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.

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.

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

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

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

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

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

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

Enterprise-sized customers can choose Informatica Cloud Data Integration.

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

The product's initial setup phase was not difficult.

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

The solution is deployed on the cloud.

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

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

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

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

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

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

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

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

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

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

I rate the tool a seven out of ten.

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.

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.

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 the solution for preparing the data according to the data quality rules.

The mapping and mapplet features are valuable. The product improves data quality. The processes are running smoothly. The interface is good. Data integration is easy.

I use the solution to integrate on-premise and cloud products. There are issues with connectors to cloud applications. The integration process is not easy. The customer experience must be improved. When we implemented it, we did it with the help of Informatica professionals. I would not recommend them as an implementer. They didn't have any experience. It was very hard. The customer experience was not good. It's hard to understand the new features of the HR list. The tool must have more options for connectors. It is hard to connect to some of the applications. The remediation process is not straightforward.

I have been using the solution for one and a half years.

I rate the tool’s stability an eight out of ten.

I rate the tool’s scalability a six out of ten. It is a bit complex to scale. We have 20 users.

The technical support is not good. The support team must improve the quality of support.

The product is cloud-based.

We got a 50% discount.

We couldn’t deliver the full project. I will not recommend the product to others. Overall, I rate the solution a seven out of ten.

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.

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 use the solution in my company for data ingestion and the ETL process. Though in our company, we started to use the product for data cataloging, we still use it for ETL and data integration.

Informatica Enterprise Data Lake is a product that was recommended to our company after we got in touch with various product vendors, and all of them suggested that Informatica is the best in the market, especially as per Gartner's report. A lot of reports were available for the product around five or six years ago, which was before our company decided to use Informatica Enterprise Data Lake. Informatica Enterprise Data Lake is probably the right fit for our infrastructure.

Governance, data dictionary, and data cataloging are not available in Informatica Enterprise Data Lake. A lot of businesses are facing issues related to understanding the area revolving around insights of data.

At Informatica Enterprise Data Lake's level, in our company, we have a lot of redundant data in a lot of our core systems. The basic thing that our company wants is for the product to develop a reporting layer and access data from the document layer so that we can avoid duplication in projects, databases, and data.

There is a lot of maintenance to be done owing to the instability users may face every time because of the huge processing capacity as the company has around more than 50 nodes, which causes a lot of maintenance issues because of which a lot of people don't benefit from the platform as it functions in a slow manner.

Informatica Enterprise Data Lake's setup process was complex since it doesn't support a lot of real-time systems. Every time, we have to find different tools we can use in our company with the solution since it doesn't support many real-time systems. Even if our company invests in some tools, Informatica Enterprise Data Lake creates too many small files with some issues, which we cannot read because we invested in HBase and Kudu, but performance-wise, the process is slow.

I have been using Informatica Enterprise Data Lake for four to five years.

The area revolving around the stability of the product depends on how we have set up the clusters in our company. In our company, if we consider high availability tools for every service, we distribute services according to multiple racks and nodes, meaning we self-structure them to fix the issues.

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

It is a scalable solution. If you want to expand the tool, it's not a big task, but there is a need to procure the hardware and space in Cloudera, where our has stored Informatica Enterprise Data Lake. The process of using the tool's scalability option is well documented.

Scalability-wise, I rate the solution an eight to nine out of ten.
Considering so many dashboards built into the product, more than 100 users of the solution. The tool's reporting layer is also used in our company.

I have experience with the solution's technical support. The solution provided by the support team for the product is fine, but there is some time delay when it comes to certain issues. The support takes time to review the logs and get a solution, meaning the support is able to resolve the issues even though it takes time.

I rate the technical support a six to seven out of ten.

My company uses Informatica EDC for cataloging along with Informatica Analyst.

The solution is deployed on an on-premises model.

The product's deployment phase was carried out with the help of some professional services, but it was not with the help of a third-party professional service. My company could manage migrations and upgrades with our own in-house support team.

The licenses attached to the solution are highly priced. Informatica has licensing models for every product and for every feature, like the web service feature, which is something my company doesn't like. When our company buys a product, it should come with all these features. I would give the product a low rating in the area of pricing.

I rate the overall tool a seven out of ten.