Cash and card planning has become data driven and now predicts demand and peak periods
What is our primary use case?
My main use case for IBM Watson Studio is that the data science experience has helped my business unit to make business decisions concerning management of cash and keeping stock of debit cards.
Generally, it helps us predict the amount of cash and debit cards we would need to meet the demands of customers at any given time.
What is most valuable?
IBM Watson Studio offers features that help predict profitability of terminals at any of our locations, predict peak and off-peak periods, and aid preparation.
These features also help us plan and improve cash management efficiency by relying on past data.
Out of those features, I find predicting profitability, peak periods, and improving cash management efficiency all valuable in my day-to-day work.
IBM Watson Studio has positively impacted my organization since we recently adopted its use.
What needs improvement?
IBM Watson Studio needs to improve its mobile experience. Additionally, I think a chatbot would be beneficial to guide users.
I would add that it can be difficult to navigate at times.
For how long have I used the solution?
I have been using IBM Watson Studio for six years.
What do I think about the stability of the solution?
IBM Watson Studio is stable.
What do I think about the scalability of the solution?
IBM Watson Studio is very scalable.
How are customer service and support?
The customer support is very proactive and transparent.
I would rate the customer support nine out of ten.
Which solution did I use previously and why did I switch?
I previously used Jupyter Notebook.
How was the initial setup?
My experience with pricing, setup cost, and licensing is that it is very cost-effective.
What was our ROI?
I have not seen a return on investment yet because I am just starting, thus I cannot provide any input yet. However, I would imagine it could provide a positive impact if I data mined and properly presented that visual tool to my stakeholders, though the negative impact is yet to be determined.
What's my experience with pricing, setup cost, and licensing?
Since we recently adopted IBM Watson Studio, I have noticed that it is very convenient for quick-hit applications, the cost of ownership is for personal use, and it offers easy deployment.
Which other solutions did I evaluate?
Before choosing IBM Watson Studio, I evaluated options such as Anaconda and RStudio.
What other advice do I have?
The advice I would give to others looking into using IBM Watson Studio is that this will depend on your application.
I believe IBM Watson Studio is agile enough to carry out most of my basic business intelligence tasks and could instantly show data-driven insights to drive twenty percent incremental revenue over existing results.
IBM Watson Studio offers a great development and deployment platform for data scientists. I would rate this review nine out of ten.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Flexible AI tools and seamless integration have streamlined data analysis and saved project time
What is our primary use case?
IBM Watson Studio offers open tools like Jupyter Notebooks and TensorFlow, which gives me the flexibility to choose the right tool. It is very easy to use and I frequently use it considering the documentation and information provided.
I use IBM Watson Studio's AI capabilities, which automates the process and makes it time efficient. It offers open tools and it seamlessly integrates and implements with a wide range of data sources, making it easy to analyze the data. IBM Watson Studio solves the purpose of providing a wide range of tools to analyze and visualize the data.
What is most valuable?
In my opinion, the best features IBM Watson Studio offers are seamless integration, flexibility, and great customer support.
IBM Watson Studio has positively impacted my organization by being very cost effective and time-saving, contributing to savings of 30 to 55%.
What needs improvement?
One of the disadvantages I have with IBM Watson Studio is the cost, as it is a bit more on the higher side considering the market competition. It requires specific and dedicated training and expertise to work on this tool. Additionally, it has a dependency on IBM for ongoing support and updates.
For how long have I used the solution?
I have been working in my current field for seven years.
What do I think about the stability of the solution?
IBM Watson Studio is stable.
What do I think about the scalability of the solution?
IBM Watson Studio's scalability is very good, as it can handle my organization perfectly.
How are customer service and support?
The customer support for IBM Watson Studio is very responsive and proactive.
Which solution did I use previously and why did I switch?
Before using IBM Watson Studio, I previously used Vertex AI.
How was the initial setup?
My advice to others looking into using IBM Watson Studio is that it is very easy to set up and meets requirements.
What was our ROI?
I have seen a return on investment with IBM Watson Studio, which is an excellent cloud-based solution for automating machine learning and deploying AI-based models, and it helps to save time.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, setup cost, and licensing is that it is more expensive compared to other solutions.
Which other solutions did I evaluate?
Before choosing IBM Watson Studio, I evaluated other options, specifically Alteryx.
What other advice do I have?
IBM Watson Studio is well-organized and supports a large range of data science and ML tasks that can be performed almost seamlessly. Its easy integration makes it easy to work on my existing data sets. I give this review an overall rating of 9.
Collaborative tools have transformed how our team builds models and makes faster decisions
What is our primary use case?
My main use case for IBM Watson Studio is for learning and helping other fellow members to learn some concepts of machine learning. I learned about IBM Watson through a Coursera specialization and then continued experimenting with IBM Cloud for some time. Whether using their services or storing objects in a bucket, it was an amazing experience.
I specifically use IBM Watson Studio for machine learning, and it is well suited if you want to use some well-known services without investing much of your time there. There are a lot of services that can be used and experimented with, and these services are just a few clicks away. There is also a free plan if you want to try before actually using the product.
I also mainly use IBM Watson Studio in the data science team, by the data science team. It mainly addresses the DevOps overhead of heavy Jupyter notebooks and provides an integrated interface for people who are not familiar with the infrastructure and storage. It also provides a point of integration with other IBM services.
How has it helped my organization?
IBM Watson Studio has impacted my organization positively by giving us an idea on which models to focus on, cutting the turnaround times from three days to less than four hours. While the models are not perfect at the first run, it has also been a great tool and helpful, especially because we are able to save a lot of time and cut a lot of costs.
Our turnaround times are reduced from three days to one day, which allows us to create more models and service more clients.
What is most valuable?
In my opinion, the best features IBM Watson Studio offers include IBM Watson services like Speech to Text, which are just some clicks away. You just need to specify some basic details like location, and the resource will be ready for use. IBM DB2 engine is a fully managed relational database for all our needs, and sharing with the team is very convenient. GitHub integration is great, and the free pricing plan if you want to try things out before initially purchasing this tool is great.
One of the most robust features is very great. All the features are great, but there are a lot of services available from which users can choose what suits their needs. This feature helps to predict the profitability of terminals at any of our locations and helps to predict peak and off-peak periods, hence it aids preparation. Also, it helps us to plan and improve on cash management efficiency by relaying past data.
AutoAI makes creating predictive models so much easier and faster.
What needs improvement?
It takes time to integrate with IBM Watson Studio.
IBM Watson Studio desktop should be improved.
IBM Watson Studio needs to improve on its desktop app. Additionally, a help chatbot would go a long way to guide users and save a lot of time.
For how long have I used the solution?
I have been using IBM Watson Studio for the past nine years, even on my previous organization I was using it.
What do I think about the stability of the solution?
IBM Watson Studio is stable in my experience as I have not experienced any lagging or downtime.
What do I think about the scalability of the solution?
IBM Watson Studio is very scalable; it has continued to grow with my organization's needs and caters to my organization's needs well.
How are customer service and support?
The customer support is very professional and unparalleled.
Which solution did I use previously and why did I switch?
I previously used Google Cloud AI.
I switched from Google Cloud AI to IBM Watson Studio because Google Cloud may be a good place but it is not as easy to understand as IBM Watson Studio. Google Cloud has a lot of things and it is terrifying for a beginner, while IBM Watson Studio is very simple and user-friendly, making it easy to adopt as a beginner. Additionally, the cost of IBM Watson Studio is more cost-effective compared to Google Cloud AI.
How was the initial setup?
My experience with pricing, setup cost, and licensing shows that it is a very cost-effective and affordable tool that can be used by any size of organization.
I would say it has been a very helpful tool because it has ease of deployment, thus saving a lot of time. The cost of ownership is somewhat cost-effective, making it very convenient for quick hit applications.
What about the implementation team?
My company is a customer but has no business relationship with this vendor other than being a customer.
What was our ROI?
IBM Watson Studio has impacted my organization positively by cutting turnaround times from three days to less than four hours and saving costs.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, setup cost, and licensing shows that it is a very cost-effective and affordable tool that can be used by any size of organization.
Which other solutions did I evaluate?
Before choosing IBM Watson Studio, I evaluated other options such as Pub/Sub.
What other advice do I have?
I would advise others looking into using IBM Watson Studio that it is well suited for any size of organization and allows for collaboration between technical and non-technical users. However, it is also less suited for companies that already have large production ML pipelines as the cost of migration could be higher and the initial overhead of learning the tools still remains.
I love IBM Watson Studio because it makes my work easier, saves time and cost. It helps business units to make some business decisions concerning the management of cash and keeping stocks of debit cards. Generally, it helps to predict the amount of cash and debit cards I would be needing to meet the demands of customers at the time.
I rate this product a 9 out of 10 overall.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Efficient Data Management with Powerful Analytics
What do you like best about the product?
I use IBM watsonx.data to handle and access large amounts of data, and it's great for fast querying and analytics. I really like that the platform helps me handle large and complex datasets and does a good job with storage optimization, which helps decrease computational costs. The efficiency of the system is impressive, particularly with the lakehouse architecture, which supports high performance use. I appreciate the platform's integration with different AI tools, which enhances its utility for me. The analytics tools are strong, helping me monitor heavy workloads. It also enables easy extraction of insights from raw data and supports training and deploying machine learning models within the lakehouse. The BI tools assist in creating dashboards for outputs across developed models and usages.
What do you dislike about the product?
Most of all the whole platform and usability were good but what I feel could be improved is the platform's documentation. In the initial times, I found it hard to understand the documentation which is not fully understandable for new users.
What problems is the product solving and how is that benefiting you?
I use IBM watsonx.data to handle large datasets efficiently. It optimizes storage, reduces computational costs, and supports fast querying. The platform's integration with AI tools enhances insight extraction and model deployment. I switched from MongoDB Atlas for improved performance and easier data export.
IBM watsonx.data: Flexible Lakehouse SQL on Object Storage with Iceberg Support
What do you like best about the product?
I used IBM watsonx.data in several client projects over the past few months, mainly for data-heavy tasks where we needed a lakehouse-style setup. What I liked most is that it allowed us to keep data in object storage while still querying it with SQL, without needing to move everything into a traditional warehouse. This cut down on a lot of unnecessary data duplication.
The support for open formats like Iceberg was truly helpful. In one project, we had schema changes halfway through. Being able to manage versioning without disrupting existing queries saved us time.
What do you dislike about the product?
The initial setup took us some time, especially when it came to configuring storage and access controls. It’s not exactly plug-and-play, so there is a learning curve for teams new to lakehouse architectures. We also needed to review the documentation closely to understand some configuration steps. Once it was set up, it worked well. However, onboarding could definitely be smoother.
What problems is the product solving and how is that benefiting you?
In some of our projects, we faced scattered data across various storage systems. This made analytics and reporting slower and more difficult to manage. With watsonx.data, we centralized data in object storage and could query it directly without having to move it into separate warehouse systems.
This reduced data duplication and simplified our pipeline design. It also allowed our team to run analytical queries faster and prepare datasets for ML workflows more efficiently. Overall, it improved collaboration between data engineers and analysts, as everyone could work on the same governed data layer.
Scalable Analytics Platform with Smooth AI Integration
What do you like best about the product?
I like IBM watsonx.data for its scalability, which lets me manage growing datasets without needing to redesign my systems. Its high analytics performance speeds up the process of gaining insights, and the smooth AI/ML integration makes building and running models on the same dataset much simpler. I also appreciate the support for open data formats, as it helps avoid vendor lock-in, while keeping storage and processing costs efficient.
What do you dislike about the product?
Some things that could be improved in IBM watsonx.data are better documentation for advanced use cases, simpler initial setup and configuration, and more out-of-the-box integrations with third-party tools to reduce onboarding time. Improvements could be made in UI simplicity, faster onboarding tutorials, clearer cost visibility, and more real-world sample use cases to help teams adopt and use the platform more effectively. The initial setup was moderately challenging — it required careful configuration of cloud resources and permissions.
What problems is the product solving and how is that benefiting you?
I use IBM watsonx.data for centralized data storage and analytics. It solves problems like handling large-scale data efficiently, reducing data silos, improving query performance, and supports AI/ML workloads with scalable and cost-efficient data access.
Enterprise-Ready Data Platform with Flexible Hybrid Support and Built-In Governance
What do you like best about the product?
like how IBM watsonx.data feels built for real world enterprise needs. It’s flexible enough to run across hybrid environments, supports open formats, and doesn’t lock you into one engine. What really stands out is the built in governance and AI readiness, which makes managing and using data at scale feel much more practical and streamlined
What do you dislike about the product?
watsonx.data can be a little complex to get started with
What problems is the product solving and how is that benefiting you?
What I like about IBM watsonx.data is that it tackles the messy reality of scattered, siloed data and makes it easier to bring everything together in one place. It also reduces the fear of vendor lock-in. For me, that means spending less time dealing with infrastructure headaches and more time actually getting useful insights from the data
Hybrid Data Solution with Room for Improvement
What do you like best about the product?
I like IBM watsonx.data's ability to unify data across hybrid environments while controlling costs and supporting both structured and unstructured data for AI. Its open architecture and strong integration capabilities provide flexibility and prevent vendor lock-in, making it easier to turn diverse data into actionable insights. These capabilities allow us to centralize fragmented data across environments, reduce infrastructure costs, and efficiently power AI models with diverse datasets for faster and more informed decision making.
What do you dislike about the product?
Some areas for improvement include simplifying initial setup and configuration, enhancing performance tuning guidance, and providing more intuitive management and monitoring tools. Improve documentation, simplify deployment, enhance performance, and strengthen governance tools.
What problems is the product solving and how is that benefiting you?
I use IBM watsonx.data to overcome data silos and high storage costs, unifying data from various environments. It supports AI by leveraging both structured and unstructured data, centralizing fragmented data for informed decision-making while controlling infrastructure costs.
Powerful Data Analytics and Visualization Tool
What do you like best about the product?
It is very useful for data analytics and visualization.
What do you dislike about the product?
It is taking time when updating data in the system.
What problems is the product solving and how is that benefiting you?
It is solving data processing and data management.
Cost-Effective and Flexible, Needs UI/UX Improvements
What do you like best about the product?
I like using IBM watsonx.data because it is cost-effective and flexible for working in a hybrid cloud environment. It makes it easy for me to restructure my data and organize unstructured data, which helps me understand the correct picture of the business. Additionally, setting up IBM watsonx.data was quite easy.
What do you dislike about the product?
Sometimes performance and also UI/UX need to be improved.
What problems is the product solving and how is that benefiting you?
I use IBM watsonx.data to organize unstructured data, helping me understand the correct picture of business.
