Informatica Cloud Data Quality

Score6 out of 10

40 Reviews and Ratings

What is Informatica Cloud Data Quality?

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.

Categories & Use Cases

Data source connectivity
Ease of connection to multiple data sources
Category average: 8.3
Data profiling
Ability to find anomalies and hidden relationships between data elements
Category average: 8.3
Address verification
Data format checking and valid address checking
Category average: 8.6

Master data management (MDM) integration
Quality of integration with MDM systems
Category average: 8.6
Match and merge
Ability to identify potential matches and then merge
Category average: 8.2
Data element standardization
Parsing and standardization of data elements according to predefined rules
Category average: 8.3

Nagaraj C

Senior Software Engineer in Corporate at KPMG (10,001+ employees employees)

Use Cases and Deployment Scope

It has great function that will aid using hybrid cloud for data storage possible. Customer support is very well. Data virtualization has made us achieved three major things one reduced expense, reduced risk as well as an improved revenue in total and we have a single access point to monitor the data

Pros

Deliver excellent data virtualization
Reducing expense, risk and increase revenue in total
Single access point to monitor the data

Cons

The product performance is satisfactory in overall

Most Important Features

It makes it possible to integrate data from multiple disparage sources
It is great tool to leverage if you want to create data virtualization layer for apps

Return on Investment

Scalable flexible solutions driving data management in financial services
pretty good integration tool but need to consider third party integrations

Angel Caroline View profile

Information Technology Consultant in Information Technology at FCCI Insurance Group (1001-5000 employees employees)

Use Cases and Deployment Scope

Informatica Data Quality is a solution that pushes companies to take a vibrant step in managing data, enhancing and reinforcing security, and activating all the analytical demands in the business. Further, Informatica Data Quality focuses on data collaboration and standardization, a form that improves the quality and reliability of the database system. Finally, the whole validation and cleansing process is effectively supported by Informatica Data Quality, from address verification to content filtering.

Pros

Perfect data collaboration and detailed standardization. The two procedures support universal acceptability.
Further, the concerned address control and validation increase the accuracy, effectiveness, and reliability of the database.
Finally, Informatica Data Quality has an outstanding cleanser, very rapid, robust, and speedy.

Cons

Data reviewing is not fully encompassed by Informatica Data Quality, which would validate the database.
Besides, third party applications face challenges, due to poor integration support.
Nonetheless, there are proficient, reliable, and contained safety and analytical measures.

Most Important Features

Efficient collaborative application, with the standardization procedures.
Further, suitable and appropriate cleansing solution, for maximum satisfaction.
More so, Informatica Data Quality generates proper analytical reports, which are authentic and fundamental in streamlining decisions.

Return on Investment

Enhanced master data management allows complex details to be easily analyzed without the system getting to breakdown.
Further, Informatica Data Quality has proper validation, cleansing, and safety measures, that keep our database secure and accurate.
Finally, Informatica Data Quality facilitates predictive analytics, very affirmative for demand budgeting.

Alternatives Considered

Oracle Autonomous Database and SAP Data Quality Management

Other Software Used

Column Information Security, CloudApper Safety, BarCloud Stock

Sunil Reddy View profile

Software Engineer in Corporate at Allegis Group (1001-5000 employees employees)

Use Cases and Deployment Scope

IDQ is used for data cleansing and address validation. Passing Invalid Addresses and getting output as valid address and using these address to send mail to employees. Data cleansing rules while loading data from source to warehouse.

Pros

Address Validator Transformation - makes things so easy.
The algorithm for address validation to would take years to write.
Address doctor standalone application is powerful and can use any ui for performance improvements.
Geocoding of Address doctor is so useful.

Cons

It would be great if it could merge with PC Designer.
This tool will be used in the front end systems -where addresses can be visible to the users.
If address doctor improve like google api for lat n longs when we pass input attributes ang combinations.

Most Important Features

Address Doctor
Cleansing transformations like labeller .
Deduplication using match transformation
Thin client version

Return on Investment

ROI will be good when the product has used over a long period.

Other Software Used

Informatica MDM

Gurcan Orhan View profile

Data Quality Management Software Development Manager at Yapı Kredi (10,001+ employees employees)

Pros

It performs quality Data Profiling operations.
A web-based data quality check (without the need for any client tool installation).
Seamless integration with other Informatica products.
Ease of use (2 days of training for profiling, 4 days for intermediate development, and 4 days for advanced development skills).

Cons

A hardware monster.
Needs easier security.
Could provide easier administration.
Needs easier installation steps.

Return on Investment

We already had the licenses of the product with the Informatica Data Integration family.
Installation steps could be simplified.
It would be better if the product had fewer hardware needs.

Alternatives Considered

Oracle Enterprise Data Quality and SAS DataFlux

Other Software Used

Oracle Enterprise Data Quality, SAS DataFlux

Verified User

Manager in Information Technology (5001-10,000 employees employees)

Use Cases and Deployment Scope

Automation of technical and business data quality checks on data from over 10 different data sources across various sectors. Access to end-users on the data profiles makes the company proactive in resolving data quality issues which in turn improves the trust in data. Eventually promotes business decisions backed by data.

Pros

Data quality profile
Automation of data quality check
Exception management
Customizable template to capture metadata

Cons

The cloud offering does not have all the features of on-prem version
The DQ report has limit of only a few sample records it shows, it could show more
Ability to delete an older version of metadata from the business glossary

Most Important Features

Metadata / business glossary
Data quality automation
Data profiling

Return on Investment

Overall DQ efficiency
Easier to understand data before writing the ETL code for the developers
Increased business user participation

Other Software Used

Microsoft Azure

Usability

Ataccama ONE

Syncsort Trillium DQ for Big Data, now Precisely Trillium Quality

RedPoint Data Management & Quality (ubpublished)

DataMentors

Informatica Cloud Data Quality

What is Informatica Cloud Data Quality?

Categories & Use Cases

Key Features

Top Performing Features

Data source connectivity

Data profiling

Address verification

Areas for Improvement

Master data management (MDM) integration

Match and merge

Data element standardization

Reviews

Use Cases and Deployment Scope

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Cons

Most Important Features

Return on Investment

Use Cases and Deployment Scope

Pros

Cons

Most Important Features

Return on Investment

Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

Pros

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Most Important Features

Return on Investment

Other Software Used

Pros

Cons

Return on Investment

Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Most Important Features

Return on Investment

Other Software Used

Usability

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