Informatica Cloud Data Quality vs. SAP Information Steward

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.

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SAP Information Steward is well suited for companies looking for profiling data, monitoring data quality and managing policies. It provides dashboards and features to create the awareness on Data Quality dimensions. SAP IS is not suited to manage huge amount of data due to performance issues. It is not designed to correct data by its own. However, It can remediate data through integration with SAP Master Data Governance or SAP Data Services.

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• 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.

• Audit trail management of Metadata to ensure due diligence with respect to the quality of data
• Control features and identification of risk wrt data
• Ready recknor in form of advance bi reports for procurement and costing kpi
• Flexible comparison and filter functio ality across cross organisation data

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• 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.

• More real-time integration scenarios needed.
• Needs more generic standard content for metadata management.
• The user interface can be more intuitive based on AI.

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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.

Out of the box solution

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Easy to use not only for developers but also business users

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It provides out of box solution for information management at organisation level

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

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

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I believe many features from SAP Information Steward are available in other SAP products as well hence it is more confusing for business or customers to do the right selection. Moreover SAP strategy is not very clear for this particular product hence often we are confused while exploring more related scenarios

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Ready recknoer tools that act as reference are missing that can help to formulate ;Decision Driven Framework' for various strategic decesions

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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.

Both are great products and I highly recommend both

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

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• 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

• Improvement on Organization Data Quality
• Real time Data Quality Issues discovery
• Data Policies effective management

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Independently Invited by TrustRadius

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