Informatica Intelligent Data Management Cloud (IDMC) Reviews

They could improve the product support for the Arabic language. Currently, there are limitations in processing and the interface itself regarding Arabic language support.

We have been using Informatica Enterprise Data Catalog for two years.

I rate the platform's stability a nine out of ten.

Our organization has 12 Informatica Enterprise Data Catalog users. I rate the scalability an eight out of ten.

We encountered a delayed response from the technical support team.

Neutral

We also use the Erwin Data Catalog, depending on the customer's requirements.

The initial setup process is easy. It takes around two days to complete.

We get help from third-party vendors for product implementation.

The ROI from using Informatica EGC (Enterprise Data Catalog) can be substantial, as it helps everyone, from novices to experts and stakeholders, maximize their data's potential. It enables thorough understanding, insightful analysis, and seamless sharing of data assets. Features like adding business terms enhance data quality and governance, benefiting businesses across various sectors. Many customers have truly benefited from these options.

For 12 users, the platform's estimated annual cost is around $160,000.

Our data management processes primarily use Informatica to connect to data sources, scan metadata to obtain physical data and perform data classification. Subsequently, we create business terms to establish a business glossary repository within the platform.

The features that have been most effective in improving our data governance include auto-classification and the ease of exploration and navigation within the tool. It can automatically connect or associate business terms with various options, providing flexibility beyond general capabilities.

The AI-driven discovery capability has significantly enhanced our data cataloging processes. Utilizing AI for auto-classification and discovery has been particularly beneficial. However, there is room for improvement in certain areas for understanding the data.

It has adapted well to changes in our data landscape, seamlessly accommodating new types of data sources. It offers flexibility in adding new data sources and supporting multiple data connectors.

Looking ahead to the future of Informatica Data Catalog, data lineage will be a significant trend influencing the product. These features are currently available, but there is potential for further enhancement and detail. It will enable organizations to comprehensively understand their data and its relationships with business processes and regulatory requirements.

I highly recommend it to others and rate it a nine out of ten.

I use Informatica Data Quality to consolidate master data across our organization, ensuring consistent and high-quality data. Combining MDM and data quality features, we address issues like customer data inaccuracies. Additionally, we document business terms in our data catalog for a unified view across the company.

The profiling feature in Informatica Data Quality is incredibly effective for data governance. It helps identify data types and potential duplicates, streamlining the detection process. It also suggests matches with predefined fields in our catalog.

There is room for improvement in the Data Marketplace aspect. Specifically, we found some basic functionalities lacking, like transparent integration of quality indicators. Customizability and additional features in the Data Marketplace would be welcomed in future updates. 

I have been working with Informatica Data Quality for three years.

I would rate the stability of the solution as an eight out of ten.

Scalability with Informatica had some initial challenges, especially when ingesting metadata into the data catalog due to the large volume of source systems and data. While it eventually worked, it took considerable time. Overall, I would rate it a six out of ten for scalability at the moment.

Initially, a few hundred users were using Informatica, but now it is a few thousand. 

The technical support is good. I would rate them at a seven out of ten.

Neutral

Maintenance and troubleshooting are handled solely by the vendor.

Informatica's licensing cost is among the highest in the market. I would rate it as a five out of ten in terms of costliness. With Informatica, we have a single bundled agreement covering the entire solution based on total capacity rather than specific modules. 

Before choosing Informatica, we evaluated various vendors, including IBM and Microsoft. Initially, we considered a best-of-breed approach, but Informatica emerged as the preferred choice for three out of four key areas: MDM, data quality, and data marketplace. While there were some limitations in certain tools like the data marketplace and data catalog, we opted for Informatica's complete suite to avoid additional integration efforts and to capitalize on its strengths in critical areas.

The rule-based strategies feature in Informatica Data Quality has greatly benefited our data quality initiatives. With predefined rules in place, it provides a solid framework for assessing data quality. While data quality ultimately depends on source system updates, these rules give valuable insights and are heavily used alongside automatic profiling for effective data management.

Informatica Data Quality works within our IT setup for analyzing and reporting on data quality. It doesn't directly integrate with other tools, but it gives us clear insights. Data flows from sources to Informatica for analysis, and then to data stewards for updates, but there is no integration back to the original applications yet.

Navigating between modules within Informatica isn't always seamless. For instance, actions in the catalog might not be visible in the data marketplace.

Overall, I would rate Informatica Data Quality as an eight out of ten.

The solution is used for data quality implementation. We handle the whole life cycle of data quality from end to end, including rule specification and exception handling.

We're seeing more productivity. The tool provides accelerated libraries that we can use instead of building things from scratch. It's looking very promising from a productivity perspective.

The profiling features are much better than the on-premise version.

We have complex datasets. Logical views are a little bit behind in comparison to the on-premise version.

I have been using the solution for less than three months. I am using the latest version of the solution.

The product is very solid.

I rate the product’s scalability a nine out of ten.

The technical support is very good.

Positive

We have used the on-premise and SaaS versions of the solution. All our on-premise infrastructure is in Informatica. It was a natural move to Informatica Cloud Data Quality. It was easy.

The initial setup is quite straightforward.

The pilot is not in production. It's in a lower environment. We are not at the stage of doing performance testing until the new year. We are not fully utilizing our on-premise infrastructure. Moving to the cloud could save us some money.

People wanting to implement the solution must consider it like launching a new data management program from scratch. Rather than just thinking about migration from on-premise to the cloud, try to adjust your mindset to use the solution as a completely new tool. If you just migrate from the on-premises to the cloud, if you have any technical debt in the on-premise version, you'll just replicate that into the cloud.

Overall, I rate the product a nine out of ten.

We use Informatica Axon to make sure our data is in good shape across different apps. It helps us work with clients to set rules for data and keep things organized. For a bank, it makes sure customer info, like contact details, is correct and follows privacy rules. Axon also made it easy for our team to check and trust the data we were using. It is like a tool that brings everyone together and helps us find and use data better.

The most valuable feature of Informatica Axon has been the data marketplace. It has been a favorite because it not only boosts user engagement but also fosters collaboration. Implementing it was straightforward, and it seamlessly became a part of our organizational culture, making it easy for people to work together and share information.

One area I would like to see improvement in Informatica Axon is achieving feature parity between the on-premise and cloud versions. It would be great if the cloud version could match the full range of capabilities available on-premise. This would ensure a seamless experience and allow users to leverage all of Axon's features regardless of their deployment environment.

I have been using Informatica Axon for almost two years.

I would rate Informatica Axon at eight out of ten for stability. The main thing to improve is making the features and interface consistent between the on-premise and cloud versions. The cloud interface looks modern with DDGC, but the on-premise one is a bit outdated. Aligning them would be a big plus.

It is quite scalable. We had approximately 500 end users.

The standard support is good – they usually get back within the agreed time. If you have a higher-level support contract, like gold, silver, or platinum, you get a faster response, but the support team is generally effective and helpful.

Setting up Informatica Axon varies, but it generally depends on having a good grasp of data concepts. If you understand data well, the setup becomes more straightforward. Deploying and maintaining Informatica Axon depends on the use case, but I would say not too many people are needed. I would recommend at least two people per department or business unit to start with. It is good to have a mix, like an admin or engineer and a developer. One person could handle the technical side, while the other, who understands data and processes well, can provide expertise.

The licensing for Informatica Axon is expensive, but it provides value for the investment. With the new cloud platform, they have introduced a consumption-based license model, where you only pay for what you use. This change has made it more affordable and easier for users.

For those looking to start using Informatica Axon, my advice is to do thorough research, especially on available solutions, as integration with other software plays a big role. With the Informatica cloud solution, you have the flexibility to choose features that suit your needs. Starting with data quality is usually a good move before diving into more complex tasks. Consider exploring tools like robotics for repetitive or tedious tasks. Having someone experienced with data is a significant advantage, as these projects often have long cycles. Experienced individuals can guide you, help set realistic expectations, and emphasize the need for patience, as data governance projects take time to show results. Overall, I would rate the solution as an eight out of ten.

A wide range of solutions exist because data is primary, and my company looks over many overall data management solutions, from which Informatica Data Quality becomes one of our biggest priorities.

I cannot just go into details of the product's features randomly because if you look at a very wide range of overall requirements, be it Informatica or Talend or more data lake solutions like Snowflake. My company goes through an entire range of areas, making it difficult for me to just speak about the features.

Accuracy is one of the key areas a client looks at before using a solution. Considering internal data from legacy systems, it is quite difficult to know if Informatica Data Quality meets that high level of accuracy criteria. The aforementioned area can be considered for improvement in the solution.

I have been a consultant for Informatica Data Quality for two years in my company. I don't directly use the solution.

From a stability point of view, I haven't heard of any complaints about the solution from any of my company's clients who use it.

I don't go into the technical side of Informatica Data Quality much since I mostly work on the procurement side and market knowledge area related to the product. Scalability remains one of the requirements of the clients who buy the product, especially for future growth. My company caters to a mixture of clients, consisting of small, medium, and enterprise-sized businesses.

I do not go into the complete deployment areas of Informatica Data Quality.

The solution is deployed on a hybrid cloud.

Informatica Data Quality is a product that is worth the money.

I rate the product's pricing a seven on a scale of one to ten, where one is the lowest price and ten is the highest price.

I do look at a number of data quality tools, but Informatica, Talend, and Ataccama are a few of the top solutions I have come across.

My company deals with Informatica Data Quality since Informatica is a big vendor in the overall data management area.

I won't be able to speak on how I can recommend the solution to others since my suggestions will have to be based on the product's features and technical details. I wouldn't be able to point out anything about the product in detail, especially since I am not much of an end user.

Based on the feedback that I have received about the product from my company's customers, I rate the overall product a seven or eight out of ten.

Our use cases were related to the financial and banking sectors. We created data and transferred it to the enterprise level. 

The most valuable feature of Informatica Axon is features, interfaces, and data mapping. It represents clusters nicely. 

The solution should improve its integration. 

 I have been using the product for three years. 

Informatica Axon is stable. 

The product is scalable. 

The tool's support is fine. 

Informatica Axon's deployment is straightforward. 

I rate the product an eight out of ten. 

We have a customer with a lot of data. We see a lot of quality issues, so we have implemented a data quality solution on-premises. Since everything is moving to the cloud, we are also moving our data quality solution to the cloud. We are building data quality goals for profiling, matching, and enrichment. There are other use cases, one example is matching data in our on-premises MDM system. To do this, we need to clean the data and profile it to understand its quality. We can then use data quality rules and matching modules to identify and resolve data quality issues. We also use it to validate customer email addresses and phone numbers on the fly in our in-house customer applications. Other use cases include data integration and data governance.

The user-friendliness and performance of Informatica is quite impressive. It’s easier to grasp and understand data quality, and how data governance tools work, as they are built on the same underlying architecture. The QCA addresses performance issues, whether it’s related to data, PowerCenter, or any other aspect. The shared underlying architecture enables me to pinpoint parameters for optimization. Informatica can handle large volumes of data without compromising performance. While other tools may offer similar capabilities, Informatica consistently delivers impressive results.

Informatica Cloud Data Quality provides many of the capabilities found in on-premises solutions. While the cloud edition offers additional features, it has also deprecated certain functionalities, such as connecting to web services. In the on-premises version, features like web service consumer and web service provider are available, but these functionalities are currently unavailable in the cloud edition. Vendors offer options for custom solutions, although they may not offer the same functionality as the on-premises version.

I have been using Informatica Cloud Data Quality for years. We are using the latest version of the solution.

I rate the solution’s stability an eight out of ten.

I rate the solution’s scalability an eight out of ten.

The team is acquiring knowledge. In our previous on-premises environment, we had a team of experienced engineers who were familiar with the system and could provide instant answers. However, in the new cloud environment, there’s a learning curve in the new cloud environment, and it may take some time for them to understand and respond effectively.

Positive

Overall, I rate the solution a seven-point five out of ten.

Data Quality involves quality checks and data profiling. It scans through the data, providing metrics like the number of nulls and unique values. Informatica Data Quality profiles the data.

In my experience, I've specifically worked with a feature called ARS Doctor, which is part of IDQ (Informatica Data Quality). So, in my earlier project, the core functionality of ARS Doctor revolved around address information sourced from the USPS postal service. It does validate the data. This is crucial because customer addresses often contain diverse writing errors. Given the presence of multiple applications, addresses can be inputted or tagged across various systems or enter their address data differently, yet it needs to conform to a standardized format. 

Therefore, each address, even if inputted from distinct systems, undergoes validation to match a single, customizable format. We also have customized settings for these validations.

There's certainly room for improvement. One crucial area is generating detailed reports on file statuses. Presently, this is represented visually, often as graphs or charts. Such reporting could offer comprehensive insights into the areas that demand attention and further scrutiny.

I possess extensive hands-on experience and expertise in Informatica.

I do find Informatica Data Quality is stable. It generally maintains a high level of reliability and stability, making it an asset compliance in various scenarios.

In terms of scalability, you see, it is on the cloud environment, which itself gives the scalable, and flexibility in terms of accommodation of the data. So if we want to scan across the data for the quality and checks, like, not on any sample data. We really want to do this profiling on the checks on a lot of data. So it is very much visible in the cloud environment, which is cloud storage, which is provided by AWS or Azure, or GCP.

Based on my experience and involvement in reported discussions throughout my career, I would definitely give their customer service a high rating. We receive excellent support from Informatica's team, especially during activities like upgrades and other related tasks. 

Positive

When it comes to the initial setup, particularly in a cloud environment, it's primarily license-based. There are various features available, each catering to different needs. The platform can establish connections with various systems based on the connectors it offers. These connectors facilitate integration with different environments. 

When it comes to data quality, it is a license-based model. So, if you procure the necessary licenses, you gain access to the relevant options. It's all about tailoring the solution to the specific requirements of the project.

If the goal is to perform quality checks separately, there's the option to employ dedicated products for that purpose. Alternatively, data quality checks can be integrated into the data integration process. Naturally, data integration involves elements of reconciliation, verification, and quality assessment. The chosen approach hinges on factors such as data volume and the complexity of requirements. If these factors increase, the option to select different products designed for specific tasks becomes viable and can be explored further.

Overall, I would rate the solution a seven out of ten. 

The solution gives us the visual features on how the quality is because back in few years or back some time ago. So it was, like, always a kind of hidden thing because, as a part of the data governance, we do have a lot of data dumped in the data warehouse. But relaying we have been just relying on the data warehouse. So we didn't know, like, it is really giving us the powerful insight, or it is really giving our quality data because it could be junk or anything. So, in that case, at least this solution definitely gives us the strength with the visualization part, which gives us, "Okay, these are the different types of data it exists and data. These are the different qualities where it can improve the quality."