Software Alternatives & Reviews

RingLead VS Microsoft Data Quality Services

Compare RingLead VS Microsoft Data Quality Services and see what are their differences

RingLead logo RingLead

RingLead offers a complete end-to-end suite of products to clean, protect, and enhance company and contact information.

Microsoft Data Quality Services logo Microsoft Data Quality Services

Data Quality
  • RingLead Landing page
    Landing page //
    2023-10-05
  • Microsoft Data Quality Services Landing page
    Landing page //
    2023-10-02

RingLead

Categories
  • Sales Tools
  • Lead Generation
  • B2B
  • Business & Commerce
Website ringlead.com

Microsoft Data Quality Services

Categories
  • Data Integration
  • Sales Tools
  • OS & Utilities
  • Lead Generation
Website docs.microsoft.com

RingLead videos

Capture by RingLead for Pipeliner CRM

Microsoft Data Quality Services videos

Live Action: Microsoft Data Quality Services

Category Popularity

0-100% (relative to RingLead and Microsoft Data Quality Services)
Sales Tools
83 83%
17% 17
Data Integration
0 0%
100% 100
Data Hygiene
78 78%
22% 22
Lead Generation
100 100%
0% 0

User comments

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What are some alternatives?

When comparing RingLead and Microsoft Data Quality Services, you can also consider the following products

Clearbit - Clearbit provides Business Intelligence APIs

WinPure Clean & Match - WinPure Clean & Match is the worlds best data cleansing & data matching software for sophisticated matching, cleansing and deduplication.

Lead411 - Lead411 is the Top B2B Marketing & Sales Intelligence data platform for prospecting, contact enrichment, Bombora intent and list building. Get Verified Emails, Direct Dials and Company Intel to improve your sales pipeline.

Oracle Data Quality - Overview of Oracle Enterprise Data Quality

Cloudingo - Cloudingo - a cloud-based SaaS, connects to Salesforce and allows system administrators to scan their entire database for similar or duplicate records.

SAS Data Quality - SAS Data Quality gives you a single interface to manage the entire data quality life cycle: profiling, standardizing, matching and monitoring.