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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SAP Master Data Governance
Score 8.2 out of 10
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SAP Master Data Governance is a master data management solution that helps users to implement a cohesive and harmonized master data management strategy across all master data domains. It is presented as a solution that simplifies enterprise data management, increases data accuracy, and that facilitates consolidation, central governance and data quality management. The SAP Master Data Governance, cloud edition, a…
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Pricing
Informatica Cloud Data Quality
SAP Master Data Governance
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Informatica Cloud Data Quality
SAP Master Data Governance
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Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
Yes
Entry-level Setup Fee
No setup fee
No setup fee
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Community Pulse
Informatica Cloud Data Quality
SAP Master Data Governance
Features
Informatica Cloud Data Quality
SAP Master Data Governance
Data Quality
Comparison of Data Quality 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.
The best use case of SAP Master Data Governance is centrally consolidating your organizational data so you can make sense of it and make business decisions using that data. We live in a world where data can be all over the place, and making sense of it is a skill of its own.
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.
Data conversation and Data uploading into SAP S/4 HANA system using SAP Master Data Governance during SAP implementation phase.
Centrally Data governance to have visibility of data in different systems with integration to SAP Master Data Governance, including SAP S/4, SAP EWM and SAP Ariba.
Data Management and Data correction using MDM through SAP Master Data Governance for Materials, which is subjected to approval before finally using in the S/4.
We've had to design our own unique or alternative solutions to satisfy our business objectives because plugins don't always perform well with the main product.
It can be sluggish at times and lacks functionality offered by rival MDM packages, as well as limited resources and documentation for certain areas such as hierarchy.
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.
MDG has proven to work and to bring results once implemented with the right approach, right engagement and sponsorship. Given that it is a very good tool to govern and control master data. Mainly if your company runs SAP systems which there will be a very straight forward integration, not needing any additional middleware or technologies.
Upto 8 1. Powerful Features: It offers a wide range of powerful features. 2. Complex Data Management: It helps companies to comply with large and complex regulation and audit processes. Missing 2: 1. Less interactive user interface or less modern look. 2. Limited integration with other tools
SAP provides great support for Master Data Governance (MDG). They work with us when we are faced with issues in standard solutions. SAP has a great set of Fiori tools that can be used for a better user experience. The issue with SAP is their web dynpro UI screens and they need to provide better support for performance issues that customers face.
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.
An environment provided by SAP Master Data Governance seamless and integration with other SAP products is very good. Also it ensures compatibility and streamlines procedures, this integration is beneficial for businesses who have already invested in SAP products.
MDG is definitely an investment that takes a few years to recuperate. (If you are looking at pure, direct financial benefits.)
However, if you look at all the productivity gains in the business due to not having to stop the business constantly because of data issues, the payback period would be substantially shorter.
In particular, we were able to reduce master data maintenance staffing levels while increasing quality and decreasing workload for business requesters and approvers.
Not having to worry about data integrity issues has shortened the time to realize benefits for several of our major process change related projects and it has also decreased the risk of these projects running into significant delays (often at the last minute) due to master data being misjudged during the process resdesign.
