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.
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Oracle Data Masking and Subsetting
Score8 out of 10
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Oracle Data Masking and Subsetting is designed to help database customers improve security, accelerate compliance, and reduce IT costs by sanitizing copies of production data for testing, development, and other activities and by discarding unnecessary data.
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Pricing
Informatica Cloud Data Quality
Oracle Data Masking and Subsetting
Editions & Modules
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Offerings
Pricing Offerings
Informatica Cloud Data Quality
Oracle Data Masking and Subsetting
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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Features
Informatica Cloud Data Quality
Oracle Data Masking and Subsetting
Data Quality
Comparison of Data Quality features of Informatica Cloud Data Quality and Oracle Data Masking and Subsetting
For effective data collaboration, systematic verification of customer information, and address, among others, Informatica Data Quality is a fruitful application to consider. Besides, Informatica Data Quality controls quality through a cleansing process, giving the company a professional outline of candid data profiling and reputable analytics. Finally, Informatica Data Quality allows the simplistic navigation of content, with a dashboard that supports predictability.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
The tool is excellent when you need to provide all the details about your clients, yet hide their identity - all while maintaining the referential integrity of the data (so child-records of the masked parent record and maintain the same fake ID of the parent).
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
The matching algorithms in IDQ are very powerful if you understand the different types that they offer (e.g., Hamming Distance, Jaro, Bigram, etc..). We had to play around with it to see which best suit our own needs of identifying and eliminating duplicate customers. Setting up the whole process (e.g., creating the KeyGenerator Transformation, setting up the matching threshold, etc..) can be somewhat time consuming and a challenge if you don't first standardize your data.
The integration with PowerCenter is great if you have both. You can either import your mappings directly to PowerCenter or to an XML file. The only downside is that some of the transformations are unique to IDQ, so you are not really able to edit them once in PowerCenter.
The standardizer transformation was key in helping us standardize our customer data (e.g., names, addresses, etc..). It was helpful due to having create a reference table containing the standardized value and the associated unstandardized values. What was great was that if you used Informatica Analyst, a business analyst could login and correct any of the values.
It offers several ways in which you can mask your data; for example, you can choose to replace all names with "real fake names", or choose to replace all SSNs with existing SSNs, but randomly assigned. You control the algorithm.
It works on non-Oracle databases as well (in our case, we use it for both Oracle and SQL/Server).
The overhead is minimal (it doesn't take long to run, and it doesn't consume too many system resources.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
As pointed out earlier, due all the robust features IDQ has, our use f the product is successful and stable. IDQ is being used in multiple sources (from CRM application and in batch mode). As this is an iterative process, we are looking to improve our system efficiency using IDQ.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
IDQ is used by a department at my organisation to ensure and enhance the data quality. The usage was started with address standardization and now it had been brought to altogether a next level of quality check where it fixes duplicates, junk characters, standardize the names, streets, product descriptions. In the past we had issues mainly with duplicate customers and products and this were affecting the sales projection and estimates.
We also looked at Delphix: the tool was quite powerful, easy to use, and competitive from a cost standpoint. However, since our entire data warehouse environment is built on the Oracle technology stack, it made sense to us to use the Oracle product here, as it integrates very well with other Oracle database and ETL products.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
We have many compliance regulations we need to adhere to. Without this tool, we were always taking a risk of exposing client information, and get penalized by the State of the Feds (the financial consequences are significant).
So while the tool doesn't save us money directly, it greatly reduces the risk we had been taking all these years. To some degree, this is much like an insurance policy.
Given the above, it also allows us to share information with other departments/agencies, in situations where before we simply couldn't take the risk of exposing client information.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info