Clear Analytics vs. IBM InfoSphere Information Server vs. Informatica Cloud Data Quality

Clear Analytics has almost all the features which are mostly required by the organizations for their data analytics and transformation activities. Clear Analytics is built on the support of a parallel query engine which makes it handle large volume requests and also provides optimized results. However, every solution has some limitations, Clear Analytics lacks advanced features such as Predictive Analytics which is a very demanding feature in the market as most organizations are adopting AI/ML automation for their businesses. Also, it needs to add a lot of advanced visualizations so that the user has the choice of selection. Overall a good solution to be recommended.

Incentivized

Information Server is extremely useful to replace manual developments that require a lot of coding effort. It significantly increases the productivity of the initial development and the future maintenance of the processes since it has a visual development environment with self-documentation.

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

• Quick management of the data through Excel thanks to the tools Clear Analytics provides such as filtering different information for an easier view.
• Ability to add new KPIs to measure different areas of our business.
• Drag and drop tool which makes an easier usage of Excel.
• Cost-effective. I find Clear Analytics to be affordable considering the number of features added and the unlimited sharing and editing it provides.

Incentivized

• IIS best for ETL ,not ELT , and many and diffrent source systems.
• It also can process big data , unstuctured data
• It is not only DWH , you can use infosphere for analys and see the bigger architecture of your OLTP systems

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.

• For Charts and Graphs , getting the Power Point Deck would be helpful. As of now we use the data from Excel Reports

Incentivized

• I would be nice to have a new web development environment for DataStage.
• Connectivity Packs such as Pack for SAP Application are a little pricey.
• It is confusing for new developers the possibility of developing jobs using different execution engines such as Parallel or Server.

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.

• Scale of implementation
• IBM techsupport

Incentivized

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.

I have really enjoyed working with Clear Analytics. This platform has contributed to increased production and reliable data control processes. It has flexible user interface that can be operated by any organization member. Reports are shared on simple and transparent way in the dashboard for easy access. Data analytics from all departments have created manageable working environment that have increased production.

Incentivized

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 have had multiple scenarios where I had to reach out to the support team of Clear Analytics, be it access-related, general queries, during initial implementation, etc. My overall experience has always been good. I got the needed support every time without any delay. I can definitely recommend Clear Analytics simply on the basis of its excellent support team.

Incentivized

There are other great tools that are out there but Clear Analytics is reasonable for our business and provides a lot of features that we specifically needed

Incentivized

DataStage is more robust and stable than ODI The ability to perform complex transformations or implement business rules is much more developed in DS

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

• Super useful to get started immediately
• Didn't have to rely on migrating data
• Easy to add members
• Easy to learn and build charts

Incentivized

• Productivity of the development of integration processes.
• Better documentation and governance.
• Reduce training costs of various technologies.

• 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