I am using Informatica Intelligent Data Management Cloud (IDMC) as a consultant providing technical support for the platform.

I have been dealing with the product for approximately 20 years, starting with the PowerCenter tools and then moving to the Data Quality side. We used the on-premises Data Quality, the Enterprise Data Catalog, and Axon for the business metadata and glossary components, and we have migrated all of those on-premises solutions into the cloud solution.

Regarding IDMC's real-time data streaming capability, we do not use it for any sort of replication as we have other tools that do that, such as Qlik for replication. Those tools were already established, and the biggest driver for us is a catalog, glossary, and data quality—those are the three primary areas that are our biggest use at the moment.

My specialization in Informatica Intelligent Data Management Cloud (IDMC) really focuses on the lineage side and the cataloging. I do a bit of development, but I am focusing on actually implementing the end-to-end lineage, creating the custom lineage, and creating custom resources.

We do utilize IDMC's data integration features, and we use the CAI component and have integrated it into other applications, such as Jira and the Confluence tool. Our company landscape is quite broad, so we have a lot of different technologies because as the company has grown over the years, we have acquired other companies with their own technology suites that we have had to integrate to, which presents challenges.

Regarding IDMC's data catalog, I am still fairly new to it, having used it for approximately the last month, so I am getting to grips with it. There were some features I thought were better on-premises, but it feels a catch-up game. What I do appreciate about the cloud is the transparent update cycles with minimal downtime, all managed within the cloud environment, which definitely provides a better user experience.

We have a huge footprint on the data quality side with IDMC. Many of our subsidiaries are using that, and it is becoming increasingly important with the AI component, understanding data quality before leveraging it for any AI initiative. Many business units are now mandated, as part of their key performance indicators, to ensure data quality on all sources.

I have not explored IDMC's automation capabilities driven by AI and metadata too much at the moment, but it is on the cards. We are basically creating the foundation, as the whole migration has taken place recently and it is still early days.

I think Informatica Intelligent Data Management Cloud (IDMC) is evolving, and as the vendors move forward, they pick up new concepts from each other. I have seen that products leapfrog each other, and from my experience over the years, the big players tend to copy features or add enhancements based on industry trends. I feel whatever the tool does not have now, there is a feedback loop allowing us to request new features, and we continually ask for different ways to do things as we have a pipeline into the product management team.

It is difficult to say what additional features I would prefer to see in the next release of IDMC. I would appreciate more automation on the lineage front, with more AI to seamlessly join independent sources and create seamless lineage between different technologies, such as from file into database A into a different database and landing up in a reporting system such as Cognos, Qlik, Qlik Sense, QlikView, or Power BI.

I rate the technical support for IDMC at an eight out of ten as any issues that we raise are dealt with fairly speedily.

We also have a support partner in South Africa that deals directly with Informatica. We log our calls with our support partner, who in turn logs it with Informatica, facilitating all of the interaction.

The interface of Informatica Intelligent Data Management Cloud (IDMC) is fine, and I appreciate the browser interface. There is no thick client needing constant upgrades, and I am enjoying the fact that the software is quite seamless to actually use.

Regarding the pricing of IDMC, that is not really in my domain as it is more at the management level.

I rate this product a seven out of ten overall.

I worked for around seven to eight months on a project in the insurance domain using Informatica Intelligent Data Management Cloud (IDMC) and there I was working as a Senior Solution Architect or a Principal Cloud Consultant.

We use the data integration features of Informatica Intelligent Data Management Cloud (IDMC) with a focus primarily on the ETL perspective or the data warehouse perspective, where we are taking different data sources, combining them, implementing some data quality rules on top of it, and then storing it in our data warehouse environment. From there, we are using that data for our reporting and analytics purposes.

We also use the IDMC Data Catalog, but that is very limited, and we are using it only for governance purposes. As a Solution Architect, my main involvement is to design the solution at a very high level; at a ground level, I'm not using the Data Catalog on a day-to-day basis.

The best part about the Informatica Intelligent Data Management Cloud (IDMC) is the modularity; when I say modularity, I mean the things get easier by working into different modules. For example, if you want to implement the Master Data Management solution, we are only limited to the master data solution. If you want to go with the admin side of the things, then we are only limited to the admin side of the things. The second important thing I appreciate about this is the more focus from the AI angle, Artificial Intelligence angle, which is inbuilt into the IDMC.

Definitely, we see return on investment from this solution in terms of time; time reduction or cost benefits is what we are getting very good results from.

One of my clients has a requirement; they want to integrate metadata into the process, which means, for example, if I just want to implement a new field into my database, that field needs to be reflected throughout, let's say, 200 mappings. This highlights the need for a data-driven approach.

My experience with technical support from Informatica is quite interesting; I would rate it as nine out of ten for Informatica PowerCenter kind of products or the Informatica integration products, because my team can do some hands-on using their free licenses or one-month kind of products. However, for products Master Data Management or related to MDM or Data Governance, there is no way by which we can directly practice, and my team struggles at that point.

I worked for around seven to eight months on a project in the insurance domain using Informatica Intelligent Data Management Cloud (IDMC) where I was working as a Senior Solution Architect or a Principal Cloud Consultant.

I find Informatica Intelligent Data Management Cloud (IDMC) to be a sustainable and scalable solution.

My experience with technical support from Informatica is quite interesting; I would rate it as nine out of ten for Informatica PowerCenter kind of products or the Informatica integration products, because my team can do some hands-on using their free licenses or one-month kind of products. However, for products Master Data Management or related to MDM or Data Governance, there is no way by which we can directly practice, and my team struggles at that point.

My experience with technical support would indeed be rated as nine out of ten.

The initial setup of Informatica Intelligent Data Management Cloud (IDMC) is moderate; it is not complex.

We see return on investment from this solution in terms of time; time reduction or cost benefits is what we are getting very good results from.

My company is a partner with Informatica, and we are the service provider for the company, which defines my relationship with Informatica.

My main role is not involved in the pricing side of things because that is already between the company and the clients. My title is Principal Cloud Engineer at Fifth Dimension Robotics.

On a scale of 1-10, I rate Informatica Intelligent Data Management Cloud (IDMC) a nine out of ten.

We are using Oracle, Informatica IDQ, EDC, and Axon. These three tools are currently being used on one of our projects in the Middle East.

We recently started a project with one of our clients in Saudi Arabia, and we are using Informatica Cloud Data Quality for data quality management.

The customer required us to use Informatica Cloud Data Quality. They insisted on having it and were willing to pay more, which is fine because we tried to ensure that the tool is just a mechanism to automate activities, not the backbone for implementation.

EDC stands for Enterprise Data Catalog, IDQ stands for Informatica Data Quality, and Axon is for governance.

Mainly with data quality, we have a full data quality management suite through Informatica Cloud Data Quality itself, starting from documentation of business rules, profiling, getting any kind of issues with data quality, doing assessments, and we do these kinds of frequent measures for data quality, as well as resolving any kind of issues.

Regarding the features, the dashboards related to data quality profiling are the most powerful tool that we are using, and the integration between EDC and the data quality is very important. Having this feature provides a real understanding of the environment and also aids in quality management for whatever kind of data sources within the organizations.

The configuration of Informatica Cloud Data Quality is one of the challenges we have, especially with a limited number of users which is always a challenge when using Informatica as a tool.

Our administration team always refers to Informatica Middle East for each step, so communication is also one of our obstacles because materials to explain the steps seem not very mature. The technical team struggles, as they keep getting back to Informatica almost every day for feedback and guidance, and this should be automated somehow so that people can easily access the materials.

The tool itself is mature and everything is fine, but we have these kinds of facilitations that the tool itself needs to be improved.

I am satisfied with the quality of support I receive, but sometimes we get some delays here and there when it comes to responses, but overall, it's good.

I do not consider that Informatica Cloud Data Quality is cost effective enough, as it's very expensive and we are using another tool called OvalEdge, which is similar in some features, but not all features. OvalEdge has about 90% of the functionality of Informatica Cloud Data Quality but costs about a third of the price.

I definitely will not recommend Informatica Cloud Data Quality because it's very hard to manage the licensing model and the price is very high. My experience with Informatica Cloud Data Quality compared to OvalEdge shows that OvalEdge is better because they are very flexible, they develop connectors whenever there is a requirement, they respond immediately, and the installation and configuration of the tool is straightforward and easy to use at a third of the price of Informatica Cloud Data Quality.

Collaboration between team members is important. On a scale of one to ten, I rate Informatica Cloud Data Quality a nine out of ten.

We use Informatica Cloud Data Quality for data profiling and data quality. We also use the intelligent system of Informatica, lineage, and the data catalog.

We are using Informatica to get the data profiling. For example, if we are planning data analytics or data science projects, we first plan to ingest all the data that we are planning to use in that project into Informatica. Then we check it and get the profile. We also get the catalog of the data, and then we start.

The most important benefit of the data quality product is the data quality side. When we started, the data quality level was maybe 17% for that domain. After we ingest the data into the data quality tool, it is more than 70%.

The initial setup takes approximately less than two months for one domain. It can be easier, but it also takes a lot of time on the process side, such as creating the KPIs for data quality. The system is a little bit complicated. It has too many features.

The license or pricing is an area of the cloud data quality solution that I would like to see improved or enhanced somehow.

For our plan, Informatica Cloud Data Quality is now a fit. If asking about other features that would be nice to have, the AI side can be more powerful.

If a new solution has the same features and less investment, it would be worth considering.

I have been working with Informatica Cloud Data Quality for approximately one year.

I would evaluate Informatica's technical support as good. It's more than enough.

I evaluated SAP also. Microsoft does not have a strict solution. They now have Purview, but we could not evaluate it as they didn't have a solution for the last year. We evaluated Informatica, SAP, and Erwin. We can reach the support easily in Turkey. The solution centers are good.

For one domain, the initial setup takes approximately less than two months.

I am not utilizing the data quality rules management part yet.

We had a bad experience before Informatica Cloud Data Quality. We started a data analytics project that took more than three months of wasted time because we couldn't use the data to create the optimization model. If we have an application like Informatica Cloud Data Quality or a data quality platform, we can finish the project in less than three months.

This is the on-prem data that we ingested. We don't use any cloud data that we have. For now, we are just using the on-prem data, and we don't have any integration from the data outside of the on-prem.

I would recommend it to other users. On a scale of 1-10, I rate this solution a 9.

I worked on a project where we had to use these tools to execute the data quality requirements from assessment perspectives to scorecards and similar tasks.

I haven't used any other tools, so I cannot really compare. However, anything that was related to data quality from an implementation perspective was available.

I haven't used it extensively, but it has its own use cases and could be potentially useful depending on the context. The main work I did was benchmarking the data sets that were available at the time to complete the seven dimensions of data quality.

I have used Informatica Cloud Data Quality and PowerCenter for a bit, though I worked on the data quality tool mostly.

I tried both versions, the one on the data center and the one on IDMC, which is the Informatica Cloud.

Regarding infrastructure setup, I was responsible for installing Informatica Cloud Data Quality on Google infrastructure, which was straightforward. I don't recall having significant issues. The challenges were mainly related to firewalls, security rules, and allowing network connectivity.

I think it would be very useful to have the same features on the data center version available on the cloud version. I remember when I used it, there was some limitation in one of the data quality dimensions. I was not able to perform certain tasks on the cloud version, even though I could do them on the data center version.

When a customer moves from data center to cloud or a SaaS implementation on another platform provider, they shouldn't experience reduced functionality. It should be enhanced.

I used the solution for approximately four to five months.

We had a unique setup at the time that didn't function optimally, so certain tasks had to be done manually to some extent.

The solution is stable. However, the data center version currently appears more stable than the cloud version in terms of feature usage and overall smoothness.

Informatica Cloud Data Quality is a scalable solution.

The customer service team was great. They were very proactive with us.

We were presented with Informatica Cloud Data Quality immediately when we started. There weren't any other options at that time.

As a technology consultant, I worked for the client who had Informatica Cloud Data Quality already set up for us to use.

I would prefer to remain anonymous as including my name could cause some issues.

Though I'm not a data engineer by profession, it was really easy to navigate the tool.

I would rate this solution 8.5 out of 10.

I work in the insurance industry.

For this build, the cloud offering provides scalability and agility for my current project, which is in the insurance domain for CNA insurance. We do end-to-end data management as well as follow a microservices architecture. Additionally, we are using AI and machine learning for tasks such as data discovery, data quality, and generative AI applications for development purposes.

Some of the most valuable features with Informatica Intelligent Cloud Services include master data management, API management, data governance, data quality, and cloud application integration. We can utilize both ETL and ELT capabilities and have the ability to integrate data with different data lakes, even integrating with Snowflake.

The cloud-native platform provides scalability and agility for my current project. Informatica Intelligent Cloud Services allows us to manage the entire data lifecycle in the insurance domain, supporting microservices architecture and leveraging AI and machine learning for data discovery, quality, and integration.

I still have to look into potential improvements, but what I have currently provided input on is what I'm using. I still need to work on it and check for any potential improvements.

One thing to consider is that while Informatica Intelligent Cloud Services already integrates with AI platforms, we need to use these tools rigorously and check all these aspects, including machine learning and analytics. I feel that more work needs to be done on this aspect.

I have almost three years of experience.

Product deployment generally takes place as we have an integrated version with various applications, and while we don't deploy daily, we follow a CI/CD pipeline closely for performance.

Different organizations may have different practices; some might have a release plan, but for my project, we manage deployments every one to two days without experiencing significant delays or issues, so I consider it user-friendly.

The solution is stable as of now.

It is scalable. The solution has good scalability and powerful capabilities.

The initial setup for Informatica Intelligent Cloud Services is straightforward for me because I'm not facing any issues with this.

I am part of the initial setup and deployment.

For the deployment team, we have a production development team that isn't just one person; it's a combination of almost eight or nine teams working together. Depending on the team's requirements, they handle deployments at different times, so around ten people are involved.

The reviewer needed more time to provide information about differences with other integration platforms.

I have experience with Informatica solutions, specifically with Informatica Intelligent Cloud Services. This is for my project basis, as IICS is the cloud-native solution they supply us for this purpose. I focus on ETL, ELT, reusable integration artifacts, and cloud data integration, including Snowflake. Everything is there, including scalability and performance, which are also good.

On a scale of 1-10, I rate Informatica Intelligent Cloud Services a 9 out of 10.

The typical use case of Informatica Intelligent Data Management Cloud (IDMC) is governance activities for data quality and data cataloging. I need to look at the request type from whom I am getting to provide the details.

The most helpful features in Informatica Intelligent Data Management Cloud (IDMC) are data cataloging and the data marketplace.

In terms of data quality management, Informatica Intelligent Data Management Cloud (IDMC) helps with data governance overall.

There are some improvements needed in Informatica Intelligent Data Management Cloud (IDMC), especially from a user experience point of view over the last year. They are improving, and there is still scope for enhancement.

The observability concept in Informatica Intelligent Data Management Cloud (IDMC) needs improvement as the capabilities are not up to the mark compared to the industry.

In terms of cost effectiveness, Informatica Intelligent Data Management Cloud (IDMC) is more than 20% costly compared to the industry.

Regarding AI features in Informatica Intelligent Data Management Cloud (IDMC), there is always improvement and clarity present, but compared to the competitors, they are still lacking. The AI features are helpful, but when compared to its competitors, they need enhancement.

I have been working with Informatica Intelligent Data Management Cloud (IDMC) for five and a half years.

The deployment of Informatica Intelligent Data Management Cloud (IDMC) is user-friendly, but if the software is available, it takes some time.

I have contacted the Informatica technical support team, and they have good technical support, but there is always room for improvements. My experience with Informatica technical support overall rates around three, which is above medium, but not extraordinary.

Informatica Intelligent Data Management Cloud (IDMC) takes about a couple of days to configure, depending on the services.

I am aware of its competitors, and I will provide the details once I know them more thoroughly.

I definitely would recommend the Informatica solution to other people.

I am satisfied with Informatica Intelligent Data Management Cloud (IDMC), even if it might be lacking in some areas. The expectations will be somewhere, and the tool can support somewhere, with opportunities to improve, considering the features they are building and their strengths.

I would rate Informatica Intelligent Data Management Cloud (IDMC) at around seven.

Informatica Intelligent Data Management Cloud (IDMC) is one of the cloud-based data management facilities that Informatica provides for us.

I have been working with this tool for many years, primarily focused on the CDGC, which involves working on on-premise cloud data governance models. Informatica has facilitated managing this with a modern approach, integrating base data quality and data governance. The tool retains the same on-premise architecture, but the terminologies and verbiage are somewhat different, leading us to comparatively start working with cloud-based data governance tools.

Previously, Informatica EDC was separate for managing data and metadata management, while Axon was dedicated to data governance initiatives. The integration of both was needed, but it was too complex for a layman to understand the EDC, integrate with Axon, or vice versa. The cost involved was also quite high, as there were separate licenses for EDC and Axon; utilizing EDC without Axon was not fruitful. We mandatorily needed to purchase licenses for Axon to create a full framework.

In the earlier case, proper separate licenses were required. The technical team had to ensure integration and maintain both environments, which was a difficult task. Now, with the cloud-based data quality, they have merged the two into one element. In on-premise, we call it EDC for metadata management, while in cloud-based technologies, it is known as the Metadata Command Center, which serves the same purpose as EDC concerning CDGC.

The marketplace has also moved to Informatica Intelligent Data Management Cloud (IDMC), forming a cloud data marketplace. They consolidated many tools into one, which is impressive.

The licenses are too expensive compared to before, which is why customers are now preferring other data metadata management tools like OneTrust, Collibra, and Azure Purview. As a matter of POC, they are assessing whether their requirements can meet data governance initiatives. The need for skilled professionals is increasing, but cost remains a significant consideration for organizations when implementing CDGC. If a budget is allocated and they are satisfied with the Informatica CDGC license price, then it is a very good tool to implement.

Regarding improvements, besides the price, the advanced clusters feature in CDGC needs attention. There are advanced scanners features present in both on-premise and cloud, but the setup isn't straightforward; there are lots of dependencies to consider. For instance, an SAP connector dealing with CDGC isn't simple. There are precautions and prerequisites that must be followed; otherwise, it won't work easily compared to the Oracle connector. Thus, resources need to be handled carefully with the advanced scanner configuration guide.

I have been working with this tool for many years.

Informatica Intelligent Data Management Cloud (IDMC) has been very quick and easy to deploy because it only requires a reliable internet connection and a compatible browser. We can do many tasks without needing advanced hardware; a basic laptop or desktop with an updated browser suffices for managing data with IDMC. There is no necessity for extensive engineering tasks to utilize the tools.

The technical support team from Informatica is excellent. If they are unsure how to resolve an issue, they keep customers informed, providing updates about progress and ensuring communication with the product team to deliver accurate responses. This level of intimacy and coordination is excellent.

If the process is set, every organization has its own governance structure for providing access to users, subject to policies and processes. If everything is in place, it takes only about 10 to 15 minutes; it's straightforward.

If user access approval is excluded from the process, it can take less than 5 minutes.

The licenses are too expensive compared to before, which is why customers are now preferring other data metadata management tools like OneTrust, Collibra, and Azure Purview. As a matter of POC, they are assessing whether their requirements can meet data governance initiatives. The need for skilled professionals is increasing, but cost remains a significant consideration for organizations when implementing CDGC. If a budget is allocated and they are satisfied with the Informatica CDGC license price, then it is a very good tool to implement.

Customers are now preferring other data metadata management tools like OneTrust, Collibra, and Azure Purview. As a matter of POC, they are assessing whether their requirements can meet data governance initiatives. The need for skilled professionals is increasing, but cost remains a significant consideration for organizations when implementing CDGC.

I haven't found any difficulties managing different data standards. The team and everyone at Informatica Corporation are friendly, and the documentation is self-learning and self-taught, presented in an understandable format with pictorial representations that help us not to depend on others. The only challenge is reading through around 200 pages of documentation, which can be a pain point.

Informatica is easy to integrate with other tools using APIs. However, it has some difficulties. Informatica appears to build revenue by offering different environments. If you have a rare setup, such as SAP HANA CRM systems, it isn't bundled within a standard license, so purchasing an exclusive license is necessary for integrating such atypical systems. These should be included as part of a single license.

Some connections, like MongoDB, are straightforward, and Oracle works well under a single license, but certain source systems pose challenges in managing connections and integration, such as SAP systems and CRM systems, where licenses must be obtained separately from Informatica.

If you have an SAP environment, it's better to consider alternatives or be aware that additional costs may incur for integration.

On a scale of 1-10, I rate Informatica Intelligent Data Management Cloud (IDMC) a 10 out of 10.