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 It ensures the continuous updating of data, preventing delays and inaccuracies that may arise from using outdated information in analyses.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

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. 

We use Informatica Cloud Data Integration to migrate to the cloud, upgrade our data warehouse, and implement master data management. It helps us with in-depth analysis, leveraging shared history, and addressing data governance questions.

The most valuable features of Informatica Cloud Data Integration for our clients are the AI capabilities within Informatica Intelligent Cloud Services. It excels in mapping development, searching for contracts in data governance, creating data quality rules, and designing merging processes. The ability to identify duplicates and automate analysis coverage is particularly beneficial. Additionally, the cost-effective option of using Informatica Cloud Data Integration for smaller projects or departments has made it more accessible and attractive for experimentation.

One area where Informatica Cloud Data Integration could improve is in providing more accelerators for certain functionalities. For example, for master data management, having additional accelerators could streamline implementation. Additionally, enhancing compatibility with metadata sharing across different applications, similar to what is achievable with PowerCenter, would further enhance the product's capabilities.

I would like to see more connectors for on-premise databases in the next version of Informatica Cloud Data Integration. Currently, CDI free is a great option for cloud integration, but expanding the connectors to include databases like Oracle or MySQL for on-premise environments would make the tool more versatile and accessible for smaller businesses or those not fully in the cloud.

I have been working with Informatica Cloud Data Integration since 2016.

Informatica Cloud Data Integration has been generally stable, with occasional editor issues. However, SLAs are consistently respected, and we typically experience minimal downtime, averaging around two to three hours per month.

The scalability of Informatica Cloud Data Integration is high. With advanced pushdown optimization, it performs well in various environments like Snowflake and AWS. It is suitable for companies of all sizes; for small to medium enterprises, starting with fewer IPUs is cost-effective, and scalability allows for growth as needed.

I would rate Informatica's vendor support as a seven out of ten. It is generally good, but there is room for improvement, especially as they shift focus from on-premise to the cloud. We often find answers in the community for software issues, but sometimes we encounter challenges that require official support tickets.

Neutral

The initial setup for Informatica Data Integration is straightforward. Deployment is simple, involving exporting and importing development processes. The ability to pilot the Informatica Data Integration platform from various APIs makes continuous integration and deployment easy to manage through API calls.

I would rate Informatica's pricing as an eight out of ten in terms of affordability. Starting with a small IPU volume is cost-effective, and as we grow, the pricing adjusts favorably based on consumption, which makes it reasonable for our needs.

I would rate Informatica Cloud Data Integration overall as an eight out of ten. It is a comprehensive platform covering data integration, API integration, data quality, data governance, master data, and privacy tools—all within one platform. The accessibility to all functionalities with the purchase of IPUs is a significant advantage compared to the past where certain features required additional payments and permissions.

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

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

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

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

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

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

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

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

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

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

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

Positive

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

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

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

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

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

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

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

The most valuable features of Informatica MDM are the IDQ and RDM data management. PowerCenter is the most widely used feature of Informatica MDM.

Informatica MDM has a complex user interface, which could be improved.

I have been using Informatica MDM for four years.

I rate Informatica MDM an eight out of ten for stability.

I rate Informatica MDM a ten out of ten for scalability. We plan to increase the usage of the solution in the future.

I rate Informatica MDM a four or five out of ten for ease of initial setup.

Our organization's deployment process for Informatica MDM took two to three months. A third-party team did the solution's deployment. Around 10 to 12 IT specialists and engineers were required for the solution's deployment.

Informatica MDM's pricing is not cheap but comparable to other vendors.

I advise others looking into using Informatica MDM in the future to have their use cases in order and know what they want to use it for.

Overall, I rate Informatica MDM a six out of ten.

I am a consultant and I use the solution to scan databases and lineages for my clients.

We can scan anything. We can scan reports, XML and JSON files also. That's a good thing about the Informatica Enterprise Data Catalog.

IEDC can improve the comparison of lineages, which is one thing I see in other data catalog tools that Informatica does not have, basically data catalog tools.

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

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

I rate the scalability an eight out of ten. We currently have ten to 15 users using the solution and our client is a medium-sized business.

I rate the initial setup a seven out of ten, because it is pretty straightforward. The solution took two to three days for the initial setup.

When deploying IEDC, one should have a server ready and a lot of open ports, but otherwise it is straightforward.

I rate the solution an eight out of ten.

We use Informatica MDM for master data maintenance across our organization. We have multiple departments, like material real estate, trading, and supply. We use it for consolidation, harmonization, and central master data maintenance. We are also integrating it with the nonrecipient systems.

I like that Informatica MDM has robust matching technology. Informatica MDM is also porting the external Java applications for validations. I can consider that a must-have. It is also exposed to Rest API calls, and we can engage in real-time integrations with any third-party systems.

The configurations could be better. It is a bit confusing because we must develop two tools when building a data model in Informatica MDM. Even though Informatica MDM is a single tool, we have our hub console plus the provisioning tool within that. Whatever data model we are building in the hub console, we have to develop it in the provisioning tool again. It is double the work to create a data model. 

We are also using external calls or the Java custom plans functions. This can be both positive and negative. Since MDM as a client does not support any complex validation, we have to depend on the external call or a Java call. Every time we deployed, the entire solution was impacted if something went wrong. 

I have been using Informatica MDM for four years.

Informatica MDM can be more stable. There are some issues and some crashes, and sometimes, with the data load, we usually feel like we are doing an initial data load for any project. Sometimes, it could also be down to the production environment.

On a scale from one to ten, I would give stability a six.

Informatica MDM is a scalable solution. We probably have 1000 direct and indirect users with multiple use cases. But people solely working with Informatica MDM might be around 200 or 300. Most users are from business IT data support.

On a scale from one to ten, I would give scalability an eight.

Technical support is very responsive.

Positive

I was using SAP MDM, which is currently end of its life, so we migrated from SAP MDM to Informatica MDM.

The initial setup is straightforward. We have to make a jar file which we have to download from the link and download it as a client in our local mission. However, there are some challenges if we deploy Informatica MDM with IDQ on-premises. The deployment time depends on the use case. One or two people can maintain this solution.

We have seen a return on investment. From a business perspective, there have been many improvements since we migrated from SAP MDM.

I would recommend Informatica MDM to potential users. Even though Informatica MDM does not come with data quality, Informatica as a whole has some of the right tools. We have embedded the MDM with the IDQ informatic data quality in ways we can interact. It is the best fit for non-SAP systems.

On a scale from one to ten, I would give Informatica MDM an eight.

The primary use case of this solution is to extract technical metadata, tag the data dictionary, and profiling. We also use it for lineage and relationship aspects.

I recommend Informatica to my clients based in the finance and banking sectors. It has been very helpful.

The most valuable feature is its ability to extract metadata from various sources- be it an old SaaS application or the latest cloud application. They have a custom resource that allows us to customize no matter which source system we have, and we can customize it to extract the technical metadata.

They are moving from on-premises to the cloud. I expect all the on-premises features in the cloud version as well.

I have been using the product since 2019.

It is a perfectly stable solution and a leader in data governance tools in the current market.

The scalability is tough. For example, if you want to increase the scalability in the middle of the project, it would be a challenging process, but it is doable. As consultants, we analyze the requirements from the client end and, based on that, suggest the infrastructure for scalability.

Customer service is a piece of cake if you are a premium member. You need to raise a ticket, and they will immediately respond. For example, they will let you know if it is a limitation; if it is a bug, they will provide you with emergency bug fixes.

Positive

The initial setup is easy. If we compare version 10.4 and version 10.5, they had a major architectural change in terms of infrastructure. The setup for version 10.5 became easier.

The deployment was straightforward, and it took 10-15 minutes to deploy the solution, including migrating technical metadata (importing and exporting).

I would recommend that rather than implementing it on a complete whole enterprise project, start with some small system or a small project first. And from there on, you can move on one step at a time.

I would rate the product an eight out of ten.