Informatica Intelligent Data Management Cloud (IDMC) Reviews

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 use Informatica MDM for various clients for integration, removing duplicates, and centralizing data in one place.

It has improved our organization because it has made our data more reliable. Data is the most important asset these days, and in order to trust your data, you need these tools to make sure that your data is clean and reliable.

The match and merge function is good.

New machine learning could be added to Informatica MDM because the solution is outdated and is not moving with the current trends. The solution is good, but it definitely needs a lot of improvement and needs to speed up as per the market.

Artificial intelligence is something which they should add to the solution.

They need to do MDM on real-time data as well.

The price is a big factor. We try to convince our clients to go for Informatica, but the cost is too high, and they choose another product.

I've been using this solution for 15 years.

It's a reliable and perfectly stable solution.

It is scalable but to a certain limit.

I'm satisfied with the technical support.

Previously, I used IBM MDM, Orchestra Networks MDM, SAP MDM, and Oracle MDM. I have used a lot of MDM tools, and I've found that Informatica is the best solution out of all of them in terms of the architecture and user-friendliness. Also, the graphical user interface is very simple to use.

The initial setup is not very straightforward but not very complex either. It's somewhere in the middle.

Deployment can take six to seven months to a year or two years. It depends on the number of domains and the number of data sets you need to implement.

A team of eight to nine people can usually handle the deployment.

We mostly deploy on-premises and sometimes on the cloud. Informatica is very weak on cloud-based computing though.

We implement the solution for our clients.

Based on my clients' feedback, they have seen a return on investment in the long-term. After five or six years, they can definitely see that the value of their data is growing.

Informatica MDM is very expensive. Apart from licensing fees, they have broken down their products into multiple products, and they charge for each and every product. If the data is huge, they charge for the data. At times, we have to use third party services for data cleaning, and they charge for that as well.

If you are choosing Informatica, then you need to employ the right kind of service provider. If you don't have good implementers, you'll not be able to leverage the product. So, choose your consultant and service provider wisely; then, you will not have any issues when implementing Informatica.

I would rate Informatic MDM at eight on a scale from one to ten.

I use the solution in my company and have worked with two tools from Informatica, both on on-premises and cloud models. I have experience with Informatica Axon, and I have used the cloud services under Informatica CDGC.

The use case of the tool is basically in the banking sector. It is used to optimize certain areas of a project. In Europe, we have a GDPR implementation. In the Middle East, there is the Central Bank of the UAE and UAE data protection law. Implementing data protection laws and ensuring that all the policies are enforced for financial security is my use case. I have to make sure the bank or financial institution meets the requirements of the Central Bank of the UAE. Data quality is another use case where my company handles corporate and retail customer data, and protecting their information is known as PII. The tool ensures that the system holds the PII data properly.

For data lineage, we look for things at a transformation level and how the data is transforming and managing the ETL mapping. Every tool gives this information about how the data goes, but our company is interested in knowing how the data is transforming and the transformation logic between the two data sources.

There are some tool limitations. Some connectors do not scan the metadata properly. If I want to scan the metadata from the data lineage or the Python code, such areas can get tedious in the tool. To manage such areas, one needs to have a lot of coding knowledge, and you need to know how to apply the code, and only then can you get it fixed. There is no user-friendly enterprise.

There are certain shortcomings even if we opt for subscription-based technical support.

I have been using Informatica Axon for four years. I work as a consultant.

It is a stable solution. Once it is implemented, we will handle everything for the business. Stability-wise, I rate the solution an eight out of ten.

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

The tool is meant for enterprise-sized businesses.

I rate the technical support a six out of ten.

Neutral

I have experience with Informatica Data Governance.

If one is difficult and ten is easy, I rate the product's initial setup phase an eight out of ten.

The solution is deployed on the cloud, specifically if you have cloud-based virtual machines. If there are compliance issues, and you need to hold the metadata only on-premises models, you can do that, too.

The solution can be deployed in a few days.

It is an expensive solution. I would say it is the most expensive solution in the market.

For the features of the software, the price is justifiable.

Speaking about how the tool enhances data governance tasks, I would say that we have three types of users, consisting of the consumer, who are normally a business user, then we have a developer and finally the admin. Our company onboards the user, and then the user can collaborate, comment, ask questions, give ratings to their assets, publish the data to the marketplace and go for downloads.

One needs to resolve the problem that stems from the fact that different people can have different understandings of the concepts associated with the tool. A database administrator may see an area as a table, but for a business, it may be a person. The tool gives you a common understanding of the business concept. The tool also provides lineage data and how the business data is performing. It provides an interface between organizational policies, the different processes governed by a certain policy, and the different systems governed by which policy. The tool gives you a 360-degree holistic view of the data.

The cloud version of Informatica Axon utilized the AI for the classification purposes, data domain and automatic data tagging. In my company, we use the tool's AI capabilities.

I will recommend the tool to others.

I rate the tool an eight out of ten.

They could improve the product support for the Arabic language. Currently, there are limitations in processing and the interface itself regarding Arabic language support.

We have been using Informatica Enterprise Data Catalog for two years.

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

Our organization has 12 Informatica Enterprise Data Catalog users. I rate the scalability an eight out of ten.

We encountered a delayed response from the technical support team.

Neutral

We also use the Erwin Data Catalog, depending on the customer's requirements.

The initial setup process is easy. It takes around two days to complete.

We get help from third-party vendors for product implementation.

The ROI from using Informatica EGC (Enterprise Data Catalog) can be substantial, as it helps everyone, from novices to experts and stakeholders, maximize their data's potential. It enables thorough understanding, insightful analysis, and seamless sharing of data assets. Features like adding business terms enhance data quality and governance, benefiting businesses across various sectors. Many customers have truly benefited from these options.

For 12 users, the platform's estimated annual cost is around $160,000.

Our data management processes primarily use Informatica to connect to data sources, scan metadata to obtain physical data and perform data classification. Subsequently, we create business terms to establish a business glossary repository within the platform.

The features that have been most effective in improving our data governance include auto-classification and the ease of exploration and navigation within the tool. It can automatically connect or associate business terms with various options, providing flexibility beyond general capabilities.

The AI-driven discovery capability has significantly enhanced our data cataloging processes. Utilizing AI for auto-classification and discovery has been particularly beneficial. However, there is room for improvement in certain areas for understanding the data.

It has adapted well to changes in our data landscape, seamlessly accommodating new types of data sources. It offers flexibility in adding new data sources and supporting multiple data connectors.

Looking ahead to the future of Informatica Data Catalog, data lineage will be a significant trend influencing the product. These features are currently available, but there is potential for further enhancement and detail. It will enable organizations to comprehensively understand their data and its relationships with business processes and regulatory requirements.

I highly recommend it to others and rate it a nine out of ten.

It's about mastering various domains. For example, an organization might want to master customer data residing within their IT landscape. This includes all master attributes related to customers, like name, email ID, phone number, address, and potential information like stores they visit. Ideally, if the organization also stores data related to those stores, all that information, files, and everything would be included. So, these master attributes would lie within the MDM system.

Here's where things get interesting. I'd bring this data from disparate sources. For example, accounting systems hold phone numbers, email addresses, and maybe even bank information (just an example). 

But another system, like the ER system, might have different information, like the customer's spouse. So, I'd likely get streams from both these systems, but there might be an additional attribute in the ERP system, like the spouse's name.

Now, looking at all these different attributes lying in different systems, one system might have my name as "Raja S.," while another might have it as "Raja Soni." So, the question becomes: are Raja S. and Raja Soni the same person? How do you identify them from these records?

They try to match these records based on phone numbers or any other common attribute between the two systems. Then, I would have a complete record in MDM, which is called the "Golden Record" - the single version of truth within the system.

Furthermore, MDM can also contain additional attributes that the ERP or accounting systems didn't have. So, imagine ten different applications or data sources feeding customer information. I can gather all of that information and create a single version of the truth for a particular customer within the MDM system.

Additionally, if I want my customers to update any information, I can provide a form where they can enrich their data. For example, they could potentially enter their Social Security number (although this wouldn't be common practice). This is just an example of how someone might want to collect this information. So, I can create a form and say, "Okay, can you directly feed this into the MDM system?" This becomes an enrichment opportunity as well.

With Informatica MDM, before you consume the data, you need to check what kind of data it is. There could be some systems where the first name is missing, or the last name is listed as "missing." 

This is where, before consuming or ingesting data into any MDM system, I can filter it out or rather do profiling using Informatica's data quality tool, which is a different tool called IDQ. I can profile the data, okay, and see the current state of the data from our system, any other database, or any source system. 

This is where I'll identify issues like missing phone numbers, incorrect cities, and other things.

So, when I see that the data is incorrect or incomplete (not just incorrect, but also incomplete), I can go back to the source system based on these profiling reports. I would say, "Source XYZ, you have missing data. Can you please ensure that you send it?" So, when I ingest data into the MDM system, I will only ingest it with certain rules. 

These rules define that only records with a first name, last name, city, phone number, and email address should get into MDM. If any of this is missing, I'm not going to let it get into the system.

I would ask the business to review those and then probably ask the system to correct them and then feed them again until the defined criteria are achieved. That's how I improve data quality using Informatica MDM.

Plus, I can also decide that once the data is in MDM, it can feed back to these source systems from where it was originally consuming the data. So, whatever is corrected gets looped back to these sources if they allow me to publish it to them. 

Then, this automatically becomes a cycle: data comes in, gets a data quality check, then enters MDM, and then can be fed back to the source systems if they accept it.

First and foremost, data profiling is a very important aspect, because it allows me to understand the state of the data from the different source systems and applications I'm going to pull into MDM. This is crucial, as it helps me understand the existing data landscape.

Next, Informatica MDM allows you to perform fuzzy matching. Like the example of Raja S and Raja Soni mentioned above. With fuzzy matching, I can define a threshold, and the system will let me know whether these two records are the same or not. 

This is where I think MDM shines - with its strong fuzzy matching algorithm. This is the essence of Informatica MDM. Based on these results, I can write our match conditions and then perform the corresponding data management activities.

When it comes to how data is governed - how accurate it is, what's missing, and what's not.

There are other Informatica tools involved: Informatica Enterprise Data Catalog (EDC) and Informatica Axon, which is specifically a governance tool. With these tools, users can understand data quality. 

Data quality, Informatica EDC, and Axon work together - they "talk" to each other. The moment Axon identifies that data is bad, incomplete, incorrect, or whatever, the governance tool allows you to monitor all of these things and feed them back into the organization.

For example, if someone within the organization is using the governance tool, which is connected to data quality, they might realize: "This particular source isn't giving me certain information, like city data. But I need city information for my XYZ data usage. So, I should focus on another data source or application where customer cities are available." Based on this, data governance can be implemented, and workflows can be established.

MDM also provides workflows. When records come in or if a customer wants to update a record directly in MDM, different personas can be defined. 

A workflow would then be triggered, and someone could review the data being fed through these forms or sources. They might reject or accept it based on predefined criteria. This is how data entering MDM is governed, ensuring everything is correct and meets organizational standards.

I feel the out-of-the-box APIs or the API management could be improved slightly from their current state. It could be more user-friendly.

In future releases, I would love to have more reports and dashboards available within Informatica MDM specifically for master data reporting. 

Currently, there aren't many reporting functionalities offered. While it's true that reporting isn't a core feature of any MDM system, they do have basic dashboard reports. I'd like to see them made more customizable and offer more options for creating reports.

I have been working with it for more than ten years. 

Stability-wise, it's also pretty decent and stable. So, I would rate the stability an eight out of ten. 

I would rate the scalability an eight out of ten. It's pretty good because mastering data across various domains like suppliers is crucial. Informatica is probably the best when it comes to multi-domain solutions.

There are around 500 end users. 

The quality of resources is good, and the time is acceptable. However, the initial quality of resources assigned to support tickets could be better.

Neutral

I'd rate Informatica higher. The biggest reason is the multi-domain capability. Not all vendors offer that flexibility. 

Additionally, Informatica is actively working on a clear, built-in AI engine for its tools. The SaaS version functions well, and they've introduced CLAIRE AI engine for on-premise versions. This engine learns from usage within your organization, further enhancing the experience.

The complexity of the setup depends. For example, it is different for the on-premise version. Now, Informatica has mostly shifted towards a SaaS model, which is essentially plug-and-play. You just need to log in and start using it, making it very simple. 

However, for the on-premise version, the setup process requires some expertise. It's not something just anyone can do. You need experts in place to handle the setup effectively.

Within the organization, businesses have benefited significantly. Tasks that used to take four to six weeks now only take three to four days.

Pricing can vary because Informatica doesn't have a standard price across the board. It depends more on the sales representatives in different regions. They essentially call the shots and decide the discount percentage for each customer.

So, it's quite flexible, like a bargain, in a way, but it depends on the salesperson.

Overall, I would rate the solution an eight out of ten. 

I absolutely recommend it. If you're looking to implement a Master Data Management (MDM) solution, Informatica MDM is the most flexible tool available in the market. Compared to its competitors, it offers several advantages.

Firstly, it provides the option to move seamlessly between on-premise and SaaS versions. If you start with the on-premise version, you can easily transition to the SaaS model later, if needed. 

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

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

I have been using the solution for eight years.

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

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

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

Positive

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

Our vendor manages the solution's implementation.

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

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

We use the solution for data modularization.

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

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

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

We have been using the solution for two years.

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

I rate the solution a seven out of ten.

The solution is mostly used to automate MDM, send keyword dimension sites to the engineering team, or address historical records for the training-based employee.

The valuable feature is that we can automate the integration part. Also, it's suitable for data quality. The solution also helps create the data that has been helpful in data governance. We performed some effective or administrative charges in real-time and, most recently, in a real-time solution. So, it allows enrollment to capture and transform the data anytime.

Most of the demand is about how MDM can be automated so the changes occur automatically.

The whole server will change the storage engine, like any tool key, so everything can be automated.

Informatica MDM has limitations with connectivity. This is evident when we connect with our console, especially sometimes the process gets filled. With the tools we are locally running from our end, there should be some specific instance to register that is helpful and can support Informatica in running its modeling.

More automation is much required nowadays, as mainly the work will convert into a complete normal manual process.

There should be some specific integration tools, the managed services, involving the application, and then the code they use. That should be sufficient, meaning there should be leverage to openly adopt any new integration environment like PySpark that we will use. Some resiliency with the particular application, the code, and the type of files only can we address.

I have been using Informatica MDM for more than ten years.

It is a stable solution.

It is a scalable solution for sending any new setup or data. Approximately six hundred users are using the solution.

The technical support team is good. The administration has access to both servers. They can only provide access to run the tool, allow authentication, and how data can be secured. I rate the service and support as ten out of ten.

Positive

Previously, I have used Microsoft SSIS and AWS Glue, but Informatica is capable of doing multiple things for integration purposes, but also for data evaluation.

The initial setup is straightforward. The deployment took ten to fifteen minutes, and almost fifteen administrators were needed for the deployment.

I would say that based on the current scenario, the market trends show that it is not required for the price to be increased. But, functionality-wise, and considering that now as the trend of microservices is going on in the market if Informatica MDM can be replaced in a similar way with particular tools or components to meet the client's demands, we can use only those particular things for the DDoS service covering, for the backup, for the security, and data encryption. So that can be helpful. Part-wise, if somebody needs only the pipelines to be created, someone needs to get a complete data flow task, or someone requires only the MDMs to get in place and not complete information that they need to make. We have licenses, and we are provided with certain particular services in the solution.

I urge those planning to use the solution to understand how they are modeling data encryption and data governance activities. Suppose you are implementing without knowing your data quality and data governance. In that case, the installation of those sources through MDM is not affected. So better to better understand the basics.

Then it will be helpful to use something specific.

I rate the overall solution a seven out of ten.