Everyone agrees they need better quality data. Now you can finally make it happen.


Say goodbye to manual data clean-up. Leverage the most sophisticated data-quality engine on the planet to not just get your data clean, but keep it clean for good.

Data Quality Made Easy

If you can write a formula in Excel, you can leverage the full power of Profisee’s data quality engine to  define and manage validation and assignment rules.

Measure Your Progress

Profisee makes it easy to measure and track the quality of your data so that the business can see the value of data management.

Easily embed reports of address and email verification into any view or dashboard

Understand and communicate the effectiveness of data stewardship

Industry-leading matching

Learn how Profisee’s similarity graph matching engine streamlines data quality management.

Enrich and Validate 
Your Data

Profisee helps you make your data even better with integrations with best-in-class services like Google, Melissa, Loqate, Dun & Bradstreet and more.

Name: Damon Sharpe

Address: 1864 S Granby


State: CO

ZIP: 80012

Name: Damon Sharpe

Address: 1864 S Granby St
City: Aurora

State: CO

ZIP: 80012-5736
Results: AS01, CM01, CS01
Results Definition:
AS01: Address fully verified – address is valid and deliverable according to official postal agencies. CM01: COA Match: A COA was found for…

Data Quality FAQs

In the context of MDM, data quality is a measure of whether enterprise data is fit for its intended use or purpose or if it correctly represents the real-world construct it describes. Organizations often ensure enterprise data is free of duplicates, is consistent, conforms to various standards/formats and accurately meets specific requirements.

Data quality is usually evaluated across a range of dimensions, which include the data’s uniqueness, consistency, precision, conformity, timeliness and accuracy, validity and integrity. Issues with any of these dimensions would constitute a data quality problem. For example, if a customer’s address is outdated, you might send important correspondence to the wrong address. Likewise, if a customer’s address does not conform to the post office’s record of their address, mail intended for them might be delivered to someone else.

Bad data quality is not very healthy for business, and there are countless benefits of improving data quality. Some examples include being able to better predict fluctuations in sales throughout the year, reduce procurement costs from suppliers, maintain compliance with privacy regulations like GDPR and CCPA, and more effectively manage risk when making short- and long-term decisions.

Data validation is the process of ensuring that data is accurate, complete and consistent. It involves checking data for errors, identifying anomalies and confirming that it meets specific criteria or standards. Data validation is important for several reasons, including ensuring data accuracy, maintaining compliance and improving operational efficiency.

Profisee MDM offers a number of features to help organizations improve and maintain data quality. Features like match, merge and survivorship let users break down data silos and enrich records, and a native, bidirectional integration with Microsoft Purview supports unified data governance by enforcing governance standards and remediating deficient data. Profisee also supports integrations with services like Google and Dun & Bradstreet for data enrichment and validation.



View More

Data Stewardship

View More

Data Governance

View More

Relationship Management

View More

Matching & Survivorship

View More

Data Quality

View More


View More


Complete the form below to request your spot at Profisee’s happy hour and dinner at Il Mulino in the Swan Hotel on Tuesday, March 21 at 6:30pm.


MDM vs. MDS graphic
The Profisee website uses cookies to help ensure you have the best experience possible.  Learn more