Informatica Intelligent Data Management Cloud (IDMC) Reviews

We use Informatica to extract data from production. It's mainly the configuration of the systems. We extract data from one site in the same system and mass customer data to the test system. It's a list of customers that is a subset of the data. 

In our CRM, we have data on our customers, and the data in that system is a copy of the production environment. We have all the configurations manually modified to work as a test, but we can extract only new clients, which is a problem. We need to have a CRM with only the configuration but not the customers. 

My plan is to make Informatica work with the CRM. If it works, I am sure that I can get the budget to make it work with other systems, but the first system that I need to work with is CRM. The other ones are less important and don't have the same impact of making it work with the CRM. We're at about 80 percent completion with this project. 

Informatica is user-friendly, stable, and scalable. These three aspects are the most valuable for us. If we can make it work well with the CRM, we can continue or try again this year to complete that part of the project. I am confident that this kind of solution is what we need because we have a third-party resource that works in the development. 

We need to have a lot of hardware infrastructure to make our test system work. We use a complete copy of the production environment for testing, which is a real problem for us. The problem is not with Informatica as a provider per se. It's more of an issue with our system providers, but we need support to complete the projects. That's our real situation. With the pandemic, we have undergone many changes that have slowed down these projects in the last few years.

We have the license to deploy Informatica on any system we want. Right now, we aren't using it as much as we thought we would. We understand the solution, but it isn't easy in some areas. One problem is we have systems in which we don't know all the relationships among the data, and we need support from our providers to get that information. That is one of our most complicated problems implementing it.

Informatica's support understands the solution, but they lack the experience we need for our use case. That was one thing that we prioritized when we started searching for this kind of solution. 

Informatica is working well, but we need access to the production databases to extract the data. The real problem is understanding the data in the system that we don't own or provide. Those are the most problematic to implement with this solution. We make it work with what our team has developed. Deployment took four to six months. Right now, we have one developer and two or three system administrators using Informatica, so it takes about three or four people to deploy and manage the solution. 

We worked with the team we used for all the installations. They made it work. I can say that wasn't as good as I expected when we purchased the solution. When we started the implementation, we understood that they didn't have the research that works in this project because we did a lot of work to understand the data from the implementation team. That wasn't what we wanted because we knew that Informatica would be the most complicated of all the systems we would implement in the extraction. After all, they are our clients, and almost all other systems depend on that system. 

We have a perpetual license for Informatica, and we only need to pay for support. However, because we're not actively using it at the moment, we don't have the budget to continue with the support.

We evaluated several solutions, including IBM InfoSphere Optim and Delphix. We purchased 15 licenses at the same time that totaled around $230,000.

I rate Informatica Persistent Data Masking eight out of 10. It's important to have a robust understanding of the knowledge you need in the system. That is one of the main problems that we have. You need firm control over the systems to do data masking. All of the data is a subset because we want to make the subset and the data masking, but you have to have good control over the systems. I think that is the key.

Each use case is basically based on two pillars, one is MDM tools (for better consolidated integrated master data) and the second pillar is the e-governance (the quality dimension).

You can build something, you can expose it as a service, which can be consumed by anyone. But there is a good benefit that helps us on boarding new applications, having a seamless connectivity between these. Our center which plays a very good role in ATL, also works well. It has an ADT Meta Data Management business glossary, for filing those kind of components, and it helped us in really maturing from a governance point of view and from an infrastructure set-up point of view.

It gives you accountability to centralize your data and have it available to different applications. Its not limited up to specific platform, SAP for example is quite rigid in connecting outside SAP, and within SAP it is fantastic. 

I think they should work really hard on UI.

The stability is considered "number one." The agility is much better as compared to others.

The scalability of the solution is excellent.

Tech support is good. 

The initial setup was straightforward. If you want to use immediate features, it has out-of-the-box features. But, if you want to play around with it, then it can get more complex.

It's expensive, but if you're looking for a stable solution, Informatica is a good one to choose.

We looked at Informatica MDM vs SAP (solutions) and Informatica MDM vs Microsoft MDS.

In the past, people used Informatica MDM because it was one of the leading tools at that point in time to work on an on-premises model. It was the leading tool in the market, offering vast customization options and matching and merging options that were better compared to other solutions. The way the data lineage or the customization options it gave, the tool was good. Now, the tool has a changed dynamic, and it is currently available on the cloud. Apart from Informatica MDM, there are other tools, as well as the market, that are completely on the cloud.

The feature of the product that has been most helpful in improving data quality revolves around Informatica DQ or IDQ, which is one of the tools Informatica offers. IDQ helps cater to the scoring of the data, clean up the data, and integrate it.

In terms of the room for improvement required in the product, I would say that the product is still in the maturing phase, specifically on the cloud. The product is not too user-friendly to do the configurations. There are a couple of areas in the product where I feel improvements can be made, but if I consider the algorithms used in the match process, the tool is very robust.

I have been using Informatica MDM for nine to ten years. My company is a partner of Informatica, and we also implement the tool.

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

In terms of scalability, I would say that the product is currently more available in the cloud. Scalability-wise, I rate the solution a five out of ten. The scalability on the cloud is good. On the cloud, by default, the tool becomes quite scalable.

Informatica provides premium products. The product is mainly meant for enterprise-level customers because the tool is available at the premium cost.

I have not used the technical support provided by the product. If you asked me about the support offered for the tool's on-premises model, I would say they were really good. We really liked Informatica's support. With the cloud, I'm not aware of the support.

If one is a difficult setup and ten is easy, I rate the product's initial setup phase as a five.

If you are talking about the product on the cloud, the initial configuration must be done. There is quite a lot of refresh which takes place every time we have to publish something, and then we have to wait for some time. It is not that you make a change, but you can see it immediately in the tool. The wait period is quite long. If you are a first timer, it takes time for that person to figure out the product. Once you get the hang of it, then it becomes easy.

An end-to-end implementation would take nine to ten months, or maybe a little more than that, depending upon the customer's desired features.

The tool does give a good ROI.

The product is not very pocket-friendly for small and medium-sized businesses, and it is understandable because of the kind of features the tool gives.

On a scale of one to ten, if one is a cheap solution and ten is an expensive one, I rate the tool's price as seven.

Informatica MDM is able to handle complex data integration scenarios with the help of IDCM, which offers the power of an ETL tool.

Speaking about the AI part, I feel the product is still in the maturing phase.

My first preference would be for Welkio, and only after that would I prefer Informatica MDM, so I would say that it won't be my first choice.

I rate the tool a seven out of ten.

At our company, we use Informatica MDM for data architecture and numerous other aspects of data warehouse, such as data designing or modeling. We recommend Informatica products to our customers based on their use cases, data volume, budget, and environment suitability.  

The product isn't mature enough to provide suitable connectors to various data engines. The performance of the solution in transporting data between data lakes and Big data infrastructure is not up to the mark. At our company, we use a similar solution called Spark, which is much better than Informatica MDM in terms of performance and speed. The data support capabilities of Informatica MDM can be improved. 

It's a stable product without any bugs or glitches. But overall Kafka is a better product than Informatica MDM. 

The product exhibits satisfying scalability. 

The tech support is satisfying from the vendor. The support team was able to respond and solve the issues in a very short time. I would rate the tech support an eight out of ten. 

Positive

Informatica MDM easily integrates with the Informatica environment as well as with third-party tools. But for some integrations, for instance, when Informatica MDM is integrated with SAP, the functionality slows down drastically; the task that would take five minutes with Spark takes five hours with Informatica MDM.

The same scenario occurs when Kafka is integrated with Informatica MDM. For popular database integrations such as Oracle and SQL, Informatica MDM functions well enough. 

The initial setup of the solution is straightforward. Our company professionals suggest that Informatica MDM should be on a Linux server instead of Windows. 

The product is very expensive compared to other competitor tools. Informatica MDM is not meant for small businesses but rather used by large enterprises like banks or government organizations. Informatica MDM can be purchased as a stand-alone product and doesn't come in an Informatica bundle. The support for Informatica MDM is not included in the standard licensing fees and must be purchased separately.  

I recommend that others test Informatica MDM with different integration tools before making a purchase decision. I also advise others to evaluate the use cases before choosing a product. Other popular competitor tools in the market include Collibra and SAP MDG. 

Informatica MDM fits our organization's use cases perfectly. When a customer of our company has a plethora of business products, VOCs need to be implemented, even if it's a different MDM solution than Informatica MDM.

Some customers depend on a single technology ERP, while others work with Oracle ERP or SAP, and sometimes they are using ERP and Informatica MDM in combination or migrating from one ERP to another. These use cases will impact the setup that already exists on the customer's site, and thus, the solution needs to be configured accordingly. I would rate Informatica MDM as eight out of ten. 

 A stand-alone application used for any integration with third party tools, like Oracle, it may be SAP, or it can be their local in-house build, or HRMS. It can be any application from A to Z. But this acts like in the web services. 

The data mapping capability is a valuable feature. 

Certain applications aren't accepted. For example, for IBM applications, it takes a lot of time. And certain applications are not being synced with the ION.

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

Its a very stable solution. 

There are twenty five customers at present. 

The initial setup is straightforward. The deployment takes two hours time. 

It's an expensive solution. 

Overall, I rate the solution an eight 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. 

My company does not specifically focus on selling the solution to a particular sector. My company generally sells the solution to various sectors, including manufacturing and finance.

Informatica Enterprise Data Catalog is a compact tool. Many products operate as a data system solution or as a data catalog tool. The product is helpful when it comes to data transformation. If you would like to solve specific data-related problems, Informatica Enterprise Data Catalog can serve as a useful product.

I cannot specifically point out an area in Informatica Enterprise Data Catalog that needs improvement since it properly functions as a data catalog tool. You cannot use something like Atlas to get catalog ranges, making Informatica Enterprise Data Catalog a comparatively powerful tool. The UX and UI of the solution are areas with certain shortcomings where improvements can be made in the future.

If I want to get some extra features in the product, I can contact Informatica's research and development team, who can add some new features to the product and make the tool available in the market.

I have been using Informatica Enterprise Data Catalog for more than ten years. My company has a partnership with Informatica.

Stability-wise, I rate the solution an eight out of ten.

Though there are some bugs in the solution, Informatica's research and development team, along with Informatica's customer service team, supports my company in solving such bug-related issues.

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

Informatica Enterprise Data Catalog can be a good solution that is really easy to use when it comes to enterprise-sized companies, but it may pose some challenges for small and medium-sized companies.

Most companies to whom my company provides the solution are enterprise-sized businesses.

I rate the technical support a ten out of ten. My company has a partnership with Informatica, and we provide Informatica's products to the end users or customers. Informatica has to provide my company with good support in order to hold on to its customers who deal with my company.

Positive

I rate the product's initial setup a nine and a half to ten on a scale of one to ten, where one is difficult and ten is easy.

Most of the companies to whom my company provides the solution deploy it on an on-premises model. My company also sells the cloud version of the solution while also offering our customers the option to move from on-premises to the cloud version of the solution.

My wife runs a pharmacy, and she uses an ERP solution for sales-related activities, the data of which I can move to the cloud in ten minutes if she chooses Informatica Enterprise Data Catalog.

With Informatica Enterprise Data Catalog, measuring the return on investment can be hard. I rate the return on investment a seven on a scale of one to ten, where one is zero return on investment, and ten is a hundred percent return on investment.

The price of the product is something that may vary from customer to customer. I rate the product's pricing a five on a scale of one to ten, where one is cheap and ten is expensive.

If a customer or business wants to easily maintain their data domains, they can opt for Informatica Enterprise Data Catalog.

I rate the overall product an eight to nine out of ten.

We use it for various use cases, including data provisioning, data validation, continuous monitoring of data quality, and data standardization.

We have implemented both data profiling and data quality transformations for data validation. These are the two major components we rely on, with data profiling helping us understand the data's characteristics, and data quality transformations ensuring data validation.

Exploring the possibility of incorporating AI capabilities that can suggest additional rules would significantly streamline our data analysis process following data profiling. Many other tools offer this feature, enabling them to suggest rules, which can be generated with just one or two clicks.

I have been working with it for almost seven years.

Once it was implemented, we din not encounter any stability issues. I would rate it nine out of ten.

The scalability of the solution depends heavily on the design approach. Regardless of the tool used, the effectiveness of the data quality framework hinges on proper design. If the framework is not well-designed, it may not deliver the desired results. I would rate it eight out of ten.

We receive excellent support on a priority basis. I would rate it nine out of ten.

Positive

I would rate the installation and configuration process as moderately complex.

If it has been implemented effectively, I would say it is the best solution. When considering the use of the data governance package, combining Axon ADC and Informatica Data Quality together provides a comprehensive approach. I would rate it eight out of ten.