Informatica Intelligent Data Management Cloud (IDMC) Reviews

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 It ensures the continuous updating of data, preventing delays and inaccuracies that may arise from using outdated information in analyses.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.

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.

We primarily use the solution for data integration as well as application integration.

Currently, we have a multiple-source system. With Oracle ERP, it's very hard to pull data from Oracle ERP, since it's API based. Therefore we have to make use of Informatica Application Integration to pull data from Oracle ERP.

We are pulling our supplier information, customer data, and data for projects related to information.

It's a beautiful tool. It eliminates the use of a third party or some other tool as it provides beautiful functionality.

In an earlier version, when we are working with an on-premise tool, it was hard since we had to concentrate on the configuration, et cetera. However, now, when we are working with the cloud, most of the time it's very easy to handle.

Data integration happens seamlessly and is pretty fast.

The application integration will give you more flexibility when dealing with APIs.

Every week, we seem to get notifications of new features and updates. They are always trying to improve the product. 

When we're dealing with huge amounts of data, we need to reach out to Informatica. There are some kinds of API records that we need to modify configurations for. 

Application integration, as compared to data, is a bit slow, and it can't handle millions of records.

There could be a lot more application integration. Like data integration, we find there are a lot of places where we can go ahead and improve performance. When it comes to application integration as a developer, we don't have that much flexibility. We have constraints. Our hands are tied, and we can't do anything. We need to speak with either the target system or the core system and say, "Okay, do this thing." 

I've been working with the solution for more than two years. 

It is a stable, reliable product. Sometimes there is a bug or a cache issue. We might get an error when connecting and when that happens, we reach out to support. We find it's a pretty common issue. It might be a glitch. 

It is very scalable. We have some challenges around application integration, and that requires speaking with support. We tell them we need to change a configuration to a specific size. They can assist us when that happens. That's only for application integration, not data integration. With the cloud, we can easily handle millions of records of data.

A variety of people use the solution in our company. There are 50 to 500 users, approximately. 

Technical support seems to take their time in responding. We'd like a faster reaction time. Sometimes it takes one to two days to get a response.

Positive

We did not previously use a different solution. Currently, we're working with data and application integration with Informatica only.

We've mostly dealt with on-premises deployments. As a developer, most of the time, we face a lot of challenges, which are overcome when it comes to Informatica Cloud. Informatica Cloud is much more convenient, as well as fast, and gives good performance as well. With the cloud, we don't have to install software and there aren't any dependencies. It's much more convenient and easy with Informatica Cloud.

The deployment hardly takes more than a few minutes. 

When it comes to the cloud, there is no maintenance necessary. Informatica takes care of any support tasks. They do the updating, et cetera, as well. 

We handled the initial setup in-house. There were no third parties involved in the deployment process. 

We have not looked at ROI.

The solution is subscription-based. Users are charged based on how many resources are used.

The pricing is pretty good. I'd rate it 8.5 to nine out of ten. 

We did not evaluate any other solutions previously. Our client already had this as a pre-existing tool and did not want to switch. 

I'm working with the latest version of the solution. 

Informatica is a stable, easy tool. New users should go and explore it. There is improvement required in Informatica Application Integration; not the cloud.

I'd rate the solution eight out of ten. We're quite happy with the product. 

We don't use profiling as much, however, we do use it, in certain cases, for profiling. We use the Analyst tool to do out-of-box, high-level profiling of data to see high-level quality of completeness, and uniqueness, et cetera. Mainly, we use the Developer tool to connect to the sources and to write data quality rules.

It has improved our organization. 

We started from just pretty much having flat files, and then doing some basic transformations, then writing back to Excel or QFD files. 

We gradually moved to more analytical tasks. You don't just do statistical data quality you also do analytical. You do lots of joins with other sources and do the consistency checks, and to do more complex logic, and build metrics. 

We use Tableau on the back of it to present the data and data quality, and then monitor it. We use it more like a batch process to build pipelines, and then, using Tableau, monitor the results of it and those metrics. Now, we work more with live updates and do that more than the batch.

It's probably one of the leading lights in ETL. They have really good built-in functionalities, or algorithms, that you can use to transform or process data and validate and standardize.

The solution is stable.

It's not too had to set up the cloud version. 

Support is helpful and responsive. 

We are in this transition mode, where we haven't yet got IDMC, the cloud version, so we don't actually have hands-on experience and have not actually seen the features. All we rely on, at the moment, is just the available documentation. What I don't like on the IDQ side is just the fact that in the on-premises version, you have all these applications, with separate configurations. In the cloud solution, it is fixed so that you have everything on one platform.

The performance isn't as good on-premises. For example, when you install clients, it's slow compared to the cloud. Still, we need to see. We haven't experienced it ourselves. 

The upgrades are a downside. On-premises you manage all the changes in the software. You have to do that yourself, and if there's some problem with compatibility, it makes things that much harder. With the cloud, everything is managed by Informatica on the servers.

Managing the licenses with the on-premises version was difficult. However, with the cloud, it will be much simpler. 

I've been using the solution for the last six or seven years. 

Once you set everything up, it is pretty stable. It's reliable. There are no bugs or glitches and it doesn't crash or freeze. It is way more stable than Hadoop and other applications. 

In terms of scaling, we used the clusters, and the processing was on Hadoop side. If we needed any extra space or any service, it was just managed there, so it was outside of Informatica.

Originally, we had 20 people using the solution, and then it was reduced to less than ten.

We do use it as much as we can for its purposes. In the past, we used that for the whole ETL process with data loads, and then we moved to Hadoop storage. At the moment, we are only going to be using Cloud Data Quality and others for cleansing, standardization, and deduplication, and then using some other Azure capabilities.

I've dealt with support in the past. There were issues, and we had to deal directly with Informatica for some hotfixes. They were good. They just got straight to the point and were helpful overall.

Positive

It is way more complex to install on-premises than in the cloud.

With the cloud, the installation will be way easier since you only install these secure agents. They have many different connectors, so it is definitely less hustle to install all these machines, and all these applications. On-premises, it was more user-based. Now, it's service-based, and you just pay for what you use and the licenses as well. 

We had myself, an architect, and a developer as well as help from Informatica while handling the setup.

We have about two or three people that can deploy and maintain the solution. They also cover other applications, not just Informatica.

We had Informatica support, and we had an internal group of people with Informatica knowledge who handled the solution. For some parts, we were involved as well, and we handled them ourselves. 

We're still in the early stages of moving toward the cloud. We have not seen an ROI yet.

When you are using the on-premises version, managing the licenses is quite difficult. However, on the cloud, you just pay for what you use, and it's a lot easier. With the cloud, if you want MDM, you pay for it, and if you want PowerCenter, you pay for it; however, if you don't want it or don't use it, you don't pay. We'll just pay for Data Quality, as it has all of the features we need inside it. 

I'm not involved in the conversations around licensing and agreements. That said, my understanding is that Informatica is pretty expensive. I'd likely rate it two to two and a half out of five in terms of affordability.

We definitely considered others and had StreamSets used for some other purposes. The company that I moved out of was going to be switching off Informatica at some point due to licensing, et cetera, and they just chose to go to StreamSets with Snowflake for storage. 

I haven't researched enough about other products in relation to Informatica.

We are moving to the cloud version. On-premises, we were on version 10.4.2, and that moved to 10.5. Soon, we will be on the cloud.

We're using IDMC, which is not just Data Quality. It has governance, Axon, and other applications in it.

We're just a customer.

I'd advise people to research use cases before beginning. Companies need to understand what they are trying to achieve, figure out their requirements, and then appraise the solution. 

While Informatica is good in terms of Data Quality and is probably the leading option, you need to be clear about budget, et cetera.

I would rate the solution seven out of ten.

We are currently working on a project with the Oracle Fusion system, so we have multiple use cases and often need to pool data in different source systems. In the project, we have multiple sources and target systems. Based on the requirements, clients, and what they propose, we need to make decisions or check the technical feasibility of whether it is possible to complete the integration.

Product-wise, compared to other on-premise tools, Informatica Cloud Data Integration can be deployed on-premises and cloud. So when we were working on-premises, there were a lot of challenges with the hardware. For example, there were memory space issues, but once we switched to cloud, there were no more issues. 

There are challenges when we use Cloud Application Integration (CAI). We want add-ons when we complete data integration and merge images. The current features are a bit complicated, and we need to write big scripts and test.

We have been using this solution for more than six years. We are using a cloud version and the most recent version, 10.4. We have also used versions 8.6, 9 and 10, and we have observed that there isn't that much of a difference between versions, just additional features added. We do not have private or public cloud for Informatica Cloud Data Integration, just cloud.

The solution is stable. 

The solution is scalable. We have more than 20 people using this solution.

We hardly raise an SR for this solution. But, whenever we require technical support, we do not get a satisfactory response. This is because their knowledge base may not be developed.

We used this same tool but on-premises in the past. We have also worked with AWS Snowflake, an ELT tool similar to Informatica Cloud Data Integration but with a different architectural approach. If we had to rank them, Informatica Cloud Data Integration would be the best, followed by Snowflake, then AWS.

Informatica Cloud Data Integration has some advantages over the others. It has a more UI-based tool, and the scripting is good. AWS also has an ETL tool called Glue, but it does not have everything we need to write in a script. We still need to write a script if we want to perform a small task. AWS is also challenging to teach. Informatica Cloud Data Integration and Snowflake are easier to use.

The initial setup is easier for cloud compared to on-premises. Deployment does not take much time and takes about 20 to 30 minutes.

The pricing of Informatica Cloud Data Integration and other solutions are almost the same but with slight differences. It differs from project to project, their use case, source and target. Unfortunately, I do not have information on precise licensing costs.

I rate this solution a nine out of ten. Regarding advice, it is a great solution, but users should understand their source and target system. For example, we also deal with Oracle Fusion, and it's a bit challenging to push data because Oracle has multiple models, financial supply chain and HR. Hence, it is difficult for a developer. Therefore, it is difficult to push data.

They should decide whether they are comfortable using Informatica Cloud Data Integration as their middleware tool or whether they want to use Oracle or OCI. OCI is a different middleware tool owned by Oracle.

Regarding additional features in the next release, Informatica Cloud Data Integration doesn't have image merging transformation or capability. We can do it without the solution, but it is a lengthy process. So, it would be helpful if they added the transformation to play with images and deal with invoices and financial data. We sometimes need to add 200 out of 800 invoices, and it's a bit challenging without the additional features.

We are customers of Informatica PIM and I'm a senior architect. 

Informatica comes with a predefined data model which is the way in which you enrich your products. It acts not only as master data but also as a product enrichment tool. The product provides flexibility; you can go for an undefined number of attributes that define a product. You can plug it in with any e-commerce website. That's its value to us and why we need it. 

Database access is highly restricted with PIM. If you're using the cloud version, you probably won't have database access or a view of it. If you're wanting a customized application, you can develop the out-of-the-box PIM solution with a little bit of customization here and there without even needing a database. That's a very big drawback. As a developer or as an architect, you need to have a data model. I know some people use it without database access and the issue is that they don't know what that data model is built on other than just seeing it through UI tabs. The one thing I'd really like to see is for the solution to be made scalable. 

The solution is stable, in terms of bugs they're very good. They keep evolving their toolset. I have encountered many bugs in Multidomain MDM, but to date not in P360.

Scalability depends on your use case. If you're using it as an integrator, then it is definitely scalable. That said, it would require an altogether separate MDM solution. If you don't have MDM, then there are likely to be some limitations. We're in the process of installing now but in my previous company, we created the core set of attributes through MDM. From there it went worldwide through an e-commerce website. We have 50 to 60 people internally using it as a tool. If you're exposing your data through an e-commerce website, then it's accessible to all, although just for reading and not for updates.

If you're working with Informatica, you need to interact with them on the out-of-the-box features. They are very efficient and quick in their response times. 

I've trained in Stibo which is a little bit better than PIM and I've used Oracle PDH and CDH. Those were basically a package MDM solution. If you're using SAP, I would suggest Oracle products. But there's been a very big evolution over the past decade when it comes to enterprise MDM tools. Most people working in companies are disbursed in different locations and using multiple toolsets, they can't be restricted to one technology. That's the reason that Informatica, Stibo, Riversand, and other technologies are very useful and very powerful. When comparing PDH to PIM or CDS to Multidomain, managing the Informatica toolset is easier in terms of effort required in comparison to managing Oracle MDM or Oracle CDH or PDH because they have their own set of limitations. The good thing is that they can be very easily coupled with an ERP solution. Here, you have to build real-time or multiple interfaces. 

The setup is relatively easy I would say. But if you need additional attributes, it gives you a repository manager feature where you can use XML, add your own attributes and then configure it. To my mind, the setup is simple to moderately complex.

Licensing costs are a little pricey. For my previous client, as a support contract partner, we were making less money than Informatica was charging them. 

Informatica 360 PIM was bought from Heiler and is a very good tool if you already have MDM in place and you simply want to use it as an enrichment tool. With Informatica, you perhaps need to go somewhere into the roots of Multidomain. P360, Multidomain, and C360 are all different things. Managing a thousand plus attributes into that data model is going to be very complex. If they could come up with a standard data model that Oracle offered customers like PCH or PDH, similar to what P360 has, that would be my preference. Also, my recommendation is to always utilize your team as an integrator rather than being a master data manager.

I rate the solution eight out of 10. 

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.

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.