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

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

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

The product exhibits satisfying scalability. 

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

Positive

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

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

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

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

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

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

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

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.

We had proof of concepts where certain test cases were given to improve the functionality of the vendor regarding the supply chain.

So, it was a proof of concept. It was not applied in production.

It was just illustrating the capabilities of the platform, and we were comparing it with other platforms.

If we use it comprehensively, we can improve the data quality metrics or KPIs. 

So use as much data as you process, and have a solid strategy in place. If you don't have a comprehensive approach, then you don't draw any benefits or value from such a complicated platform. So, you have to have the objectives very well set up and be part of your daily activities in order to benefit from such a platform.

I like the connectivity and richness of features for the technical team, the maturity of the product, and that it had a cloud version. There is Informatica Cloud, and it is part of the Informatica Cloud platform.

It also has integration with other data governance tools from Infromatic, which is also good. 

It offers integration with some of the latest data platforms, streaming platforms like Kafka, for example. And the setup of the platform is very challenging.

Regarding the cloud, it is deployed in the private cloud of the vendor, so it poses challenges regarding the security of the data and the confidentiality of the data. There was no offering of a cloud version deployable in the client's tenant where all the data would reside.

I used it for a couple of months because it was only demoed for the client.

It is a stable product. 

The customer servive and support could be faster. There is a slow turnaround. 

Neutral

We just compared the data profiling features or data quality features between the tools. There was a rich functionality available in the system, but we did not draw any conclusions regarding how it improved or may have improved.

It was set up by the vendor for the demo. To deploy the Informatica Enterprise Data Catalog, you need at least two admins and architects. 

It enables business processes by providing trusted data that can be integrated into many processes around data.

It's very expensive. It's subscription-based. There are several levels of subscription.

The price is high, but the competitors are even higher and more experienced, like Collibra.

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

I would recommend it due to the consistency of vision and very good integration between different products of Informatica. It offers wide coverage, the widest coverage of technologies from the major players.

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

The data mapping capability is a valuable feature. 

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

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

Its a very stable solution. 

There are twenty five customers at present. 

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

It's an expensive solution. 

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

We use the product to store data. We have established a structured system where we store all relevant information, including donor details, images, and series, ensuring easy accessibility for users.

The product's most valuable feature is the ability to select the data flow and lineage.

Informatica Axon's response times and certain aspects of the admin panel could be enhanced for better usability.

We have been using Informatica Axon for more than one year.

I rate the platform's stability as seven out of ten.

We have around 400 Informatica Axon users in our organization. I rate the scalability a nine out of ten.

The technical support services at the moment are impeccable. The support team has answers for everything. They have maintained this level of quality as we progress. There aren't many issues currently, although we're awaiting the migration to the cloud.

Positive

The implementation process could be simplified.

The platform has a premium cost. I rate the pricing as seven out of ten.

The product needs to be adequately supported in the current setup. I advise others to conduct a proof of concept (POC) and assess how it progresses. The learning curve can be steep, but with sufficient time investment, they can also impart knowledge to the users, which is crucial for effective change management.

I recommend it to others and rate it an eight out of ten.

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

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

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

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

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

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

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

The technical support services are good.

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

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

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

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

I advise others to understand the different data resources available in the organization before making the purchase decision. I rate it an eight out of ten.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

There are around 500 end users. 

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

Neutral

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

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

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

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

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

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

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

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

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

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

I use Informatica Data Quality to consolidate master data across our organization, ensuring consistent and high-quality data. Combining MDM and data quality features, we address issues like customer data inaccuracies. Additionally, we document business terms in our data catalog for a unified view across the company.

The profiling feature in Informatica Data Quality is incredibly effective for data governance. It helps identify data types and potential duplicates, streamlining the detection process. It also suggests matches with predefined fields in our catalog.

There is room for improvement in the Data Marketplace aspect. Specifically, we found some basic functionalities lacking, like transparent integration of quality indicators. Customizability and additional features in the Data Marketplace would be welcomed in future updates. 

I have been working with Informatica Data Quality for three years.

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

Scalability with Informatica had some initial challenges, especially when ingesting metadata into the data catalog due to the large volume of source systems and data. While it eventually worked, it took considerable time. Overall, I would rate it a six out of ten for scalability at the moment.

Initially, a few hundred users were using Informatica, but now it is a few thousand. 

The technical support is good. I would rate them at a seven out of ten.

Neutral

Maintenance and troubleshooting are handled solely by the vendor.

Informatica's licensing cost is among the highest in the market. I would rate it as a five out of ten in terms of costliness. With Informatica, we have a single bundled agreement covering the entire solution based on total capacity rather than specific modules. 

Before choosing Informatica, we evaluated various vendors, including IBM and Microsoft. Initially, we considered a best-of-breed approach, but Informatica emerged as the preferred choice for three out of four key areas: MDM, data quality, and data marketplace. While there were some limitations in certain tools like the data marketplace and data catalog, we opted for Informatica's complete suite to avoid additional integration efforts and to capitalize on its strengths in critical areas.

The rule-based strategies feature in Informatica Data Quality has greatly benefited our data quality initiatives. With predefined rules in place, it provides a solid framework for assessing data quality. While data quality ultimately depends on source system updates, these rules give valuable insights and are heavily used alongside automatic profiling for effective data management.

Informatica Data Quality works within our IT setup for analyzing and reporting on data quality. It doesn't directly integrate with other tools, but it gives us clear insights. Data flows from sources to Informatica for analysis, and then to data stewards for updates, but there is no integration back to the original applications yet.

Navigating between modules within Informatica isn't always seamless. For instance, actions in the catalog might not be visible in the data marketplace.

Overall, I would rate Informatica Data Quality as an eight out of ten.

We use the solution for party data management. The tool ensures that the information is the same in every business system so that all the systems have the same view of what parties are involved in an agreement.

It is easy to create REST-based interfaces of the master data objects. It can be used for reading, creating, and modifying the master data objects of the parties, their addresses, and contact information. The creation and modification features always consider the data quality rules, and it is a very powerful way to provide a data interface to master data objects. It is missing in many other tools.

The solution meets the performance needs of my organization. There isn't much that Informatica doesn't do. The maintenance of the data quality rules has been improved. It’s a lot better than it was when we started. Replacing the old SOAP APIs with the new REST APIs is a great improvement. It’s still a work in progress. Some interfaces still use the old SOAP APIs.

The management of different properties is sometimes divided between different user interfaces. The tool should provide a unified user interface to manage the data objects.

I have been working with the tool for ten years.

The tool can scale according to our needs.

The initial setup was not difficult. We had very good support from Informatica’s professional services.

The team was a mix of in-house, vendor and other consultants.

The product has a high price point. However, it does not lack features. TIBCO EBX and Profisee are much cheaper. However, they lack features that Informatica MDM provides out of the box. Though the list price of Informatica MDM is high, it is negotiable.

I do not have experience with the new workflow engine. It is much more extensible because it is a full business process engine. Overall, I rate the product a nine out of ten.

I mainly use it for data management.

It's for tool management, maintaining the golden record of customer status.

Data integration should be improved. Another area of improvement is customer applications, along with business 365 application case integration between applications.

I have been using this solution for three years now. I work with the latest update.

I would rate the stability a six out of ten. There is room for improvement in the stability.

I would rate the scalability of the solution an eight out of ten. There are currently nine end users in my team. There might be more people in the company. 

Recently, Informatica MDM upgraded, it transitioned from its traditional on-premise MDM offerings to a cloud-based platform called Informatica Intelligent Cloud Services (IICS). So, we are not planning to expand its further usage.

The customer support and service are helpful. 

Neutral

I would rate my experience with the initial setup an eight out of ten, where one is difficult, and ten is easy. 

It is very easy to set up. However, the deployment time depends on the configuration. It can take 10 to 15 minutes.

The pricing is reasonable.

The users should have more training on Informatica MDM, especially practical training. The vendor should provide some practical training.

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