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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SAS Data Management
Score 8.0 out of 10
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A suite of solutions for data connectivity, enhanced transformations and robust governance. Solutions provide a unified view of data with access to data across databases, data warehouses and data lakes. Connects with cloud platforms, on-premises systems and multicloud data sources.
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Pricing
Informatica Cloud Data Quality
SAS Data Management
Editions & Modules
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No answers on this topic
Offerings
Pricing Offerings
Informatica Cloud Data Quality
SAS Data Management
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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More Pricing Information
Community Pulse
Informatica Cloud Data Quality
SAS Data Management
Features
Informatica Cloud Data Quality
SAS Data Management
Data Quality
Comparison of Data Quality features of Product A and Product B
Informatica Cloud Data Quality
8.2
4 Ratings
3% below category average
SAS Data Management
-
Ratings
Data source connectivity
8.94 Ratings
00 Ratings
Data profiling
8.74 Ratings
00 Ratings
Master data management (MDM) integration
8.24 Ratings
00 Ratings
Data element standardization
7.14 Ratings
00 Ratings
Match and merge
7.94 Ratings
00 Ratings
Address verification
8.44 Ratings
00 Ratings
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Informatica Cloud Data Quality
-
Ratings
SAS Data Management
8.3
10 Ratings
1% above category average
Connect to traditional data sources
00 Ratings
8.610 Ratings
Connecto to Big Data and NoSQL
00 Ratings
8.19 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Informatica Cloud Data Quality
-
Ratings
SAS Data Management
6.7
8 Ratings
19% below category average
Simple transformations
00 Ratings
6.18 Ratings
Complex transformations
00 Ratings
7.48 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Informatica Cloud Data Quality
-
Ratings
SAS Data Management
6.7
8 Ratings
16% below category average
Data model creation
00 Ratings
5.56 Ratings
Metadata management
00 Ratings
7.47 Ratings
Business rules and workflow
00 Ratings
6.67 Ratings
Collaboration
00 Ratings
7.07 Ratings
Testing and debugging
00 Ratings
6.17 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
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.
When data is in a system that needs a complex transformation to be usable for an average user. Such tasks as data residing in systems that have very different connection speeds. It can be integrated and used together after passing through the SAS Data Integration Studio removing timing issues from the users' worries. A part that is perhaps less appropriate is getting users who are not familiar with the source data to set up the load processes.
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.
SAS/Access is great for manipulating large and complex databases.
SAS/Access makes it easy to format reports and graphics from your data.
Data Management and data storage using the Hadoop environment in SAS/Access allows for rapid analysis and simple programming language for all your data needs.
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.
The main negative point is the use of a non-standard language for customizations, as well as the poor integration with non-SAS systems. However, there is no doubt that it is a high-performance and powerful product capable of responding optimally to certain requirements.
With SAS, you pay a license fee annually to use this product. Support is incredible. You get what you pay for, whether it's SAS forums on the SAS support site, technical support tickets via email or phone calls, or example documentation. It's not open source. It's documented thoroughly, and it works.
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.
Because of ease of using SAS DI and data processing speed. There were lots of issues with AWS Redshift on cloud environment in terms of making connections with the data sources and while fetching the data we need to write complex queries.