Informatica Intelligent Data Management Cloud (IDMC) Reviews

We were trying to publish a compendium of all our various assets using an Oracle-based data warehouse. We also had vendor-based solutions like Epic for electronic medical records on an SQL Server base. We had many research systems on various platforms because it's a cancer research institution. 

We wanted to pull together a comprehensive data catalog to show people all the different assets we had and where they could go to find things. If there were vendor-based definitions of things, we'd pull in the definitions and various attributes. It's a heterogeneous environment data-wise. We get a lot of value from a comprehensive enterprise catalog.

It's available on our intranet through a data governance portal. You could go there for a catalog, quality reports, etc. In a year, there may have been 500 unique visitors. It was my job to publicize it, but I didn't necessarily do a good job. I worked with research groups and clinicians to get their stuff on it, and they would tell other people about it. The nice thing about Informatica is that it was easy to link from our internal web portal to their catalog. I had excellent developers working for me. It didn't require much development, and it took less and less as time went on. 

I like EDC's self-service capabilities. You can put the catalog on the intranet inside the organization, so users can search for something. People in the research world have specialized systems, and you might find data from various places that sound similar. 

The purpose of the Enterprise Data Catalog was to build an internally searchable catalog of stuff. You may not have permission to see this data but at least you could find out where it was and contact the person who owns it. 

Interoperability is one area where EDC has room for improvement. It was challenging when the faculty took over the data world and had specific vendors they wanted to use, and some were not particularly open platforms. 

It was a challenge to promote data visibility while also jumping through hoops to take some of the proprietary things and make them part of the calling, especially when the vendor doesn't necessarily support interoperability. You need to export things from a vendor program and import them again. 

We used Informatica for a lot of the data governance stuff I worked with when I joined the company, including things like cataloging and profiling data. The organization has really changed a lot in the last year and went with Informatica EDC. They're moving from the on-prem version to the cloud.

Informatica is a strong company, and EDC is a solid platform. 

The scalability is excellent. Informatica really knows what it's doing. They're smart people and great engineers. 

Informatica's customer service and support were decent. They listen to their customers. If something is missing or wasn't working, they will put it on their roadmap. They were listening to customers for roadmap stuff. 

We wound up building a staging database to pull stuff out of the EDC, stage it, and then access that. It was open, so we would often have an intermediary layer at Oracle. If we wanted to show, for example, data quality over time, we would have a weekly quality process that would run. We staged that in an Oracle database, and the portal could access that.

We had to build Informatica pipelines and do some Azure work to feed data to them. With data warehousing, you typically have static data that is timestamped daily or weekly and sometimes monthly or annually.  We were trying to get real-time data, like blood pressure monitoring and stuff like that. That was a lot of work. I didn't do it. The integration team. 

When I joined the company, it was an Oracle-based solution. Then Epic took over, which was SQL Server-based. After that, they went to Azure. Now, all the analytics are supposed to be on this platform, and it's not particularly open. 

Setting up Informatica EDC is usually pretty straightforward. The challenge is working with vendors like Oracle and Microsoft, which have APIs for integrating things. You can query what they have. They want it to play well with other vendors because they know customers don't only have one brand of product. In some cases, you get vendors in there that are, "No, we're not going to do that because we're so special." It was a challenge. The platform of choice for data initiatives at the cancer research institution is Palantir. 

We didn't look for an alternative, but Azure has its own catalog.

I rate Informatica Enterprise Data Catalog nine out of 10. It's a great solution, and I was happy with it. It did everything I wanted, and Informatica dramatically increased the integration points over time. When we started, it was like Oracle and SQL Server, but now they're adding NoSQL databases and others.

Informatica has user groups and listens to user input. One of the people that worked for me hosts an annual Informatica user group in Houston. You can try to build your own Oracle-based or SQL-based solution. They listened to the market, and they're working on improving the interoperability. Maybe it works better with Palantir since I left, but they work across all platforms. They're making it Cloud-based. I didn't know anything about them before I got here. I was dropped into a department where Informatica had been for at least five or six years, and it was a good choice.

We essentially use Informatica Axon to set up a data governance framework for a corporation, usually a medium-sized or large organization.

The dictionary, the search, and the ratings are without a doubt the most beneficial components of this solution. 

Socializing data governance is one of the examples, but I'm not sure what the others were. For instance, if you locate a specific data attribute, you can grade its accuracy by assigning it a star rating between 0 and 5. 

When someone else conducts a search, they might discover whether employees at the company believe the data to be of high quality.

In terms of additional features, I believe it is quite rich. As I previously stated, it is a fairly mature product.

The main issue probably has nothing to do with end users, but installation can definitely be simplified. Also, I believe that this is something that they are already working on it, but it needs to be provided as a service. 

The Microsoft version, Purview, is now available as a cloud service, it must be provided as a service rather than just installation. You do not need to go through the installation, you literally create an account in five minutes. That is something they need to do. I believe that it is already something they are on working on, but I don't believe it has yet been released.

Over the past year, I have definitely used Informatica Axon.

I have worked with Informatica Axon for two years.

We are always working with the latest version.

Informatica Axon is a stable product.

It's a fairly mature product. For many years, it has been the market leader.

Informatica Axon is a scalable solution.

The smallest organization would have 500 users.

Any of the multinational corporations could be the largest. It's quite pricey. 

It is commonly used by large multinational corporations.

It has two or three user interfaces, one for everyone in the organization, one for specific data analysts, and one for IT technical staff.

Like many organizations, technical support responds quickly at times. It can take some time. It's about average, in my opinion. I wouldn't say it's particularly good or bad. It's average.

I would rate the technical support a three and a half out of five.

Neutral

I am just really familiar with Azure DevOps as a deployment technology.

Some of the Azure services, such as Database and Purview, and Informatica tools, such as Axon, and Data Governance, are among the things I am familiar with.

Informatica Professional Services should absolutely perform the installation due to its complexity. Additionally, training is required for end-client configuration. It is not something you can pick up and configure immediately.

Although there is no cloud version of it yet, it can be deployed in the cloud as a virtual machine, or VM.

Axon itself, you normally allow anywhere between five and ten days to conduct the installation configuration together, and that's not elapsed time. It normally takes a week to two weeks to install and configure an initial setup. This is elapsed time, not necessarily man hours. You will always need additional configuration to be done. Although the actual installation and configuration might be completed in a few weeks, getting it ready for use by the organization might take six months.

We do not have a technical team. I was the person who deployed this solution.

It was a challenge, but I did it for the POC.

Informatica Professional Services was consulted when it came time for the client's actual installation.

Axon can typically be present; the technical installation only requires one person, and a second data specialist is typically required for configuration. Normally two. Additional technical setup and data configuration are required.

It is definitely high if used properly, but many organizations spend all of the money and then fail to use it to its full potential. It is unquestionably a good investment if used properly.

It depends on how the client uses it.

Pricing is determined by the number of licensed users as well as the number of Core CPUs. 

You can imagine how expensive it would be for a company with 10,000 users, but it all depends on how many analyst user licenses they require. 

I believe there is a flat fee for the end user. 

It is not expensive, but individual licenses are required for technical users, data analysts, data stewards, and the data governance team, which can add up if you have a large number of them.

Different clients may have different licensing costs, but it is based on a yearly paid license. It is renewed every year. There is an initial licensing fee as well as an annual renewal fee.

They are very expensive, and they charge a lot because they know they are number one. I would rate them a five out of five. Five being the most expensive.

I would definitely recommend it. If they can afford it. I strongly advise them to use it. But, before diving into Axon or any of the other governance tools, make sure they understand their data landscape thoroughly because the tool will not help them with that. They will fail if they do not have good data practices in place.

I'm a freelancer, but the consultancy I went through is also independent, but they do have a partner relationship with Informatica, as they do with many vendors.

I would rate Informatica Axon a nine out of ten.

We use the solution to validate emails, telephone numbers, and postal codes. It can also create some data quality rules using the Cloud app's data quality function.

It offers enhanced usability with the ability to create rules quickly using its AI engine. This feature simplifies rule creation by allowing users to specify their needs, and the system generates the necessary scripts, acting as a significant accelerator. 

Another advantage is its seamless integration with other Informatica capabilities, making data quality a deeply embedded part of the solution. Informatica has a strong history in data quality with its on-premise product. All those powerful capabilities are now available in the cloud, making this product even more robust.

Accessing data as a service is essential, especially for validations requiring external data services. This goes beyond basic syntactic checks, like ensuring an email address contains the @ symbol or .com domain. Instead, it's about advanced validation, such as verifying if an email address exists or if a phone number is valid. 

These services are in high demand, especially for regions like China, Argentina, and other parts of Latin America. While such capabilities were initially more common in the United States, they are now increasingly required globally.

Five to six people are using this solution.

Informatica Support is one of the best services available in the market now.

Positive

Many clients acquired this product because of the ROI.

Informatica is one of the most expensive solutions available in the market.

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

We've 300-400 data quality rules and different kinds of rules for completeness, conformity, and timeliness. We're using Informatica to implement those rules in Cloud Data Quality.

Informatica has CDI. So, if you find some issues, you can always integrate your CDI and then fix your data.

The reusability factors are nice. The Cloud Data Quality is much better. A major flaw in the previous version was integration with the catalog, which is now seamless.

I have been using Informatica Cloud Data Quality as a partner for three months.

I rate the solution's stability a nine out of ten.

We want to keep costs down, so we are not getting enough resources.

It is suitable for enterprise level.

I rate the solution's scalability as nine out of ten.

Support response time could be better.

Positive

I use Collibra Data Quality. I switched to Informatica because Collibra cannot integrate. Colibra Cloud Data Quality is a very basic tool. There is no integration capability. If you have problem records, there is no fix to remediate them using the same tool. With Informatica, you can integrate with CDI and then create a remediation plan.

The initial setup was straightforward, and we were able to start configuring the rules quickly, etc.

It took about half a day to deploy since we are also learning.

There are different tiers based on consumption, but there is no flexible option. The catalog grows slowly over time. It would have been nice to use a flexible feature at the beginning and then transition to a model where you have a fixed set of units that you consume and pay for. If you exceed those units, you move to the next tier. We were expecting a flexible pricing structure, which we did not get.

We would have preferred a pay-as-you-go model. Although pay-as-you-go is slightly more expensive, we know our consumption will not be high initially. 

It is easy to integrate cloud data quality with IT workflow. We haven't faced many challenges. It is fairly straightforward.

I recommend the solution. 

Overall, I rate the solution a ten out of ten.

We use the solution for data cataloging and lineage.

The valuable feature is metadata management. If you want to trace sensitive data, you can auto-classify them. You can search for sensitive information through EDC. Using Discovery, you can identify if there is any type of data set.

EDC is only used for cataloging and meetings. Currently, not many different databases are integrated with it. The ETL logic should be visible within the tool. If EDC is integrated with Axon, you will see the data lineage and catalog because of EDC. If EDC is removed, you cannot see any lineage or catalog.

We need EDC on the application level. Currently, it is only at a system level. It can be shown as an application layer between policies or regulations. This kind of feature can be enabled.

The integration with other data management tools can be enhanced. For instance, there is no integration with tools like Collibra or Hubview.

I have been using Informatica Enterprise Data Catalog for seven years.

The solution is stable.

EDC needs many ports to run. Therefore, to improve performance, you need to increase the number of cores.

The initial setup is easy and takes two weeks to complete. One person is enough to deploy it.

EDC gives you a better understanding of architecture and the system used. You cannot use EDC completely unless you have access.

The product is expensive.

EDC is a standalone application, so it doesn’t really make much sense. It doesn’t give you a lot of features that way; it’s primarily for cataloging and classification. Nobody would buy EDC as a standalone application. When people are buying EDC, it’s always with Axon.

I recommend buying the solution altogether with Axon.

Overall, I rate the solution as seven out of ten.

Informatica MDM is used for our customers' master data management journey. The solution meets our client's requirements for their customer-supplier or any other entities.

The solution's most valuable features are its data quality, match-merge engine, and CLAIRE AI engine, which helps with AI automation.

Compared to other tools in the market, Informatica MDM is costly.

I have been using Informatica MDM for 13 years.

Informatica MDM is highly stable compared to any other tool in the market.

Informatica MDM is a highly scalable solution. Our clients for Informatica MDM are mostly medium and enterprise businesses.

I rate the solution eight and a half out of ten for scalability.

Technical support refuses to support us if we customize the tool. We customize the product based on the customer's needs, and we expect Informatica to provide support during that time as well.

Positive

On a scale from one to ten, where one is difficult and ten is easy, I rate the solution's initial setup an eight out of ten for on-premises and nine out of ten for the cloud version. Setting up the tool in the cloud via IDMC doesn't take much time.

The solution can be deployed within a few hours.

Our customers have seen a return on investment with the solution.

Informatica MDM is an expensive solution. On a scale from one to ten, where one is cheap and ten is expensive, I rate the solution's pricing nine and a half out of ten.

I would recommend the solution to other users because of its reliability and stability. Compared to other product vendors, Informatica MDM always has additional features. Informatica MDM has a CLAIRE AI engine, which helps with AI automation.

Overall, I rate the solution eight and a half 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.

You have to deal with a minimum of five, even if you want to go and check only a particular logic, making it an area with issues where improvements are required.

Certain shortcomings in the product's UI make it an area where improvements are required. The tool should provide ease of use and flexibility.

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

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

I see that my company faces frequent challenges in the tool when it comes to the area of UI and related to the area of characters. In my company, we have experienced some issues that occur when upgrading the tool.

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

Enterprise-sized customers can choose Informatica Cloud Data Integration.

The technical support for the solution is helpful, but it takes them time to figure out the issue. The time taken to close the ticket depends on the support that Informatica gets from their backend technical team. It takes a long time to close a ticket. I rate the technical support a seven out of ten.

Neutral

The product's initial setup phase was not difficult.

The product's deployment phase can be carried out by linking a .exe file provided by the solution with your server.

The solution is deployed on the cloud.

Against Informatica Cloud Data Integration, I recommend SnapLogic, which is a cheaply priced product. People know about SnapLogic, as it comes with very good features and ease of use.

The feature of the product that I find the most beneficial for data warehousing tasks is the use of the tool for extracting and loading purposes, which depends upon the connector's availability. Many connectors in Informatica are not free or out of the box, and you have to purchase them. The tool has quality connectors.

I haven't used the tool for real-time data analytics, but I have used it in my company for batch-oriented purposes. Data integration will not be supported in real-time in Informatica Cloud Data Integration. For real-time data integration, you have to go with either the application integration or other products like Informatica PowerExchange.

In Informatica Cloud Data Integration, the transformation feature was almost the same as that of Informatica PowerCenter. In Informatica Cloud Data Integration, the transformation features may offer a couple of more functionalities.

In terms of the ease of learning that the product provides, I would say that those who have experience with Informatica PowerCenter can easily adopt Informatica Cloud Data Integration. A person who has worked on Informatica Cloud Data Integration may not be comfortable with Informatica PowerCenter. In terms of ease of use, I consider Informatica PowerCenter to be easier than Informatica Cloud Data Integration.

I rate the ease of use experience attached to the tool a six out of ten.

Only after considering the use cases of people can I recommend the product to them. If a person only wants a product for extraction and loading purposes, then I feel that there are many cheap products in the market that can satisfy their needs. Suppose someone is looking at a tool that offers widespread features, like application integration and real-time functionalities. In that case, all of it comes under Informatica, which can be a good option.

The tool is the most expensive one compared to any of the other products in the market, especially in terms of ETL functionalities or integration features.

My company has integrated SAP, many databases, and APIs into Informatica Cloud Data Integration.

I can see that the performance of the product keeps improving with every new release, especially in the area of job executions, even though the hardware may be the same.

I rate the tool a seven out of ten.

We use the tool to collect, integrate, and process the data from different sources. Later, we direct the processed data to the required solution for analysis purposes.

It is a cloud service integration platform providing flexible and scalable data management architecture. It enables seamless integration with large data ecosystems, including Hadoop and NoSQL databases. It supports real-time data streaming platforms as well.

They could provide more robust performance for data integration processes. It would help in improving the data quality more efficiently.

We have been using Informatica Data Integration Hub for the past six months.

I rate the platform’s stability a seven out of ten. The complexity depends on the types of data. It has a robust software architecture.

The product offers two types of scaling mechanisms. We can add more nodes, integrate the service grid, and distribute workloads across multiple processing units. Also, we can add more resources, including CPU memory. It enhances the capability to handle large data volumes.

We can even utilize the load-balancing feature while handling high volumes of data. I rate the scalability an eight out of ten for efficient adaptability, pricing, and performance.

The technical support services are good.

The complexity of the initial setup depends on several factors, including types and volumes of data. It also depends on the data integration and configuration.

Working with a healthcare client with diverse locations, we faced the challenge of analyzing data in isolation, making it difficult to generalize findings across different areas. With the help of Informatica Data Integration Hub, we can collect data from various locations and enhance business insights.

We are familiar with centralized publishing and subscription models. It ensures the enforcement of data governance policies across all data sources and consumers, promoting data consistency and quality.

The automated artificial data synchronization facilitated by the data integration hub has proven invaluable in supporting our daily operations. It ensures the continuous updating of data, preventing delays and inaccuracies that may arise from using outdated information in analyses. This capability became particularly crucial during the sudden changes brought about by the COVID-19 pandemic, allowing us to adapt swiftly to the evolving business landscape. However, they could provide us with real-time data to increase the efficiency of manufacturing processes.

I advise others to understand the different data resources available in the organization before making the purchase decision. I rate it 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.