Informatica Intelligent Data Management Cloud (IDMC) Reviews

We use Informatica as our one-stop cloud integration solution. We are connecting two different data systems like SAP or Salesforce, integrating on-site financial data with some applications that process financial information like an ERP to plan budgets and forecast revenue. I'm creating a data warehouse and using that with tools like Power BI to create reports and dashboards.

Informatica is good for integrating data and cloud applications. We have connectors for integrating cloud applications like Salesforce. You can quickly integrate anything with an exposed API or a REST API.  The industry is increasingly shifting to the cloud, so we need more products that can connect to cloud-based applications. The integration is seamless and works in real time. It's also secure because you don't need to expose databases or tables. 

There is room for improvement at the highest level in terms of useability and connectors for various types of new applications. The row processing performance could be better because you experience some latency dealing with high volumes of data. Most organizations will be dealing with multiple cloud applications, so you could see performance issues moving from one system to another. 

I have worked with Informatica products for about seven or eight years.

I rate Cloud Data Integration seven out of 10 for stability. Even though Informatica is based in the cloud, it still depends on a secure agent deployed on-prem. The performance of the individual machine where you install the gateway can affect the stability. 

I rate Cloud Data Integration eight out of 10 for scalability. 

I rate Informatica support eight out of 10. They have a dedicated customer success portal where you can search a lot of knowledge base articles that tell you how to use Informatica in various scenarios. You can also use the portal to raise a support ticket describing your scenario, what you hope to achieve, and where you're having problems. The technical support responsiveness and dedicated customer support are decent enough. 

Positive

I have some experience with Azure Data Factory, and I worked with another Informatica product called PowerCenter. I switched to Informatica Cloud Data Integration because my customers' needs changed. More companies began prioritizing clouds. As developers, we also needed to scale up our services.

I'm not aware of the exact licensing cost because the senior management handles that, but I know that products like Azure Data Factory are less expensive than Informatica. At the same time, Informatica gives you more options and connectors. Everything is possible, whether you want to do API-based integration, traditional data integration, etc. 

Azure Data Factory is more useful for connecting with the Azure cloud. You can also play around with the native data integration tools that other cloud providers offer, like GCP or AWS.

I rate Informatica Cloud Data Integration eight out of 10. I recommend Informatica as an integration tool for large and medium-sized enterprises migrating to the cloud. Cost is something to keep in mind because Informatica might be too pricey for smaller companies. Large and medium-sized companies can easily afford it if they plan to use it as a one-stop solution for all their cloud integration and management needs.

If you need a middle layer to build your pipeline, that can be achieved using Azure Data Factory or any other data integration tool. Still, Informatica has some advantages in terms of connectors and help desk resources. When you contact the Informatica help desk, you can speak with engineers who helped develop the Informatica platform, so they have rich experience with integration technologies. The product itself is highly reliable and suitable for your integration needs. 

Azure Data Factory has almost 80 percent of the same features, and it might work for you if you have a Microsoft ecosystem. Using Data Factory is cheaper. However, if you're a large or medium-sized organization or a reseller and your data is more valuable, it might make more sense to use Informatica at that scale. You'll be interacting with many more types of applications and might need to integrate the applications in various ways.

We primarily use the solution for data integration as well as application integration.

Currently, we have a multiple-source system. With Oracle ERP, it's very hard to pull data from Oracle ERP, since it's API based. Therefore we have to make use of Informatica Application Integration to pull data from Oracle ERP.

We are pulling our supplier information, customer data, and data for projects related to information.

It's a beautiful tool. It eliminates the use of a third party or some other tool as it provides beautiful functionality.

In an earlier version, when we are working with an on-premise tool, it was hard since we had to concentrate on the configuration, et cetera. However, now, when we are working with the cloud, most of the time it's very easy to handle.

Data integration happens seamlessly and is pretty fast.

The application integration will give you more flexibility when dealing with APIs.

Every week, we seem to get notifications of new features and updates. They are always trying to improve the product. 

When we're dealing with huge amounts of data, we need to reach out to Informatica. There are some kinds of API records that we need to modify configurations for. 

Application integration, as compared to data, is a bit slow, and it can't handle millions of records.

There could be a lot more application integration. Like data integration, we find there are a lot of places where we can go ahead and improve performance. When it comes to application integration as a developer, we don't have that much flexibility. We have constraints. Our hands are tied, and we can't do anything. We need to speak with either the target system or the core system and say, "Okay, do this thing." 

I've been working with the solution for more than two years. 

It is a stable, reliable product. Sometimes there is a bug or a cache issue. We might get an error when connecting and when that happens, we reach out to support. We find it's a pretty common issue. It might be a glitch. 

It is very scalable. We have some challenges around application integration, and that requires speaking with support. We tell them we need to change a configuration to a specific size. They can assist us when that happens. That's only for application integration, not data integration. With the cloud, we can easily handle millions of records of data.

A variety of people use the solution in our company. There are 50 to 500 users, approximately. 

Technical support seems to take their time in responding. We'd like a faster reaction time. Sometimes it takes one to two days to get a response.

Positive

We did not previously use a different solution. Currently, we're working with data and application integration with Informatica only.

We've mostly dealt with on-premises deployments. As a developer, most of the time, we face a lot of challenges, which are overcome when it comes to Informatica Cloud. Informatica Cloud is much more convenient, as well as fast, and gives good performance as well. With the cloud, we don't have to install software and there aren't any dependencies. It's much more convenient and easy with Informatica Cloud.

The deployment hardly takes more than a few minutes. 

When it comes to the cloud, there is no maintenance necessary. Informatica takes care of any support tasks. They do the updating, et cetera, as well. 

We handled the initial setup in-house. There were no third parties involved in the deployment process. 

We have not looked at ROI.

The solution is subscription-based. Users are charged based on how many resources are used.

The pricing is pretty good. I'd rate it 8.5 to nine out of ten. 

We did not evaluate any other solutions previously. Our client already had this as a pre-existing tool and did not want to switch. 

I'm working with the latest version of the solution. 

Informatica is a stable, easy tool. New users should go and explore it. There is improvement required in Informatica Application Integration; not the cloud.

I'd rate the solution eight out of ten. We're quite happy with the product. 

I'm a freelance consultant, so I work for a few different clients on different projects. Sometimes I do system integrations, and sometimes it's more of the deployment of the tool itself. 

Informatica Axon is mainly used for master data management because it's quite a powerful tool. Lots of clients are struggling because Collibra is not an MDM tool. Azure has some possibilities in the data factory for MDM, but in the end, it doesn't have the engine that Informatica has. I see quite a few clients bring Informatica into the architecture for ETL processes. They use it to extract, transfer, and load data, in addition to MDM, since they're a bit restricted with other tools.

I think definitely what my clients find strong about the solution is all of the processes that it can do. You can extract and transfer your data as you wish it to be consumed later. I definitely hear that this adds value to the tool. 

Another thing I hear clients say is that you can use the MDM modeling functionality as a kind of engine to do data cleansing before you consume the data.

Also, for example, Collibra works closely together with Azure, which works closely together with Informatica and Google because the clients have needs that can't be fulfilled all by one platform. Solutions needs to fit into that architecture, and Informatica can fit in there, and that's appreciated in the market.

There is always room for improvement in making the look and feel more user-friendly. There are also some technical issues sometimes with integrations because clients have a lot of different types of data sources.

One thing I miss with Informatica is the sandbox environment. I do freelance consulting, meaning I give trainings, and sometimes clients ask me to give a training in my own environment, my sandbox environment. 

I have an environment that Collibra provides me with for certifications of training, so I can use a kind of sandbox to actually show a few things to clients.  I have the same thing with Microsoft. With Informatica, it's a bit more difficult. They're not that willing to provide the sandbox to an individual consultant, so I'm just on my own. That's a bit of a pity because sometimes if a client has something that is not configured, I can quickly configure it in my own environment and then show it in a demo. I don't have that opportunity with Informatica. I have to work on the client's system, which then sometimes causes security problems.

I don't have complaints about the stability, and I don't see it as a big issue coming up with my clients. The app sometimes had issues for some clients but it was not business critical or actually impacting them.

I think it is scalable, but that is not really the focus of my work. There are a lot of reasons I can give that the scalability might be affected, but they are actually not really related to the tool itself, but just how you build it in.

If I have a problem with the client and I'm a bit stuck, the support is really good. I can fall back on the people from support and they're quite willing to help. 

It can also help the client because I do a project for six or twelve months, and then I'm gone. If the client has a question after that, they can talk to the support and it's really good. 

From what I have experienced, I would give the technical support an eight out of ten. 

Positive

I think the setup is quite easy if you are data-minded. If you don't have any clue about data management or don't have that background, you're not going to be able to do it. You need to have a bit of technical understanding to do it in the correct way. If you're completely new and you don't have that background of experience, then it's a bit harder, and you'll need to follow a step-by-step plan.

I see clients starting to set it up from scratch and it takes three years. If a client says they want to deploy it within their whole organization, then, in general, you need to count about three years because it's not only the tool. You also need to set up your governance and your organization on it. All of your processes need to be aligned with the tool, so it's a three-year program in general.

Both for the business end users and for the technical people, the maintenance is more on the technical side. For example, for the API connections, the batch processes, and the real-time processes, it's not always easy. One of the things that I always say to my clients is that they need to document everything, and that helps. I tell them to build into their project a documentation pillar where they document everything that they do, like their MDM and rules. It's easier if they have good documentation, but it's still a challenge. Without documentation, it's hard.

I think definitely starting it up gradually, meaning don't buy the tool and then start trying to put everything in from the beginning. First, think about: What do I want to bring into the tool? Which sources do I want to go integrate with the tool? Which data, which business areas do I want to cover with that? You need to do a modeling exercise. You need to do some preparation work first and take it slow. Start small, take a specific business unit or data domain, and then show the value for your business. Then the budget will come, and you can do more with the tool. 

I rate this solution as an eight out of ten.

A lot of times, we use it for basic profiling. That's its most common use case. Currently, we are also in the process of establishing a set of ongoing processes around Data Quality that would feed into and augment our current metadata. So, from that standpoint, our usage is primarily around some of the basic dimensions of data quality, such as completeness, conformity, consistency, timeliness, accuracy, etc. We measure each of those or at least create quality rules that measure each of those aspects. We're in the process of doing this for all of the data that's currently feeding into our analytics engine. These are some use cases that we're currently doing on a daily basis.

There are a couple of valuable features. One is that it is very quick on the profiling. So, you get a very fast snapshot of the type of data that you're looking at from the profiling perspective. It can highlight anomalies in the data.

The other valuable feature of the Data Quality tool is the flexibility of using their Analyst tool to create a mapping specification, which allows you to join multiple sources of information. You can then create rules within that data set. You can apply aggregations and all other types of functions, and then you can feed that into the profiling tool. From the profiling tool, you can then create your scorecards. It can be two-step where you're using that mapping engine to integrate multiple sources. If you don't have a need for that, you can do a lot more sophisticated mappings inside their Developer tool, and then maybe do an analyst type of mapping engine. So, you can do straightforward data quality within the Analyst tool, or you can do more sophisticated data quality within the Developer tool, at least as far as the rules are concerned.

Their UI needs improvement. Their scorecards and reporting also need improvement. Their data quality reporting, especially their dashboards and scorecards, is lackluster at best. Its reporting capabilities are limited. If you want to do anything beyond its limited reporting capabilities, then you're going to have to use an external reporting tool such as Power BI or something like that.

It has a few glitches that they haven't fixed. For example, while creating a new scorecard, when you get up to a point, you have to stop and save what you've done. You have to exit and then go back into the tool to finish up your work. From the development aspect, using their scorecard tool has a couple of glitches in it. This might be a tool that they're going to eventually phase out. So, they're just not doing a lot of work on it. I've been living with it for a few years now. I've learned that I got to save my work, and then I got to get back into it to finish up what I was doing.

I have been using this solution for at least five years.

It is pretty stable.

As far as I know, it scales pretty well. The part of the problem that we have is with the way it saves the results. When it saves the result, it creates a physical copy of some of the data results and stores it. So, when we're processing, for example, 500 million rows of data, depending on the type of rules that we have and how we're doing it, it can quickly use up a lot of space. We've had some issues with some of the space and storage. It scales, but you still have to be careful how you configure it so that you don't use up all your resources. We've added a lot of disk space, and we still occasionally have problems.

Currently, we have maybe half a dozen heavy users, but we're probably going to scale that up to 20 to 25.

It is straightforward.

I would rate it a six out of ten.

We are using it to understand the assets that we have from their technical metadata perspective, but we're also using it to align our business glossaries with the actual physical data location where the data is stored. Using their Claire or AI engine helps facilitate that. We've been doing that for a while.

The other thing we're trying to do is extend that metadata capability to include extended lineage and provenance attributes. We're trying to incorporate those into the existing EDC environment, and hopefully, when we get Axon, we'll try to figure out how we would expose that to the customer. We will figure out whether we're going to expose that directly or whether we're going to have to augment Axon with an additional UI layer.

From my standpoint, Informatica offers a pretty robust set of scanning tools that can scan a variety of sources of information. It offers a central repository that you can go to for interrogating and finding data. You can find the data that you're looking for based on enhanced metadata.

The other thing that we're working on is extending the existing Informatica data quality capabilities within the EDC so that we have a more robust understanding of not only what the data is and where it is located but also the quality of that data. We are doing this so that when people are looking for data, they see not only where they can find the data, but they also feel confident that the data is going to meet their needs.

The capability of the tool to scan and capture the metadata from a variety of sources is one of the capabilities that I find most useful. The central repository into which it is going to put that captured metadata is the best.

The model is somewhat flexible. There are certain aspects of the model that are not as flexible as we would like. It doesn't do certain things to a great level of depth. So, in situations where we want to drill in to do something specific, we have to essentially copy that data into our own structures in order to add that additional layer of flexibility.

Robust process management or workflow management, like Bonita, should be incorporated into the Informatica tool stack because it offers very simplistic workflow capabilities. If we had more dynamic and robust workflow capabilities, we could make use of that a lot more. Currently, we have to do a lot of pre-work outside of the Informatica tools before we can get the data loaded and start using it because they're UIs. I haven't dealt with Axon. So, I don't know exactly how that's going to change things, but with the EDC tool, I can't say the user interface is useless, but people don't use it because they find it cumbersome.

Its UI, without considering Axon, is probably their least desirable part. It has some interesting capabilities, but it is not what I would call cutting edge or super. It is not as intuitive as I would've expected. Its UI is probably prior to Axon. It is a little dated, and even Axon has been out there for a while now, but it is a little dated. That's probably why they went out and bought the company that originally made Axon.

I have been using this solution for five or six years.

It is very stable.

We're using it for our enterprise. We are not the largest enterprise in the world, but we're a pretty good size. We have 20,000 people working here and petabytes of data flowing through various systems. It is less about the people using it directly as opposed to the systems using it. It is really a matter of the system interfaces that are automating. We're trying to automate the metadata as much as possible so that when people are looking at their data, they can also see the associated metadata. Sometimes, we have to pull the metadata out of EDC and feed it into other systems so that as they're using these other systems, they can see the metadata flows with it.

I haven't dealt much with them directly, but I've had colleagues create tickets all the time. Generally, they're pretty good. 

I'm more from the end-user perspective. From a system standpoint, there is a different team that sets things up on the server and establishes various types of configurations. I do work with them, but I'm not actually doing that work. 

They have three people that are actively managing the system, and they are system administrators. There are also various people who might be testing things at any one point in time, and then there are various analysts who might be creating data to feed into the system, such as definitions of business terms. The same people may review the results once it gets into the engine. When it starts to process that data and makes the associations between the terms and the actual metadata where it is linking the two up, somebody has to go in and validate that, especially the exceptions or the ones that don't have a high enough matching score. So, there are probably three or four system admin folks, and those are more technical folks, and then you have maybe 20 people who might be putting in data, validating the data, and so on. Those are still primarily an IT function. They have subject matter expertise, but they're still reporting up through the IT group, and then, we'll eventually get to the point where we have a more robust set of business users who are reviewing and vetting that information.

I have no idea what the price actually is. It is probably not going to be the cheapest, but it is a pretty stable and robust platform from the backend standpoint. 

I would rate it an eight out of ten.

We use the solution for data cataloging and lineage.

The valuable feature is metadata management. If you want to trace sensitive data, you can auto-classify them. You can search for sensitive information through EDC. Using Discovery, you can identify if there is any type of data set.

EDC is only used for cataloging and meetings. Currently, not many different databases are integrated with it. The ETL logic should be visible within the tool. If EDC is integrated with Axon, you will see the data lineage and catalog because of EDC. If EDC is removed, you cannot see any lineage or catalog.

We need EDC on the application level. Currently, it is only at a system level. It can be shown as an application layer between policies or regulations. This kind of feature can be enabled.

The integration with other data management tools can be enhanced. For instance, there is no integration with tools like Collibra or Hubview.

I have been using Informatica Enterprise Data Catalog for seven years.

The solution is stable.

EDC needs many ports to run. Therefore, to improve performance, you need to increase the number of cores.

The initial setup is easy and takes two weeks to complete. One person is enough to deploy it.

EDC gives you a better understanding of architecture and the system used. You cannot use EDC completely unless you have access.

The product is expensive.

EDC is a standalone application, so it doesn’t really make much sense. It doesn’t give you a lot of features that way; it’s primarily for cataloging and classification. Nobody would buy EDC as a standalone application. When people are buying EDC, it’s always with Axon.

I recommend buying the solution altogether with Axon.

Overall, I rate the solution as seven out of ten.

Informatica Axon is typically used for data governance, where we establish policies and business glossaries.

Users can benefit from Axon's business lineage. Axon has something called Overlay, which can be used to extend the technical lineage. You can also extend it to stakeholders, all the compliance policies, and all the overlaying business rules. All of these can be seen in the same lineage.

I want to get a deeper understanding of the logic that is being used in the ETL. Informatica Axon does not provide complete transparency about the level of detailing you need and the logic used in ETL. I cannot use Google Fusion for ETL because I don't have a connector within Informatica Axon. I am stuck if I don't have a connector with an ETL tool.

One of the biggest disadvantages of Informatica is that it's very expensive. Many clients don't want to go with Informatica Axon because of its cost structure. You have to buy every component separately. If a customer wants governance, they obviously would need cataloging and lineage also.

Other tools like Collibra have cataloging, lineage, and governance in the same package. You don't need to buy them separately. With Informatica, you have to buy Axon for governance and EDC for cataloging and lineage. So, it becomes difficult for customers to pay for every component.

I have been using Informatica Axon for seven years.

We haven’t faced any stability issues with Informatica Axon.

Scalability is not really a factor in data governance because the volume and storage of metadata is not more than a couple of gigabytes.

The solution provides good technical support.

Positive

The solution's initial setup is easy, but you typically need Informatica support to run some of the preliminary tests. I've heard that the solution will be available on the cloud. Once it is available as software as a service (SaaS), you don't need to install it; you only need to configure it.

Typically, it takes around two days for the solution's installation and integration with the entire Active Directory and EDC. Then, it may take another day for testing. One engineer is enough to do all this work.

Only using Axon will not give you all the benefits, but you will see benefits when Axon, EDC, and IDQ are all plugged in together. It is difficult to get benefits from Axon as a standalone solution.

The solution's pricing model is easy, but it is very expensive.

Informatica Axon has a Marketplace that comes at an additional cost. Other tools like Collibra have built-in collaboration features, and you don't need to buy them separately. Collaboration features like collaborating on data sets, tagging them, and making a request are available in the Informatica Axon Marketplace.

It's not difficult to maintain the tool as long as someone has been trained in administration because Axon has many components. You need to understand how to change the logo of your screen size and how to change the dashboard wizard.

You also need to understand the product's configurations with other tools, personalize your dashboard, customize it, and add roles. There is a learning curve there. You need to train a person to be an administrator, and once they are trained, they can do the role better.

There is an NLP enabled with the solution, but it is not really that great. There is still room for growth in Informatica Axon's AI space.

Although expensive, Informatica Axon is very easy to learn, and you don't need to customize it much. Once you have the tool, you start uploading things. The solution is very easy to use because it has proper documentation and user guides.

If you want to set up business rules, Informatica Axon will give you a readymade template and ask you to start filling it out. You will be told about the mandatory and non-mandatory fields, and as long as you understand what the fields are required for, it's very easy to start filling out. Someone new to governance can get the hang of it and easily start filling in the details. You can have Axon implemented very quickly.

I am more involved with governance, cataloging lineage, EDC, and IDQ. I am getting a little bit into master data management and data security.

Overall, I rate the solution an eight out of ten.

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.