Informatica Intelligent Data Management Cloud (IDMC) Reviews

We use Informatica Cloud Data Integration because we have multiple sources for data integration, and we transform that data and load it into the database or QuickBooks.

There are fewer than 10 people using this solution in my organization.

I'm an integrator. I'm working with the latest version. 

It's deployed on cloud.

Data integration is the most valuable feature. The ability to connect to any of the sources and enterprise applications makes our lives easier.

We would like to have accessibility to the repository. We had that feature in Informatica PowerCenter, and we were able to have access to the repository query, which helped us build some tools, audit tools, and review tools. In Informatica Cloud, accessing the repository query is a challenge.

I have used this solution for about six years.

It's very stable.

It's scalable. 

We reach out to technical support frequently. We've had good experiences with them.

We previously used PowerCenter, DataStage, and Oracle Data Integrator.

I prefer Informatica Cloud Data Integration because of the ease of connecting to various connectors and various enterprise applications. The UI is also a valuable feature.

Initial setup isn't straightforward or complex. It's about medium difficulty. 

Deployment took about one week. It's not difficult to maintain.

Deployment was done in-house.

From the usage perspective, there is minimum ROI.

The pricing is high compared to other tools on the market.

We chose Informatica Cloud because we have Salesforce. If you're using Data Integration, App Integration, and many other features like operational insights, then the price is worth it. If you're only using the solution for data integration, then the price definitely isn't worth it.

We export APIs, and anyone can seek those APIs to have the data for target use cases. We hit the APIs that are exposed by external systems, and we get the data from them and process it.

We're using the latest version. It's deployed on cloud.

There are about five people who are using this solution in my organization.

We chose Cloud API Integration to connect to Salesforce.

The OAuth feature is the most valuable for authentication.

Recently, we're trying to connect to the Amazon API. There's no direct way to connect to Amazon APIs from Informatica Cloud. From Postman, there's a signature site called AWS Signature, which would actually generate the signatures for us with AWS APIs. In Informatica Cloud, we have to build the logic, and then we can generate the signature and hit the AWS APIs. Instead of generating that signature, it would be better if we could have the same connector in Informatica Cloud.

Sometimes when I open the processes or any service connectors, the labels are not properly displayed. The backend names are displayed rather than the front end label. I usually log out and log back in, and sometimes it disappears, and sometimes it doesn't.

I have used this solution for six years.

It's pretty stable. We don't have any concerns.

It's scalable.

I would rate technical support as six out of ten.

Setup is straightforward. It takes a week to do the installation and configuration.

It's not difficult to maintain.

I would rate this solution as eight out of ten. 

I would recommend this tool to other users.

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.

The most valuable features are data quality, data integrate transformations, match-merge, and a few MDM solutions we build into data quality transformation. 

One area that could use improvement is the speed of the web interfaces. At present, they are very slow. I think it is essential that we are original and robust on-premises.  

I have worked with Informatica Data Quality for the past four and a half years.

You have excellent stability in the market in comparison to other data solutions.

We find that scalability is not an issue and have installed it on fourteen servers.

I have a lot of issues with their customer support and not getting the required technical information, which we actually need unless you can do a call with their senior technicians. Most of the cases that you raise are assigned to a junior technician. 

Positive

The initial setup is simple for a person who knows the company. If you already have one of their products you will find yourself comfortable doing the deployment. If you do not have experience with the company it is medium in relationship to complexity. 

I would continue to encourage the upgrades that are taking place every other one in order to release the new and relevant features. I would rate Informatica Data Quality an eight out of ten.

Axon is a platform that lets everyone see all the data assets, business glossaries, lineage, catalog, classification, etc., but the real activities are being done on EDC.

We have only been using Axon for a short time in my organization, and we are decommissioning it, but I used the solution at my previous organization for data governance projects. Data owners, software developers, and architects all access the solution. In total, we have around 150 to 200 users on it. 

Axon has a marketplace that allows you to showcase all the assets the data owner has, so they can request access. That's one feature we use extensively.  Axon also offers great visibility and intuitive searchability, allowing you to easily search data assets. You can check the lineage and see the work EDC does. EDC handles all the classification, cataloging, and lineage. It's integrated with Axon, and anyone can see that.

Though EDC has maximum coverage, a few things were not available to scan, but I think EDC is evolving to address this issue.

I have been working on Informatica EDC and Axon for nine months.

Axon is stable.

Scaling isn't a problem. It depends on how the implementation is done. Kafka is scalable If the implementation has an image, and it would definitely be scalable on the cloud. We run Axon on our private cloud instead of a Kubernetes cluster. If we wanted to implement it on Kubernetes, we could scale it easily. 

I rate Informatica support eight out of 10. 

We tried an open-source solution, but we stopped it midway through the project and adopted EDC and Axon. We also started using Atlas and Ranger.

Setting up Axon wasn't too complicated. One to three people were involved. It took about three months to implement the solution and onboard various data assets. It was a continuous process, which means we were onboarding new assets as soon as we got them. After deployment, we only need one person for maintenance. They handle this in addition to some other responsibilities.

I can't give an ROI measured in dollars, but we see qualitative benefits. You gain the confidence of our customers because they know their data is secure and only accessible to those with authorization. Data quality is improving. We are gaining our customers' confidence, and they rely on us for data classification.  

EDC comes with an Enterprise Licensing Agreement, whereas Axon is a yearly subscription. That is an expensive affair. I don't know the exact cost because the asset management team handles that. We only tell them how many licenses to get.  The administrator who manages the platform distributes the licenses, and we provide them to data owners who are making policy changes.

Axon is sold separately, and it's subscription-based. Usually, EDC is included in a big data package as an enterprise license. It would be nice if EDC and Axon could be bundled in one enterprise license.

Every tool is evolving. New technology platforms and applications are coming out all the time, like Databricks, Scala, IBM Db2, z/OS, etc. 

I rate Informatica Axon eight out of 10. It's a leading solution in the data governance segment. If you're looking for a quick and easy implementation, I recommend Informatica EDC and Axon.

I use the solution in my company and have worked with two tools from Informatica, both on on-premises and cloud models. I have experience with Informatica Axon, and I have used the cloud services under Informatica CDGC.

The use case of the tool is basically in the banking sector. It is used to optimize certain areas of a project. In Europe, we have a GDPR implementation. In the Middle East, there is the Central Bank of the UAE and UAE data protection law. Implementing data protection laws and ensuring that all the policies are enforced for financial security is my use case. I have to make sure the bank or financial institution meets the requirements of the Central Bank of the UAE. Data quality is another use case where my company handles corporate and retail customer data, and protecting their information is known as PII. The tool ensures that the system holds the PII data properly.

For data lineage, we look for things at a transformation level and how the data is transforming and managing the ETL mapping. Every tool gives this information about how the data goes, but our company is interested in knowing how the data is transforming and the transformation logic between the two data sources.

There are some tool limitations. Some connectors do not scan the metadata properly. If I want to scan the metadata from the data lineage or the Python code, such areas can get tedious in the tool. To manage such areas, one needs to have a lot of coding knowledge, and you need to know how to apply the code, and only then can you get it fixed. There is no user-friendly enterprise.

There are certain shortcomings even if we opt for subscription-based technical support.

I have been using Informatica Axon for four years. I work as a consultant.

It is a stable solution. Once it is implemented, we will handle everything for the business. Stability-wise, I rate the solution an eight out of ten.

It is a highly scalable solution. Scalability-wise, I rate the solution an eight out of ten.

The tool is meant for enterprise-sized businesses.

I rate the technical support a six out of ten.

Neutral

I have experience with Informatica Data Governance.

If one is difficult and ten is easy, I rate the product's initial setup phase an eight out of ten.

The solution is deployed on the cloud, specifically if you have cloud-based virtual machines. If there are compliance issues, and you need to hold the metadata only on-premises models, you can do that, too.

The solution can be deployed in a few days.

It is an expensive solution. I would say it is the most expensive solution in the market.

For the features of the software, the price is justifiable.

Speaking about how the tool enhances data governance tasks, I would say that we have three types of users, consisting of the consumer, who are normally a business user, then we have a developer and finally the admin. Our company onboards the user, and then the user can collaborate, comment, ask questions, give ratings to their assets, publish the data to the marketplace and go for downloads.

One needs to resolve the problem that stems from the fact that different people can have different understandings of the concepts associated with the tool. A database administrator may see an area as a table, but for a business, it may be a person. The tool gives you a common understanding of the business concept. The tool also provides lineage data and how the business data is performing. It provides an interface between organizational policies, the different processes governed by a certain policy, and the different systems governed by which policy. The tool gives you a 360-degree holistic view of the data.

The cloud version of Informatica Axon utilized the AI for the classification purposes, data domain and automatic data tagging. In my company, we use the tool's AI capabilities.

I will recommend the tool to others.

I rate the tool 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.

It's about mastering various domains. For example, an organization might want to master customer data residing within their IT landscape. This includes all master attributes related to customers, like name, email ID, phone number, address, and potential information like stores they visit. Ideally, if the organization also stores data related to those stores, all that information, files, and everything would be included. So, these master attributes would lie within the MDM system.

Here's where things get interesting. I'd bring this data from disparate sources. For example, accounting systems hold phone numbers, email addresses, and maybe even bank information (just an example). 

But another system, like the ER system, might have different information, like the customer's spouse. So, I'd likely get streams from both these systems, but there might be an additional attribute in the ERP system, like the spouse's name.

Now, looking at all these different attributes lying in different systems, one system might have my name as "Raja S.," while another might have it as "Raja Soni." So, the question becomes: are Raja S. and Raja Soni the same person? How do you identify them from these records?

They try to match these records based on phone numbers or any other common attribute between the two systems. Then, I would have a complete record in MDM, which is called the "Golden Record" - the single version of truth within the system.

Furthermore, MDM can also contain additional attributes that the ERP or accounting systems didn't have. So, imagine ten different applications or data sources feeding customer information. I can gather all of that information and create a single version of the truth for a particular customer within the MDM system.

Additionally, if I want my customers to update any information, I can provide a form where they can enrich their data. For example, they could potentially enter their Social Security number (although this wouldn't be common practice). This is just an example of how someone might want to collect this information. So, I can create a form and say, "Okay, can you directly feed this into the MDM system?" This becomes an enrichment opportunity as well.

With Informatica MDM, before you consume the data, you need to check what kind of data it is. There could be some systems where the first name is missing, or the last name is listed as "missing." 

This is where, before consuming or ingesting data into any MDM system, I can filter it out or rather do profiling using Informatica's data quality tool, which is a different tool called IDQ. I can profile the data, okay, and see the current state of the data from our system, any other database, or any source system. 

This is where I'll identify issues like missing phone numbers, incorrect cities, and other things.

So, when I see that the data is incorrect or incomplete (not just incorrect, but also incomplete), I can go back to the source system based on these profiling reports. I would say, "Source XYZ, you have missing data. Can you please ensure that you send it?" So, when I ingest data into the MDM system, I will only ingest it with certain rules. 

These rules define that only records with a first name, last name, city, phone number, and email address should get into MDM. If any of this is missing, I'm not going to let it get into the system.

I would ask the business to review those and then probably ask the system to correct them and then feed them again until the defined criteria are achieved. That's how I improve data quality using Informatica MDM.

Plus, I can also decide that once the data is in MDM, it can feed back to these source systems from where it was originally consuming the data. So, whatever is corrected gets looped back to these sources if they allow me to publish it to them. 

Then, this automatically becomes a cycle: data comes in, gets a data quality check, then enters MDM, and then can be fed back to the source systems if they accept it.

First and foremost, data profiling is a very important aspect, because it allows me to understand the state of the data from the different source systems and applications I'm going to pull into MDM. This is crucial, as it helps me understand the existing data landscape.

Next, Informatica MDM allows you to perform fuzzy matching. Like the example of Raja S and Raja Soni mentioned above. With fuzzy matching, I can define a threshold, and the system will let me know whether these two records are the same or not. 

This is where I think MDM shines - with its strong fuzzy matching algorithm. This is the essence of Informatica MDM. Based on these results, I can write our match conditions and then perform the corresponding data management activities.

When it comes to how data is governed - how accurate it is, what's missing, and what's not.

There are other Informatica tools involved: Informatica Enterprise Data Catalog (EDC) and Informatica Axon, which is specifically a governance tool. With these tools, users can understand data quality. 

Data quality, Informatica EDC, and Axon work together - they "talk" to each other. The moment Axon identifies that data is bad, incomplete, incorrect, or whatever, the governance tool allows you to monitor all of these things and feed them back into the organization.

For example, if someone within the organization is using the governance tool, which is connected to data quality, they might realize: "This particular source isn't giving me certain information, like city data. But I need city information for my XYZ data usage. So, I should focus on another data source or application where customer cities are available." Based on this, data governance can be implemented, and workflows can be established.

MDM also provides workflows. When records come in or if a customer wants to update a record directly in MDM, different personas can be defined. 

A workflow would then be triggered, and someone could review the data being fed through these forms or sources. They might reject or accept it based on predefined criteria. This is how data entering MDM is governed, ensuring everything is correct and meets organizational standards.

I feel the out-of-the-box APIs or the API management could be improved slightly from their current state. It could be more user-friendly.

In future releases, I would love to have more reports and dashboards available within Informatica MDM specifically for master data reporting. 

Currently, there aren't many reporting functionalities offered. While it's true that reporting isn't a core feature of any MDM system, they do have basic dashboard reports. I'd like to see them made more customizable and offer more options for creating reports.

I have been working with it for more than ten years. 

Stability-wise, it's also pretty decent and stable. So, I would rate the stability an eight out of ten. 

I would rate the scalability an eight out of ten. It's pretty good because mastering data across various domains like suppliers is crucial. Informatica is probably the best when it comes to multi-domain solutions.

There are around 500 end users. 

The quality of resources is good, and the time is acceptable. However, the initial quality of resources assigned to support tickets could be better.

Neutral

I'd rate Informatica higher. The biggest reason is the multi-domain capability. Not all vendors offer that flexibility. 

Additionally, Informatica is actively working on a clear, built-in AI engine for its tools. The SaaS version functions well, and they've introduced CLAIRE AI engine for on-premise versions. This engine learns from usage within your organization, further enhancing the experience.

The complexity of the setup depends. For example, it is different for the on-premise version. Now, Informatica has mostly shifted towards a SaaS model, which is essentially plug-and-play. You just need to log in and start using it, making it very simple. 

However, for the on-premise version, the setup process requires some expertise. It's not something just anyone can do. You need experts in place to handle the setup effectively.

Within the organization, businesses have benefited significantly. Tasks that used to take four to six weeks now only take three to four days.

Pricing can vary because Informatica doesn't have a standard price across the board. It depends more on the sales representatives in different regions. They essentially call the shots and decide the discount percentage for each customer.

So, it's quite flexible, like a bargain, in a way, but it depends on the salesperson.

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

I absolutely recommend it. If you're looking to implement a Master Data Management (MDM) solution, Informatica MDM is the most flexible tool available in the market. Compared to its competitors, it offers several advantages.

Firstly, it provides the option to move seamlessly between on-premise and SaaS versions. If you start with the on-premise version, you can easily transition to the SaaS model later, if needed.