Informatica Intelligent Data Management Cloud (IDMC) Reviews

Informatica MDM is used for our customers' master data management journey. The solution meets our client's requirements for their customer-supplier or any other entities.

The solution's most valuable features are its data quality, match-merge engine, and CLAIRE AI engine, which helps with AI automation.

Compared to other tools in the market, Informatica MDM is costly.

I have been using Informatica MDM for 13 years.

Informatica MDM is highly stable compared to any other tool in the market.

Informatica MDM is a highly scalable solution. Our clients for Informatica MDM are mostly medium and enterprise businesses.

I rate the solution eight and a half out of ten for scalability.

Technical support refuses to support us if we customize the tool. We customize the product based on the customer's needs, and we expect Informatica to provide support during that time as well.

On a scale from one to ten, where one is difficult and ten is easy, I rate the solution's initial setup an eight out of ten for on-premises and nine out of ten for the cloud version. Setting up the tool in the cloud via IDMC doesn't take much time.

The solution can be deployed within a few hours.

Our customers have seen a return on investment with the solution.

Informatica MDM is an expensive solution. On a scale from one to ten, where one is cheap and ten is expensive, I rate the solution's pricing nine and a half out of ten.

I would recommend the solution to other users because of its reliability and stability. Compared to other product vendors, Informatica MDM always has additional features. Informatica MDM has a CLAIRE AI engine, which helps with AI automation.

Overall, I rate the solution eight and a half 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.

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.

Neutral

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.

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.

The use cases attached to Informatica MDM may vary for different industries. Suppose I talk about industries related to healthcare and life sciences. In the aforementioned scenario related to healthcare and life sciences, there have been situations where a lot of sales representatives have issues while trying to set up calls with different physicians. Informatica or any MDM solution usually helps people in the healthcare industry optimize their business processes, especially when setting up calls with different physicians. Typically, Informatica MDM helps to reduce the number of duplicate details related to a physician or the data related to a particular organization data so that the sales representatives don't end up sending duplicate call invites to the same physician, especially those physicians who have probably different versions of their own name.

I think the integration feature is probably one of the key features in Informatica MDM. When compared to other Indian products, Informatica MDM has an edge due to its integration capabilities.

I don't see any area of improvement required when it comes to Informatica MDM since it is one of the leading products in the market. Some unification in Informatica MDM's UI side would probably make it better. I find Reltio more intuitive when compared to Informatica MDM's UI. Informatica MDM's UI is not intuitive enough. Informatica MDM's UI can be made a bit more intuitive.

I think all the latest up-to-date features are made available in Informatica solution. When it comes to Informatica MDM, all the features are mostly included, because of which there is nothing that needs to be added.

The long response time taken by the technical team of Informatica can be considered for improvement.

I have experience with Informatica MDM. I currently work for a company that has an alliance partnership with Informatica. I use Informatica to develop MDM solutions for my company's clients. I work as a solution provider since my company wants its employees to work as middlemen for Informatica products. In my company, we use Informatica to build solutions for clients. I use the latest version of Informatica MDM.

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

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

If it is for a single region deployment, the number of users who use the solution can vary somewhere between ten to fifteen, but if it is for a global deployment, then there could be hundreds of users who would use the solution.

The solution is used on a daily basis in my organization.

If there is a super high-priority issue, then the technical team of Informatica is super responsive. However, if it is a low-priority issue, they usually take a little longer than their usual response time. I rate the technical support an eight out of ten.

Positive

When it comes to Informatica MDM, it is mostly a cloud or SaaS-based product, because of which the configuration or setup phases may not pose a challenge. Most of the MDM solutions are now SaaS-based products that the product vendor maintains, and so such products are mostly accessed through web URLs over the cloud. Right now, any configuration or setup is not challenging, which used to be the case several years ago when these solutions were deployed on an on-premises model, because of which installation and other such procedures were required. Currently, most products are SaaS-based solutions, so there are no such challenges with respect to setup.

I rate the initial setup phase of Informatica MDM as a nine on a scale of one to ten, where one is difficult, and ten is easy.

Predominantly, Informatica MDM gets deployed on a public cloud. There is a provision to go ahead with if any of our company's clients want the product to be deployed on a private cloud, but it comes at a very high cost.

The time taken for the end-to-end deployment process depends on the complexity of the project that you are working on, but if I have to give you a standard answer, it would be approximately six months to start from scratch until production goes live.

Considering the members from the production and UAT departments, the UAT department dealt with the product for three weeks, and the overall deployment process took place over a month to a month and a half. Around eight staff members were required for the deployment process, including an Informatica developer responsible for configurations, a lead developer, two people for integration, a project manager, and one solution architect.

One of the in-house teams in my company carried out the deployment process.

I would say that the return on investment from the use of the solution is fairly good.

I rate the product's ROI a nine on a scale of one to ten, where one is zero percent return on investment, and ten is one hundred percent return on investment.

I rate the product's pricing a nine on a scale of one to ten, where one is low price, and ten is high price. The product does come at a certain cost since it is one of the best products in the market. The payments for the solution are made on a subscription-based model, meaning it is a pay-as-you-go model. In general, the costs attached to the solution depend on its usage.

The costs, in addition to the standard licensing fees for the solution, depend on users' requirements.

The people required for the maintenance of Informatica MDM depend upon the requirements of the clients who use the product.

I would say that a team of five people that includes a solution architect who operates in a partial capacity would be good enough to take care of Informatica MDM's maintenance phase.

I recommend the solution to those who plan to use it.

I rate the overall product a nine out of ten.

We utilize it for two tasks. The first involves collaborating with our business team to identify specific data quality issues, and this part of the process runs smoothly. However, the second task is focused on using Informatica to investigate data issues that aren't entirely clear. This aspect doesn't yield satisfactory results, and we struggle to effectively pinpoint the problems using the tool.

When discussing data quality within IDMC (Informatica Data Management Cloud), it involves several modules, including CDI (Cloud Data Integration), CDP (Cloud Data Profiling), and Cloud Data Governance. Initially, we focused on Cloud Data Integration, which was the aspect we were already familiar with. We encountered challenges when trying to explore data quality issues using Informatica. We primarily used Cloud Data Profiling to connect with Cloud Data Governance, a tool also used by Teva. This integration allowed users to access data quality results within the data governance catalog.

We are currently working on improving the user interface for our end users. At the moment, our end users do not frequently connect to Informatica for data quality because they have trouble locating the results within the platform. The remediation processes are not adequately supported within it, and we cannot easily assign data quality issues to specific individuals, nor can we send the entire results to anyone via email. While we can send reports via email, we lack the capability to attach the actual results or provide links to them. We have been suggesting these improvements to Informatica, but as of now, we have not seen any significant progress. If given the opportunity, I would like to address these concerns, particularly with regard to enhancing the end-user experience. Also, there were initial architectural misalignments with the product. We believed that Informatica could directly connect to the source systems, but while it is technically possible, doing so would significantly impact the performance of the source systems. As a result, we did not receive approval to connect directly to them, and we did not plan to maintain copies of these systems.

I have been working with it for one year.

I would rate the stability of the platform seven out of ten. We have faced a recurring issue after each quarterly upgrade or new release in their cloud platform. This problem involves the functionality that used to work correctly no longer operating as expected.

I would rate its scalability capabilities six out of ten.

Overall, I would rate it six out of ten.

We have a customer with a lot of data. We see a lot of quality issues, so we have implemented a data quality solution on-premises. Since everything is moving to the cloud, we are also moving our data quality solution to the cloud. We are building data quality goals for profiling, matching, and enrichment. There are other use cases, one example is matching data in our on-premises MDM system. To do this, we need to clean the data and profile it to understand its quality. We can then use data quality rules and matching modules to identify and resolve data quality issues. We also use it to validate customer email addresses and phone numbers on the fly in our in-house customer applications. Other use cases include data integration and data governance.

The user-friendliness and performance of Informatica is quite impressive. It’s easier to grasp and understand data quality, and how data governance tools work, as they are built on the same underlying architecture. The QCA addresses performance issues, whether it’s related to data, PowerCenter, or any other aspect. The shared underlying architecture enables me to pinpoint parameters for optimization. Informatica can handle large volumes of data without compromising performance. While other tools may offer similar capabilities, Informatica consistently delivers impressive results.

Informatica Cloud Data Quality provides many of the capabilities found in on-premises solutions. While the cloud edition offers additional features, it has also deprecated certain functionalities, such as connecting to web services. In the on-premises version, features like web service consumer and web service provider are available, but these functionalities are currently unavailable in the cloud edition. Vendors offer options for custom solutions, although they may not offer the same functionality as the on-premises version.

I have been using Informatica Cloud Data Quality for years. We are using the latest version of the solution.

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

I rate the solution’s scalability an eight out of ten.

The team is acquiring knowledge. In our previous on-premises environment, we had a team of experienced engineers who were familiar with the system and could provide instant answers. However, in the new cloud environment, there’s a learning curve in the new cloud environment, and it may take some time for them to understand and respond effectively.

Positive

Overall, I rate the solution a seven-point five out of ten.

The primary use case is for data governance, data quality, data lineages, and data cataloging.

It is used by an internal team for data discovery. However, we are not able to provide business area-level access to other users because of data security.

It's a good product, and the features for data quality, data cataloging, and UI are excellent. It has a lot of advantages.

The major points of improvement are stability, integration, and permission to sub-folders or categories of data. If you want to provide access to a particular type of data discovery to a team, it exposes a lot of data points which in turn makes Axon not that useful for them.

Permissions, scalability, and connection with other informatic tools are the main areas that need development. Integration with other tools, such as IDQ, is challenging. The management of data discovery is highly challenging because many data points have identical definitions.

Mostly, this will come under the data security permission level or admin access.

I have been using Axon for a year, but I have been using other solutions from Informatica for five years now. 

I currently use the latest version of Axon.

I would rate the stability a three or four out of ten. It's not loading at all and it keeps on getting down.

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

We developed something on IDQ on the data quality side. If there is a huge data or a big workload, it takes a lot of time to run and we don't get the result in Axon. 

There are around 10 to 15 end users using Axon in our organization. 

There is a different team for platform maintenance, management, and deployment. There are around ten people on the team. It's a mix of engineers and developers.

The platform team had some difficulties with admin-level things and keeping Axon up all the time because it's on-premises. 

The solution is expensive.

Not on the governance, but I have used Databricks, Azure Synapse, Oracle, and other services by Azure.

Databricks is my personal favorite because of the scalability and the runtime it takes to load a large amount of data from a data engineering and data science perspective. 

I'll probably look for another solution because it has stability issues. When it's needed, it's usually unavailable.

However, it is a good product the way it's designed. It's a very good product, and it has a lot of features that are required for data governance. It moved all the Excel work to our web page, which can be accessible to everyone. It's a good product, but it needs some improvement.

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