export from the CRM, an Excel file from billing, a JSON or XML extract, or a table from a SQL Server database, and works on a copy so the original systems stay untouched. You then choose the columns to match across the sources, such as name, email, phone number and postcode, and map them to each other even when each system names them differently. WinPure groups the records that belong to the same person and shows why they matched, so you can review the results before anything is merged.

Question 4

Do you need to clean the data before entity resolution?

Accepted Answer

It is recommended as a best practice to clean your data before matching it for entity resolution. In WinPure the cleaning happens in the same project as the matching, so there is no separate tool or export in between.

Question 5

How do you decide which record becomes the golden record?

Accepted Answer

With rules where the business has them, and scoring where it does not. WinPure&#8217;s master record settings let teams prefer the record where a field such as VAT number is populated, or where a value is the longest or highest, or take the most populated record from a chosen table. Where no rule applies, SmartMaster AI™ scores each record in the group on completeness, consistency and reliability and selects the master.

Question 6

Do data stewards need programming skills to resolve identities and entities on WinPure?

Accepted Answer

While WinPure Clean&Match is a no-code platform, it is designed for data analysts and IT managers to program and structure match configurations around their specific requirements. The software offers full control so you can work on your data just the way you like it!

Question 7

What data sources can WinPure Clean&Match import from?

Accepted Answer

Text/CSV, Excel, JSON, JSONL, XML and SQL Server, and any custom data source. We also help with creating a connector if the need arises.

Question 8

Is there a cloud or SaaS version?

Accepted Answer

WinPure does not offer a SaaS version, and that is a deliberate choice. Clean & Match Enterprise is a locally deployed platform, so the organisation decides where it runs: on a desktop, on a server, in a virtual machine or air-gapped network, or in its own cloud environment on Azure or AWS. Wherever it is installed, data is processed inside the organisation&#8217;s own environment and is never sent to WinPure or a shared hosted service, which is what gives regulated and security-conscious teams control over their data. Teams that need embedded or automated processing inside their own systems can also use the WinPure API.

Question 9

How is WinPure licensed?

Accepted Answer

Annual licence, volume-based tiers, no per-record or API fees. It also covers which tiers include audit logging and automation.

Question 10

How long does it take to see results?

Accepted Answer

First run within a day, personalised onboarding, and the 30-day free trial.

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