Atlan vs. Informatica Cloud Data Quality

Well suited if you're using a lot of tools that work in tandem with this such as Snowflake, Amazon, S3, Tableau, Power, BI, Redshift, and others. It also has robust functionality for the quality management of metadata sets. The most helpful thing here is all the dashboards that provide visibility in one place. In my experience, less suited for collaboration across teams. Not a ton of ways to do things in real-time together.

Incentivized

Helps to increase productivity, optimize costs, and democratize data across multiple cloud environments with cloud ETL and ELT. Capacity to integrate data sources at scale and with ease. Has cloud data integration capabilities that cover diverse sets of patterns, use cases, and users ensuring we have well-architected and seamless automated data pipelines.

Incentivized

• Here, it is very easy to extract and modify the data.
• We can do very quick and smooth integration over here.
• It's OpenSource framework provides extension capabilities to the API it uses

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.

• API documentation could be improved
• Support team could be quicker in their response
• Not a lot of "help" type information users can self-access

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

Incentivized

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.

Incentivized

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

Incentivized

• Atlan has certainly saved time and money by consolidating our metadata sets
• Integrations with Amazon and Tableau have definetely sped up our time to value

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