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Compare Monte Carlo and Oracle Data Quality 
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At a Glance
Market Segments
Enterprise (51.3% of reviews)
Enterprise (48.1% of reviews)
Entry-Level Pricing
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No pricing available
Monte Carlo
Star Rating
(439)4.4 out of 5
Market Segments
Enterprise (51.3% of reviews)
Entry-Level Pricing
Contact Us
Browse all 3 pricing plansOracle Data Quality
Star Rating
(54)4.0 out of 5
Market Segments
Enterprise (48.1% of reviews)
Entry-Level Pricing
No pricing available
Learn more about Oracle Data QualitySummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that Oracle Data Quality excels in data quality monitoring with a score of 8.9, while Monte Carlo's performance in this area is less impressive at 6.4. Reviewers mention that Oracle's ability to identify and correct data issues is a significant advantage for enterprises focused on maintaining high data integrity.
- Reviewers mention that Monte Carlo shines in quality of support, achieving a score of 9.3 compared to Oracle's 8.4. Users on G2 appreciate the responsiveness and helpfulness of Monte Carlo's support team, which can be crucial for mid-market companies needing quick resolutions.
- G2 users highlight Oracle Data Quality's superior ease of setup with a score of 9.0, while Monte Carlo scores 8.4. Users say that Oracle's straightforward installation process allows teams to get up and running quickly, which is particularly beneficial for larger enterprises with complex data environments.
- Users report that Monte Carlo offers better real-time alerts with a score of 8.3, compared to Oracle's 8.1. Reviewers mention that the timely notifications from Monte Carlo help teams respond swiftly to data anomalies, which is essential for maintaining operational efficiency.
- Reviewers mention that Oracle Data Quality provides robust reporting capabilities with a score of 9.0, while Monte Carlo lags behind at 7.4. Users say that the detailed reports generated by Oracle help organizations make informed decisions based on data insights.
- Users on G2 highlight that Oracle Data Quality's data integration capabilities score 8.7, making it a strong choice for enterprises needing to consolidate data from various sources. In contrast, Monte Carlo's integration features are perceived as less comprehensive, which may limit its effectiveness for users with diverse data ecosystems.
Pricing
Entry-Level Pricing
Monte Carlo
Start
Contact Us
All you need to make data reliable for one team
- Monitoring for Data Warehouse, Business Intelligence, ETL
- Incident triaging, troubleshooting and root cause analysis
- Lineage
Oracle Data Quality
No pricing availableFree Trial
Monte Carlo
No trial information availableOracle Data Quality
No trial information availableRatings
Meets Requirements
8.3
410
8.3
48
Ease of Use
8.2
417
7.8
48
Ease of Setup
8.2
281
9.0
12
Ease of Admin
8.5
148
9.2
13
Quality of Support
9.0
367
8.4
44
Has the product been a good partner in doing business?
9.3
149
8.7
13
Product Direction (% positive)
8.9
406
7.8
47
Features by Category
Functionality
9.0
258
Not enough data
8.8
258
Not enough data
7.8
237
Not enough data
8.3
246
Not enough data
7.6
241
Not enough data
7.4
243
Not enough data
Agentic AI - Database Monitoring
7.1
13
Not enough data
6.9
13
Not enough data
6.9
13
Not enough data
7.1
13
Not enough data
6.8
12
Not enough data
6.5
13
Not enough data
7.1
13
Not enough data
Data Management
8.5
51
Not enough data
8.4
47
Not enough data
8.6
51
Not enough data
7.9
49
Not enough data
Agentic AI - DataOps Platforms
6.7
5
Not enough data
6.0
5
Not enough data
6.3
5
Not enough data
6.3
5
Not enough data
6.3
5
Not enough data
Analytics
7.8
49
Not enough data
7.7
47
Not enough data
Monitoring and Management
9.1
52
Not enough data
7.6
48
Not enough data
Cloud Deployment
7.4
43
Not enough data
7.0
41
Not enough data
Generative AI
6.2
34
Not enough data
6.1
34
Not enough data
Functionality
7.3
287
Not enough data
8.8
309
Not enough data
8.1
291
Not enough data
8.0
294
Not enough data
Management
8.7
305
Not enough data
7.7
283
Not enough data
8.2
297
Not enough data
8.0
300
Not enough data
8.1
302
Not enough data
Generative AI
5.8
230
Not enough data
Agentic AI - Data Observability
6.1
27
Not enough data
6.2
27
Not enough data
6.7
27
Not enough data
6.4
26
Not enough data
6.4
27
Not enough data
Functionality
8.0
177
9.2
16
6.4
171
8.6
16
6.6
166
8.8
16
6.0
161
8.6
16
6.4
162
8.5
14
Management
7.2
164
9.0
16
7.4
165
8.2
16
7.9
164
8.7
14
7.3
169
8.8
14
7.5
164
8.8
14
Generative AI
5.2
142
8.6
14
5.2
141
8.7
14
Categories
Categories
Shared Categories
Monte Carlo and Oracle Data Quality are categorized as Data Quality
Unique Categories
Oracle Data Quality has no unique categories
Reviews
Reviewers' Company Size
Monte Carlo
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Oracle Data Quality
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Small-Business
(50 or fewer emp.)
Mid-Market
(51-1000 emp.)
Enterprise
(> 1000 emp.)
Reviewers' Industry
Financial Services
14.8%
Computer Software
11.1%
Information Technology and Services
10.7%
Marketing and Advertising
3.7%
Pharmaceuticals
3.5%
Other
56.1%
Hospital & Health Care
20.4%
Information Technology and Services
16.7%
Computer Software
11.1%
Research
5.6%
Transportation/Trucking/Railroad
3.7%
Other
42.6%
Alternatives
Discussions
What is Monte Carlo software?
1 Comment
MV
Monte Carlo is a fully automated, end-to-end data observability platform that helps data engineering teams reduce time to detection and resolution for data...Read more
Monte Carlo has no more discussions with answers
Oracle Data Quality has no discussions with answers
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