Informatica Data Quality Reviews

We are using Informatica PowerCenter for ETL, and simultaneously we are using Informatica Data Quality for data profiling, validation, to remove duplicate entries, and for data cleansing.

Ours is a restaurant client, so we get the sales data. There are more than 5,600 franchise sites in our project. We get data from multiple sites once EOD is cleared for all the sites. Some of this data has duplicate records, some has wrong entries or null values. If we directly load the data into our target tables, it may cause errors in querying and reporting tools, leading to data disturbance. We use Informatica Data Quality to maintain good data profiling and data cleansing steps.

We had one situation where we did not encounter any data miss, but during reporting, it was showing as if the sales exceeded our daily limit. We thought that the restaurant got profits, but after due diligence, we realized that there were some duplicate entries. We then understood that we needed to have a data quality check, which is why we integrated Informatica Data Quality along with Informatica PowerCenter.

Some of the best features Informatica Data Quality offers include AI automation using CLAIRE, which integrates AI with Informatica Data Quality, and its user-friendly drag-and-drop interface. All of this is simply usable to any person who has minimal knowledge of ETL.

Rather than querying every table to check for any duplicate entries or null values, it is impossible to query for each site. Once we integrate it with Informatica Data Quality and use the drag-and-drop function to specify the conditions we need and connect to the databases, it directly checks if the values are within the threshold or if we can set conditions, such as not entering records with null values. It also features a match and merge condition, from which data profiling and data cleansing can be done.

One thing is that, compared to the features provided by Informatica Data Quality, when compared to other tools offering similar features, it is somewhat costly. The scalability is not up to mark compared to current market trends.

When we want to work on a large dataset, and we want to scale the data down or up, it requires a lot of work. We also need to reboot the server every time there is a data scalability issue. Additionally, the pricing factor is quite high for the kind of features it offers.

I have been using Informatica Data Quality since my internship days for almost one year.

Informatica Data Quality is stable, but sometimes there are server crashes that require a reboot of the entire server.

The scalability is not up to mark in my view because even a small increase in data, like the number of rows, can cause the server to crash, requiring a reboot.

The customer support for Informatica Data Quality is not up to mark, but the documentation is somewhat relatable from which we can learn about the features and problems.

Before we were not using Informatica Data Quality, some days there was data disturbance, and we thought it was caused by the source itself. Later we discovered that the disturbance occurred during the transformation phase in Informatica PowerCenter or SnapLogic tool. When we integrated Informatica Data Quality with Informatica PowerCenter, there was minimal chance of missing data, null value entries, or duplicate entries. The reporting and data quality have improved significantly since then.

There were very few errors, only minimal errors that can also be managed by changing the metrics in Informatica Data Quality or Informatica PowerCenter. The reporting has also been faster because there were no null values or duplicate records from which the workflows or sessions may have failed. Since we integrated Informatica Data Quality, we have seen a lesser number of situations where users report that data is missing or incomplete.

Since we integrated Informatica Data Quality in our project, the amount of human interaction has reduced, so the team has decreased. There were 12 people working on BI solely focused on data monitoring and error handling. After integrating Informatica Data Quality, it has been reduced to five people now, resulting in cost savings for our project. Additionally, time has improved; previously, we manually checked for data missing or ran a pipeline every three to four hours manually to check for missing or null values. The process has been automated with Informatica Data Quality.

I have been informed by our management team that the pricing is high, but I am not sure about the specific figures regarding what the pricing is.

We have evaluated the quotations from Collibra and Monte Carlo, which are similar tools to Informatica Data Quality, but since we were already using Informatica PowerCenter, we were keen to adopt Informatica Data Quality.

Informatica Data Quality is not a tool that any person with ETL knowledge can use. Proper training on the tool is necessary, as it is somewhat complex. A minimum of one month of training is required before operating in the production environment. Additionally, reviewing the documentation is crucial before working with the tool. When we want to work on a large dataset, and we want to scale the data down or up, it requires a lot of work. We also need to reboot the server every time there is a data scalability issue. Additionally, the pricing factor is quite high for the kind of features it offers.

The customer support for Informatica Data Quality is not up to mark, but the documentation is somewhat relatable from which we can learn about the features and problems. My overall review rating for Informatica Data Quality is 7 out of 10.

At this moment, I am working only with Informatica products. I deal only with Informatica and I am a consultant.

At this moment, I am using Informatica Cloud with some licenses, including Data Integration, Application Integration, Mass Ingestion, and Data Quality. We have licenses for these products, but not for Master Data or Business-to-Business.

At this moment, we are implementing Informatica Cloud directly in one of our customers in Puerto Rico. It is in a bank in Puerto Rico, in the banking sector. We are implementing now the features using Informatica Data Quality, but it is in an initial phase. First, to clarify, the customer has Informatica on-premise products, including PowerCenter and Informatica Data Quality. Now we are implementing Informatica Cloud. Basically, we are migrating all processes from PowerCenter directly to Informatica Cloud. After this, we will try to suggest to the customer to explore the functionality that we have in Informatica Cloud using Informatica Data Quality. At this moment, we are migrating our process, so it is not the correct time to suggest to the customer. I think that it could be a good idea to show to the customer the importance of Informatica Data Quality products at this moment. However, the principal concern of the customer is just to migrate from PowerCenter directly to Informatica Cloud. After this phase, we can try to use Informatica Data Quality with all the features that are available in Informatica Cloud.

About Informatica Data Quality, I do not think that I have any questions because the product is very good. It works fine. The customers that have implemented Informatica Data Quality use it with high frequency without any problems. I do not have any issues or doubts. I suggest to all customers to use Informatica Data Quality product, independent of whether they use Informatica Cloud or on-premise products.

Tokenization is valuable. Standardization is valuable. I use Profiler a lot to provide the right rules for applying Informatica Data Quality. Based on this, these are the features that I find most valuable.

At this moment, I am not considering machine learning-powered insights on data quality management.

For each customer, we have different necessities to implement. At this moment, I do not know what could provide more use or more features in Informatica Data Quality. In my opinion, all features that Informatica Data Quality has is sufficient for the customers. We can try to explore, for example, AI to use directly in Informatica Data Quality to improve the development or suggest more things to customers to explore in the features. However, I sincerely do not know how to explain or how it is necessary to appoint at this moment to Informatica Data Quality to improve more features or other things.

I have worked with Informatica Data Quality since 2009.

I do not have any problems. I think it is perfect for the customer and for me. I do not have any problems with stability.

It is amazing. I like the customer service team. The time to response is very good. The level of knowledge for each one is perfect. I confess that I am a fan of the GCS team.

Informatica Data Quality is a 10. It is amazing. I am an enthusiast to use Informatica products. All the products, we can put the level 10 to use. About the features, the facilities, and the security, all features are 10 in level. My overall review rating for this product is 10.