Informatica Intelligent Data Management Cloud (IDMC) Reviews

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 The cloud interface looks modern with DDGC, but the on-premise one is a bit outdated.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.

We have used it to elaborate on a project for Golden Record, for IDQ data quality, and create business cases for the control parties.

The product could be improved in the area of architecture and technology. All the parts are old.

I would like to migrate it to a new technology. It's better to have a new method for every IT system. Integrate with API, Kafka, and JMS, as API has in Jira code or Jira.

I have used Informatica MDM for ten years. I have used Version 8.6 and 9.1 of this solution.

I think that it's a good solution, but it's an old solution. It is stable because we have the experience to deploy this solution.

I think it's based on ODBC drivers, and they are not good for scalability.

We have not contacted customer service.

The initial setup was complex. It must have taken around eight or nine months to deploy all Informatica systems. The deployment processes for on-premise and cloud are different. For on-premise, we must deploy the data models, and then we must deploy the rules. We must also deploy a .Go file extension and an exposition of the vendor code, among other things.

It's different if we have an on-premise solution. The stability is for the on-premise infrastructure. It builds the IT of enterprises of clients because Informatica is at 'Legacy' level.

So, there are plans for licensing. There are subscription-based and usage-based licenses. Also, there are licenses for exceptional analytics, etc. In short, there are different models of licensing for every enterprise.

I think there is a new Jira-driven event, Jira-driven data, for the flows and extensions. So in this power center technology, I think that it's complex to navigate through the solution without new technology. Overall, I rate the solution a seven out of ten.

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.

Informatica MDM is used for customer affiliations and sites and hierarchy management.

Informatica MDM has helped our organization because we now work from a harmonized single record for customers and patients. We're able to have an aggregation or hierarchy view, which drives a whole variety of things, such as data quality and data reliability.

The most valuable feature of Informatica MDM is hierarchy management. It's not something that's typically available and effective within ERPs.

Informatica MDM can improve the data catalog and data marketplace.

Informatica MDM is expanding in AI by making it much more efficient and automated stewardship-related work. I'd like to see that accelerated and made available for on-premise.

I have been using Informatica MDM for approximately four years.

The stability of Informatica MDM is good.

Informatica MDM is scalable.

We have approximately 100 people using this solution in my company and their roles vary. For example, we have stewards, data analysts, platform administrators, architects, developers, and support teams. The active users are primarily stewards and data analysts.

We rate Informatica MDM support on an annual basis and we have quarterly executive business reviews where we discuss support issues.

Positive

We previously used SAP for MDM. We switched because there were new use cases that SAP couldn't meet. 

The initial setup of Informatica MDM is more complex than it should be.

We used a third party for the implementation of Informatica MDM.

We have approximately 20 people that do the maintenance and support of the solution. This includes support admin teams and architect engineers. We have a BA for each of the domains, shared architecture, shared engineers, ETL, and MDM. Additionally, we rely heavily on third-party assistance.

We have seen an ROI in duplicate data management, visibility to market share, and reduction in risk.

I rate the licensing cost of Informatica MDM a five out of ten.

We did go through a solution selection and RFP process before we chose Informatica MDM.

My advice to others is for them to look at SaaS and cloud options first.

I rate Informatica MDM a seven out of ten.

Informatica MDM has a lot coming ahead from a solution standpoint, which is a benefit for them. However, there's a lot of configuration involved to keep the solution going and this is why I would encourage a SaaS solution for anyone coming in.

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. 

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."