Informatica Intelligent Data Management Cloud (IDMC) Reviews

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. 

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." 

In my company, we use Informatica Cloud Data Integration because I work in the insurance sector, where we use the tool for data migration from one environment to another. We also use Informatica Cloud Data Integration in scenarios where the clients switch from their legacy system to Informatica Cloud using Azure Synapse, because of which we have to migrate all the data to Azure Synapse.

The most valuable features of the solution are pushdown, optimization, and partitioning, which can be used for frequent data loading, especially when we have a huge data volume in our company.

While my company operates on the cloud, we have seen that Informatica Cloud Data Integration has some performance issues causing it to lag. I feel that Informatica is still in the process of improving its cloud. Sometimes, there are connectivity shoes in the product causing it to have multiple issues, because of which the solution hangs or lags. The product may have issues because they are new to the market and are trying to improve their services.

I have been using Informatica Cloud Data Integration for two years. 

Stability-wise, I rate the solution a six out of ten.

The solution can be easily scaled up since we just need to take the aid of some PIM processor and increase the data volume as per what we want.

Scalability-wise, I rate the solution a seven out of ten.

The solution's technical support is good. I rate the technical support a seven out of ten.

Neutral

I have an experience of more than ten years with Informatica PowerCenter.

Informatica Cloud Data Integration's on-premises version is very good, considering the faster performance and efficiency it provides. Informatica Cloud Data Integration's cloud version is also getting enhanced since Informatica has been regularly doing some patching to increase the product's performance. Considering the current scenario, Informatica PowerCenter is a good product, especially its on-premises version.

The deployment doesn't take much time, and only a few minutes are needed to deploy the solution.

I rate the product's price a seven on a scale of one to ten, where one is the cheapest and ten is the most expensive. The product is a bit expensive.

In the cloud, Informatica has migrated to a different environment in a single platform named IDMC, on which one can have cloud data migration, cloud application integration, data quality, and data governance, making everything combined with a single platform. Earlier in our company, we had to rely on different software or platforms for each functionality, but now we can use a single URL and work on a single platform.

I would like to speak about Informatica Cloud Data Integration's performance, which is too slow but works overall as a tool. To those planning to use Informatica Cloud, I would say that it is the second-best product in the marketplace, and soon it will become the leading product.

On a daily basis, the product has been increasing their overall availability and performance .On a weekly basis, the product gives out some new updates while fixing its old defects. It will be a good product in the coming years. Improvements in the current product will continue for some time.

I rate the overall product a seven out of ten.

The solution is robust. We can connect with quality analysis tools for summarizing the data as well. It has all the essential features.

The solution's cloud-based version has a few limitations compared to its on-premise version.

I have been using the solution for eight years.

The solution is highly stable. We never had any issues with the cloud version. I rate its scalability a ten out of ten.

The solution is highly scalable. I rate its pricing an eight or nine out of ten.

The solution's technical support team is well-skilled.

Positive

The solution's initial setup is straightforward. I rate the process a ten out of ten. It takes a little time to deploy and requires minimal staff.

Our vendor manages the solution's implementation.

Our clients evaluated Profisee and Precisely before. Comparatively, Informatica provides better integration with quality analysis, data governance, and data integration tools.

I advise others to know their company's requirements before implementing the solution. I rate it an eight out of ten.

We use the solution for data modularization.

The solution has the best feature for parameterization. We can apply it at the mapping and task flow levels. It helps with auto-linking and dynamic linking options for flexible data integration.

The solution's capability to handle large volumes of data compared to its on-premise version needs improvement. Its features for partitioning and optimization could be better as well. There needs to be more options for partitioning, including database partitioning, pass-through partitioning, etc. Additionally, it doesn't support XML data types for SQL server databases. Also, it captures success and targets rows only within a system-defined area. It should be able to capture and track the number of rows processed by each pipeline.

Presently, users need to write code against the existing APIs to access information data. It is a complicated process due to the limited functionality of APIs. Instead, there could be an option to write queries against repository tables to fetch the data like its on-premise version.

We have been using the solution for two years.

The solution's technical support team could be responsive. We have to contact their global support team to raise new feature requests. Currently, we encounter issues with parameterizing email IDs at the task level to configure features for success, warning, and failure. We sent a request to them back in 2020, but they are yet to add those features.

I rate the solution a seven out of ten.