IBM watsonx.data
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Reliable Data Platform ,Still Evolving Though
What do you like best about the product?
1.Flexible data handling and fast searches.
2. Queries run quickly and handle diverse data well.
3. I like how fast it processes and manages big data.
2. Queries run quickly and handle diverse data well.
3. I like how fast it processes and manages big data.
What do you dislike about the product?
1. Learning takes time for a beginner.
2. Initial setup also takes time.
2. Initial setup also takes time.
What problems is the product solving and how is that benefiting you?
For me, IBM watsonx.data helps address two main challenges — ensuring data quality and making it easily accessible for AI and analytics. In Responsible AI work, having a trusted, well-governed dataset is critical to avoiding bias and ensuring compliance. The platform’s governance tools make it easier to maintain lineage, manage permissions, and apply consistent policies across multiple data sources.
It also streamlines access to both structured and unstructured data, so instead of spending hours gathering and cleaning data, I can focus on building and testing AI models. Using it as a data warehouse has reduced the time it takes to prepare datasets for machine learning, which speeds up experimentation and shortens project cycles. Overall, it’s given me a more reliable foundation for developing AI systems that are transparent, scalable, and ethically sound.
It also streamlines access to both structured and unstructured data, so instead of spending hours gathering and cleaning data, I can focus on building and testing AI models. Using it as a data warehouse has reduced the time it takes to prepare datasets for machine learning, which speeds up experimentation and shortens project cycles. Overall, it’s given me a more reliable foundation for developing AI systems that are transparent, scalable, and ethically sound.
Reliable
What do you like best about the product?
What I really like about IBM watsonx.data is its ability to handle and analyze large amounts of structured and unstructured data from different sources all in one place. It’s flexible, integrates well with existing tools, and helps turn raw data into meaningful insights much faster. I also appreciate how it’s built for scalability, so it can grow with the business needs
What do you dislike about the product?
One thing I’ve noticed is that, because IBM watsonx.data is such a powerful and feature-rich platform, there can be a learning curve for new users to fully leverage all its capabilities. Also, depending on the size of the datasets and complexity of queries, performance tuning might be needed to get the best results. But once you get familiar with it, the benefits outweigh the initial challenges
What problems is the product solving and how is that benefiting you?
IBM watsonx.data is solving the problem of having data scattered across multiple systems and formats. Instead of spending a lot of time moving and preparing data, I can query and analyze it directly from where it resides, whether it’s in a data lake, warehouse, or external source. This saves time, reduces duplication, and makes it easier to get real-time insights for decision-making. It’s also helping improve collaboration, since different teams can work off the same unified view of the data
IBM Watsonx Usage Experience
What do you like best about the product?
I have Watsonx for IBM Call for code as it is a Pre-requisite of the Competiton to use the IBM Watsonx. IBM Watsonx has a wide range of AI Products which aligns well with the different usecases. It has it's own Foundation Models Like Granite which we used in our IBM Cal for code Project it's integration with the multiple other models is also easy liek for example Hugging Face Repo and DB Connections as well code Deployment in IBM Cloud. One good thing was the have documentation and walkthrough docs/videos for each and every AI model/functionalty Implementation. These docs/videos helped reduce some time in getting started as they are to the point. Talking about the customer support it is very quick i got problem with my account and got resolved in within a day or so. I have used these IBM Watsonx Three times and alway feel the Functionality and the power of AI integerated tools is amazaing.
What do you dislike about the product?
The things that i felt could have been more better is the limited Third party Resources and integrations though it has few popular tools and integration for some use cases the watsonx does not support them. The Pricing is more compared to other open resources example if i need Large Model Training or multi model usage in watsonx AI the cost increases there is no proper tanspaernecy in Cost upfront as comaprted to AWS. If i want to use the Watsonx AI with non IBM Tools custom connectors which by user needs to be build up is required which is time taking and some times the implementation goes waste.
What problems is the product solving and how is that benefiting you?
The AI Models it has huge computatuion and capable of Handeling Large amounts of data sets, example : Granite Models. These Granite Models already pretrianed with large amounts of data and for use case we have used LLM for passing our use case data as context for the Training Models to generate the results for us. The Results are 75-80% accurate. Teh IBM Granite Models have language support where it support large number of Languages across the world. Since it is integrated with the IBM Cloud everything becomes easy from development to Deployment But, if we want get the Third part tools which not supported by IBM is a bit complex to get it working. Rest it is dtraight forward approach if we are using everything like tools, models and apps from the IBM Cloud.
Makes working with data much easier
What do you like best about the product?
I like how easy it is to manage and search large datasets using the platform. The AI-assisted data preparation tools help me clean and organize data much faster than doing it manually. The interface is user-friendly, and the integration with other IBM products makes it easy to fit into our existing workflow. It also handles large amounts of data without slowing down, which is a big plus for my team.
What do you dislike about the product?
Some of the more advanced analytics features have a steep learning curve and require extra training to use effectively. Also, the cost might be on the higher side for smaller companies. Lastly, it needs a stable internet connection for most operations, so offline work is limited.
What problems is the product solving and how is that benefiting you?
IBM watsonx.data helps us centralize and manage large volumes of data from multiple sources in one platform. It reduces the time needed for data preparation, cleaning, and organization, allowing our team to focus on analysis and decision-making instead of manual processing. The platform’s AI-driven tools improve the accuracy of our datasets, which leads to more reliable insights for our business. Overall, it has increased productivity and made our data operations much smoother.
Data as a service, i think this is something fresh and new
What do you like best about the product?
The reason i explored IBM watsonx is, in my current org, we were also building a similiar kind of product, not at this scale but many of the funcitonalitier are common, the feature i liked specially is their prompt lab and how well it is easy to implement, and that actually provides a very good simulation for building different kinds of usecases a person may have. in terms of integration, the data source integration feels seemless a wide variety mainstream connectors are present and easy to integrate, didnt ineracted with the customer support as i didnt have to use it much
What do you dislike about the product?
This not a beginner friendly tool, a person should be well aware of the current AI-scenario, technical terms and how LLMS works upto some level, the UI is clean and minimal but many time i found a bit of difficulty in navigation between different screens, and sometimes i felt everything is given to me, and that made me confused what should i pick, the point is since there is big chunk of business and non-tech professionals are also adopting the use of LLMs into their workflows, and they could be a user of this platfrom, then the platform should hide some of the configuration and handle it via some assumptions, although this is just an opinion i am not very sure of the target audiene of watsonx. for my use i dont see much of use within my team, and current org, there are already many tools which are free and opensource for instance openmetadata, people who want production ready and readiness to scale within their org as they have that much data to take leverage, and exclusive proprietary platform, which is catered for them then this could be a good choice.
What problems is the product solving and how is that benefiting you?
the first is its proprietary nature with ease of integration with my data, that will help organization to quickly bootstrap their products, next is the fine tuning and its simulation with prompt labs, this will actually gives the user an idea how his model will behave without wasting much of his resources on billing and computing,
Review for Open House Leak House
What do you like best about the product?
I'm very impressed about the flexibilty it offers as Apache Iceberg and multiple query engines.
The Way is it being deisgned for handling the AI data for application.
The Feature of Data Governance & Quality Management
The Way is it being deisgned for handling the AI data for application.
The Feature of Data Governance & Quality Management
What do you dislike about the product?
Complicated to Adoption, big learning curve
Integration not that open, seems less capable.
Integration not that open, seems less capable.
What problems is the product solving and how is that benefiting you?
It makes data more meaningful to us & we can use that data for analysis, which further leads to AI Capabilities .
It's good but not so good, actually the editor is not so good but other than that it is awesome
What do you like best about the product?
The best things about watsonx.data is its UI, the way it is designed I loved it and also ease of accessing every single thing on the platform
What do you dislike about the product?
I can't say i dislike it but it is not upto my expectations from watsonx.data and it is Code editor of this platform, It can be design better and also there should be some flexibility like other code editor.
What problems is the product solving and how is that benefiting you?
I had to learn and practice some technologies like Docker and kubernatics and for that i have to install it in my personal computer but in IBM watsonx.data it is not required we can use it very easily like virtual computer with taking that much space and also works perfectly
Helped Us Cut Down Client Onboarding Time at Citi
What do you like best about the product?
I work as an Assistant Vice President in Citi’s client onboarding team, where we handle large volumes of client data from multiple sources — regulatory checks, KYC documents, transaction history, and internal risk systems. Before using watsonx.data, this information was spread across different tools, which made it slow and sometimes frustrating to pull together for verification. We needed a single platform to bring everything into one place so we could move faster while meeting strict compliance requirements.
Watsonx.data has given us a dependable central platform for storing and querying client data. Queries that previously took minutes now return results much faster, even with complex joins and large datasets. I also value its tight integration with IBM’s governance and security features, which means compliance checks happen in the background without extra manual work. Sharing consistent, up-to-date data across teams has also become much easier.
Watsonx.data has given us a dependable central platform for storing and querying client data. Queries that previously took minutes now return results much faster, even with complex joins and large datasets. I also value its tight integration with IBM’s governance and security features, which means compliance checks happen in the background without extra manual work. Sharing consistent, up-to-date data across teams has also become much easier.
What do you dislike about the product?
The initial setup was the most challenging part. Mapping our existing sources into watsonx.data wasn’t straightforward, and a few integrations needed help from IBM’s support team. The interface works fine but could be more intuitive, especially for new users who don’t have prior experience with enterprise data platforms.
What problems is the product solving and how is that benefiting you?
In Citi’s client onboarding team, where I work as an Assistant Vice President, we deal with huge amounts of data from different sources — regulatory checks, KYC documents, transaction history, and internal risk systems. Before IBM watsonx.data, this information was scattered across multiple tools, which meant a lot of manual effort to bring it together and verify.
Watsonx.data has solved this by giving us a single, governed platform where all of this data can be stored, queried, and shared securely. Now we can run complex queries across large datasets in minutes, and compliance checks are much smoother because the governance features are built in. This has directly helped us cut our client onboarding time from nearly two days to less than a day, which not only improves efficiency for our team but also gives new clients a faster, better experience.
Watsonx.data has solved this by giving us a single, governed platform where all of this data can be stored, queried, and shared securely. Now we can run complex queries across large datasets in minutes, and compliance checks are much smoother because the governance features are built in. This has directly helped us cut our client onboarding time from nearly two days to less than a day, which not only improves efficiency for our team but also gives new clients a faster, better experience.
Good AI Platform
What do you like best about the product?
enables highly customizable conversational AI across multiple channels like websites, mobile apps, WhatsApp, and Slack, supporting consistent user experiences .
What do you dislike about the product?
Looks like fre more enhancement can be performed such as on result driven and response.
What problems is the product solving and how is that benefiting you?
transforming over 1 million data points per second into immersive fan experiences via AI-driven content and personalization demonstrating practical enterprise deployment .
Very powerful and flexible platform for managing different variety of data.
What do you like best about the product?
Great while dealing with structured, unstructured and semi structured data. Highly scalable and easy to implement big data solutions. For me, the AI capabilities stand out like Gen Ai use cases such as RAG. It also has hybrid and multi-cloud deployment.
What do you dislike about the product?
The cost is quite on the higher side and it highly depends on the IBM ecosystem, outside of it some dependencies fail.
What problems is the product solving and how is that benefiting you?
Easily access all my data through a single entry point for updating daily trackers. Ai architecture and advanced analytics, drill through analytics are also very easy and fast to implement!
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