ER/Studio vs. Informatica Cloud Data Quality

Data Architect is well suited at organizations of all sizes. It is never too early or unnecessary to enforce proper modelling and design standards on data solutions, and this tool will help that greatly by providing an industry leading data modelling tool, ability to import ETL mappings for data lineage, enforcing and managing naming conventions through the naming convention tool, and publishing of data dictionaries through the report publisher. I was successfully able to build models, provide traceability, and document source to target with lineage throughout for both the business (by providing business definitions in the descriptions), and technical teams (by documenting ETL instructions in text fields) along with field level mapping (by creating "Attachments" representing data sources, tables, and fields) providing easy search capabilities using business friendly terms

Incentivized

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.

Incentivized

• ER/Studio has the ability to provide consistent field names and data types through domains, which are templates. This provides a way to have consistent naming of common fields, like CreatedBy and the data types for the fields. They also have the ability to change all the fields that use that domain to a different data type.
• ER/Studio provides the ability to create custom macros. These macros can be used to apply everything from standard fields based on domains to naming all constraints and indexes. I've also used a macro that comes with ER/Studio to spell check field and table names.
• My favorite feature is the ability to compare your data model to databases for deployments of changes, and to other data models.

Incentivized

• 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.

• ER\Studio licensing can be cumbersome and upgrading from one version to another usually takes several phone calls and emails to the licensing group to get the update installed and running.
• The repository can be slow when the model count gets larger. By large I mean 20 to 30 models.
• A nice feature that I would like to see is table comments be displayed on the model along with the attributes. Currently you have to choose between the two.

Incentivized

• 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.

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.

Easy to use not only for developers but also business users

Incentivized

The application works well except an occasional error out while using the system. It usually gets fixed when restarting the Infa server

Incentivized

Performance works just fine. It was able to load 200+ business terms, 150+ DQ automation, etc. very well.

Incentivized

I can call or email support and both get quick turn around. The only issue is they are on the west coast (US) and have a west coast work schedule and I'm on the East coast.

Incentivized

ER/Studio is the most mature, reach in features and quality and therefore useful. Unfortunately, it lacks compatibility with AWS Redshift.

Incentivized

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.

Scalability works as expected and it is truly an enterprise system.

Incentivized

• ER/Studio has had a positive impact on my project as we can develop the data model and have a clear understanding of business needs before we continue with the development phase.

Incentivized

• 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