Informatica Intelligent Data Management Cloud (IDMC) Reviews

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. 

The solution is used for data quality implementation. We handle the whole life cycle of data quality from end to end, including rule specification and exception handling.

We're seeing more productivity. The tool provides accelerated libraries that we can use instead of building things from scratch. It's looking very promising from a productivity perspective.

The profiling features are much better than the on-premise version.

We have complex datasets. Logical views are a little bit behind in comparison to the on-premise version.

I have been using the solution for less than three months. I am using the latest version of the solution.

The product is very solid.

I rate the product’s scalability a nine out of ten.

The technical support is very good.

Positive

We have used the on-premise and SaaS versions of the solution. All our on-premise infrastructure is in Informatica. It was a natural move to Informatica Cloud Data Quality. It was easy.

The initial setup is quite straightforward.

The pilot is not in production. It's in a lower environment. We are not at the stage of doing performance testing until the new year. We are not fully utilizing our on-premise infrastructure. Moving to the cloud could save us some money.

People wanting to implement the solution must consider it like launching a new data management program from scratch. Rather than just thinking about migration from on-premise to the cloud, try to adjust your mindset to use the solution as a completely new tool. If you just migrate from the on-premises to the cloud, if you have any technical debt in the on-premise version, you'll just replicate that into the cloud.

Overall, I rate the product a nine out of ten.

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.

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. 

We have a customer with a lot of data. We see a lot of quality issues, so we have implemented a data quality solution on-premises. Since everything is moving to the cloud, we are also moving our data quality solution to the cloud. We are building data quality goals for profiling, matching, and enrichment. There are other use cases, one example is matching data in our on-premises MDM system. To do this, we need to clean the data and profile it to understand its quality. We can then use data quality rules and matching modules to identify and resolve data quality issues. We also use it to validate customer email addresses and phone numbers on the fly in our in-house customer applications. Other use cases include data integration and data governance.

The user-friendliness and performance of Informatica is quite impressive. It’s easier to grasp and understand data quality, and how data governance tools work, as they are built on the same underlying architecture. The QCA addresses performance issues, whether it’s related to data, PowerCenter, or any other aspect. The shared underlying architecture enables me to pinpoint parameters for optimization. Informatica can handle large volumes of data without compromising performance. While other tools may offer similar capabilities, Informatica consistently delivers impressive results.

Informatica Cloud Data Quality provides many of the capabilities found in on-premises solutions. While the cloud edition offers additional features, it has also deprecated certain functionalities, such as connecting to web services. In the on-premises version, features like web service consumer and web service provider are available, but these functionalities are currently unavailable in the cloud edition. Vendors offer options for custom solutions, although they may not offer the same functionality as the on-premises version.

I have been using Informatica Cloud Data Quality for years. We are using the latest version of the solution.

I rate the solution’s stability an eight out of ten.

I rate the solution’s scalability an eight out of ten.

The team is acquiring knowledge. In our previous on-premises environment, we had a team of experienced engineers who were familiar with the system and could provide instant answers. However, in the new cloud environment, there’s a learning curve in the new cloud environment, and it may take some time for them to understand and respond effectively.

Positive

Overall, I rate the solution a seven-point five 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." 

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.