IBM InfoSphere QualityStage vs. Informatica Cloud Data Quality

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
IBM InfoSphere QualityStage
Score 9.0 out of 10
N/A
IBM InfoSphere QualityStage is a data quality offering from IBM.N/A
Informatica Cloud Data Quality
Score 6.0 out of 10
N/A
The vendor states that Informatica Data Quality empowers companies to take a holistic approach to managing data quality across the entire organization, and that with Informatica Data Quality, users are able to ensure the success of data-driven digital transformation initiatives and projects across users, types, and scale, while also automating mission-critical tasks.N/A
Pricing
IBM InfoSphere QualityStageInformatica Cloud Data Quality
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
IBM InfoSphere QualityStageInformatica Cloud Data Quality
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
IBM InfoSphere QualityStageInformatica Cloud Data Quality
Features
IBM InfoSphere QualityStageInformatica Cloud Data Quality
Data Quality
Comparison of Data Quality features of Product A and Product B
IBM InfoSphere QualityStage
9.0
Ratings
3% above category average
Informatica Cloud Data Quality
8.9
Ratings
2% above category average
Data source connectivity9.00 Ratings9.30 Ratings
Data profiling9.00 Ratings9.20 Ratings
Master data management (MDM) integration9.00 Ratings8.90 Ratings
Data element standardization9.00 Ratings8.20 Ratings
Match and merge9.00 Ratings8.70 Ratings
Address verification9.00 Ratings9.00 Ratings
Best Alternatives
IBM InfoSphere QualityStageInformatica Cloud Data Quality
Small Businesses
HubSpot Data Hub
HubSpot Data Hub
Score 7.7 out of 10
HubSpot Data Hub
HubSpot Data Hub
Score 7.7 out of 10
Medium-sized Companies
IBM InfoSphere Information Server
IBM InfoSphere Information Server
Score 8.0 out of 10
IBM InfoSphere Information Server
IBM InfoSphere Information Server
Score 8.0 out of 10
Enterprises
IBM InfoSphere Information Server
IBM InfoSphere Information Server
Score 8.0 out of 10
IBM InfoSphere Information Server
IBM InfoSphere Information Server
Score 8.0 out of 10
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User Ratings
IBM InfoSphere QualityStageInformatica Cloud Data Quality
Likelihood to Recommend
9.0
(0 ratings)
9.2
(0 ratings)
Likelihood to Renew
-
(0 ratings)
6.6
(0 ratings)
Usability
-
(0 ratings)
8.0
(0 ratings)
Availability
-
(0 ratings)
9.0
(0 ratings)
Performance
-
(0 ratings)
9.0
(0 ratings)
Online Training
-
(0 ratings)
10.0
(0 ratings)
Implementation Rating
-
(0 ratings)
10.0
(0 ratings)
Product Scalability
-
(0 ratings)
9.0
(0 ratings)
User Testimonials
IBM InfoSphere QualityStageInformatica Cloud Data Quality
Likelihood to Recommend
Good for issues with data quality, and instances where you need to add controls and rules to load the data. Also if you have a large amount of data.
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We used Informatica Data Quality to measure the "Data Quality Score" of internal and external reports at my company. Business users set up data profiling and prepared detailed analysis documents for business analysts. and developers developed Data Quality Mapplets for other IT teams to import their Informatica Power Center repositories. Results are stored in a centralized data quality space and then reported and summarized to related business users in detailed ways. At the end of each project, we are now able to place a "Data Quality Score" watermark score on each report involved.
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Pros
  • Create rules
  • Standardization and normalization of data
  • Integration with DataStage
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  • Watch the data real time- After creating the job the data quality engine checks and run the custom rules creating a navigation window at the bottom for review and accessing the data right away.
  • Character Set Mapping
  • Makes sense of our own data, which in turn gives us confidence that we can provide to the end users. IDQ helped us with erroneous data in accounting and HR for accurate and immaculate reports
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Cons
  • Should include all the data profiling functionality
  • Better features to tune matching capability
  • Include AI to automatically recommend actions to improve processes
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  • Several partnerships diminishing the value of technologies
  • Unable to get list of objects from Repository (like sources & targets) that don't have any dependency
  • Scheduling: The built-in scheduling tool has many constraints such as handling Unix/VB scripts etc. Most enterprises use third party tools for this.
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Likelihood to Renew
No answers on this topic
I gave a rating of 8 due to the fact that we use Informatica for both our data quality product and ETL product. Having both integrated makes it so much easier. Microsoft had a similar product of finding duplicates, but at the time it didn't seem mature enough. The usability also in IDQ was pretty easy to navigate and use.
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Usability
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Easy to use not only for developers but also business users
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Reliability and Availability
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The application works well except an occasional error out while using the system. It usually gets fixed when restarting the Infa server
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Performance
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Performance works just fine. It was able to load 200+ business terms, 150+ DQ automation, etc. very well.
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Alternatives Considered
QualityStage is positioned as a world leader as a data cleaning tool, and competes face to face with PowerCenter. Particularly in Latin America we consider that QualityStage is better customized in terms of data cleaning rules for the region.
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Informatica Data Quality has a wide range of cleansing features, that are detailed, professional, and accurate in scaling down the required database. Further, Informatica Data Quality ensures there is proper collaboration, and this fosters businesses to have the freedom of working closely with several programs. Finally, Informatica Data Quality design is authentic and allows personalization.
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Scalability
No answers on this topic
Scalability works as expected and it is truly an enterprise system.
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Return on Investment
  • Improving Data Quality
  • Reducing costs due to better Data Quality
  • Single view of customer / product
  • Better data governance
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  • Integration with tools like PowerCenter helped faster delivery of product, and at the same time conversion
  • Reduce overall project cost due to bad data , bad quality, exceptions identified nearing go-live and post production
  • Employee efficiency is increased exponentially due to more automated, customized tool
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ScreenShots