My main use case for IBM Cloud Pak for Data includes storing utility data to build a smart utility solution for the prediction of future trends. All data is stored, and with the help of AI and machine learning algorithms, analytics dashboards are built on the same platform. It helps manage and store high volumes of both structured and unstructured data and deliver desired results in optimum time.

A specific example of how I used IBM Cloud Pak for Data in one of my projects is that it serves as a very fully scalable platform for data and analytics. We use it to provide data solutions for our customers and provide various industry solutions for clients that need a cloud data platform that can be used for data analytics, data science, and data visualization.

The best features that IBM Cloud Pak for Data offers include data analytics, data science, data management, data catalog, inbuilt AI and machine learning capability, and integration with other applications, along with very safe cloud storage.

I find all of these features to be the most valuable for my daily work because they increase our impact by combining AI skills with advanced analytics and machine learning in an easy-to-use visual interface. Visualization and reporting are great in IBM Cloud Pak for Data, allowing me to manage data spread across distributed stores and clouds.

IBM Cloud Pak for Data has positively impacted my organization as it saves a lot of time by predicting outcomes faster using a platform built with data fabric architecture. It is easy to collect, organize, and analyze data no matter where it is located. Manual cataloging is eliminated, thereby saving a lot of cost, approximately 50 to 60 percent. Additionally, it significantly reduces data footprint, and AI and ML analysis for predictive analytics are excellent.

To improve IBM Cloud Pak for Data, I suggest more out-of-the-box integration, enhancement of analytics to be sharper, and the deployment options should be very flexible.

I have been using IBM Cloud Pak for Data for the past eight years, even in my previous organization.

I would add that our clients who want to address requirements alongside data catalog, data governance, and data visualization will benefit from investing in IBM Cloud Pak for Data.

I rate IBM Cloud Pak for Data a nine out of ten. I choose nine out of ten because this tool enables us to connect multiple data sources, ingest data, and run AI and ML algorithms. IBM Cloud Pak for Data is a very good solution for that work.

My advice to others looking into using IBM Cloud Pak for Data is that it has played a huge role in automating our end-to-end life cycles in the network dimension, giving us an extensive view of data across diverse data sources for required features at any moment without needing to migrate to a central repository. The platform has made our data management across multiple clouds much easier, thereby saving a lot of time. I find it to be a very commendable tool for that work.

IBM Cloud Pak for Data has enabled us to access diffuse data quickly across hybrid networks and given our teams an edge in data management through automation while adhering to compliance regulations. Additionally, it is a speedy solution, increasing our client satisfaction. I would like to note that my overall rating for IBM Cloud Pak for Data is nine out of ten.

IBM Cloud Pak for Data is a powerful cloud-native all-in-one easy-to-use solution that enables us to put data to work quickly and effectively. These tools enable us to approach analytics our way with code, low-code, and no-code options that allow us to collaborate on one platform. It is easy to transform structured and unstructured data into analytics insights where we are able to make data-driven decisions easily. We can build and test models with best-in-class AI and analytics. The support team is generally the most proactive and supportive 24/7.

A quick specific example of how I use IBM Cloud Pak for Data in my day-to-day work is that, unlike other analytics tools, it provides out-of-the-box privacy, model interpretability, and fairness monitoring, along with automatic explanation of data and models written in business language. It is a great tool that all businesses should emulate while it provides a great user experience because every feature is functional and improved constantly.

IBM Cloud Pak for Data is driving our business productivity by reducing time spent reading and analyzing data. We always use this tool in all departments that need to gather relevant information in the cloud from a single centralized point for better reporting of data and data-driven decisions.

IBM Cloud Pak for Data has positively impacted my organization by rapidly providing business-ready data to all users equally, hence being able to make data-driven decisions easily. We can manage and analyze data no matter where it is. The manual catalog has been eliminated to save cost. It drives responsible, transparent, and explainable AI workflows to operationalize AI and mitigate risk and regulatory compliance.

A specific example that shows how IBM Cloud Pak for Data has helped my organization is that it has improved our decision-making, saved us money, and saves a lot of time by predicting outcomes faster using a platform built with data fabric architecture.

The best features IBM Cloud Pak for Data offers include robust data visualization.

The data visualization feature stands out for me because it increases our impact by combining BI skills with advanced analytics and machine learning in an easy-to-use visual interface.

IBM Cloud Pak for Data can be improved because processing speeds are sometimes slow.

The slowness in IBM Cloud Pak for Data is mainly during data processing, and pricing is something that affects most small businesses, so it should be affordable. It takes some time, something that affects most small businesses and enterprises.

Occasionally, IBM Cloud Pak for Data is very slow.

I have been using IBM Cloud Pak for Data for the past three years.

IBM Cloud Pak for Data is very stable.

IBM Cloud Pak for Data's scalability is very good; it can be used by any size of organization.

The customer support for IBM Cloud Pak for Data is great and responsive.

I would rate the customer support for IBM Cloud Pak for Data a nine out of ten.

We switched from Azure Databricks to IBM Cloud Pak for Data because IBM is very easy to use. It has a flexible way to deploy and enables us to collect, connect, catalog, transform, and analyze data regardless of the area.

I have seen a return on investment because the manual catalog is eliminated, hence saving a lot of cost. It is easy to collect, organize, and analyze data no matter where it is, hence being able to make data-driven decisions.

My experience with pricing, setup cost, and licensing has been around on-premises.

Before choosing IBM Cloud Pak for Data, I evaluated other options such as Cloudera Data Platform.

I advise others looking into using IBM Cloud Pak for Data to know that, unlike other analytics tools, it provides out-of-the-box privacy, model interpretability, and fairness monitoring, along with automatic explanation of data and models written in business language and a great user experience because every feature is functional and improved consistently. I would rate this product a nine out of ten overall.

IBM Cloud Pak for Data is a powerful cloud-native, all-in-one, easy-to-use solution that enables us to put data to work quickly and effectively. This tool enables us to approach analytics our way with code, low-code, and no-code options that allow us to collaborate on one platform easily.

It is easy to transform structured and unstructured data into analytics insights, where we use those insights to make data-driven decisions easily. We build and test models with best-in-class AI analytics.

As a team, we connect IBM Cloud Pak for Data with our product for integration with cloud, and it enables all of our data users to collaborate from a single, unified interface that supports many services that are designed to work together. This helps to give our users recommendations as they want to store the data in the cloud.

Regarding my main use case, it is a very great tool that enables our users to collaborate by using single data that is stored in a centralized, unified place where they can access it at any time. It also helps to drive business productivity by reducing the time spent reading and analyzing data. We use this tool in all departments that need to gather relevant information in the cloud from a single centralized platform for better reporting of data. It is possible to analyze data from many sources in a short period of time.

The best features IBM Cloud Pak for Data offers include robust data visualization, centralized data analytics, data reliability, and compatibility with hybrid and multi-cloud environments.

The compatibility with hybrid and multi-cloud environments has helped our organization as data visualization is very simple. Migrations, reading, analysis, and data management from other sources are performed without problems of requirements. We have a team of experts in IBM Cloud Pak for Data to maintain security and correct data management easily.

I find this cloud excellent for visualizing and managing data across networks and also fulfilling fastest data storage, making it less complex and completely improving productivity in my organization. Everything is managed in multiple environments without any problem.

IBM Cloud Pak for Data has positively impacted my organization, and I have noticed some improvement since we started using this tool. Configuration with hybrid and multi-cloud environments has been very seamless and easy. It is a robust platform capable of working with multiple data sources where we gain insights to make data-driven decisions easily. It automates data analysis for quick and better performance. We have seen improvements in analysis and data correction from multiple sources. Our productivity in the company is growing, thanks to the data analysis team. We have also seen a robust hybrid and multi-cloud access system working seamlessly.

I can share specific outcomes that show how productivity has grown and how performance has improved since the data is automated, and the analysis is done much faster, saving us a lot of time. We have been able to save approximately 80 percent of our time. We are not doing data analysis manually, so this relieves our data department of dealing with data. We have been relieved of a lot of duties, and now we are able to focus on other strategic tasks. Our productivity has greatly increased since we are able to make concrete and data-driven decisions easily.

Setting up the hybrid and multi-cloud environments is a long job and it takes time.

Additionally, the customer support should be more responsive and reach and respond on time.

The two main challenges that I face are setup complexity and customer support responsiveness. Customer support needs some improvement, as they are not always unresponsive, but sometimes they are not quick to respond to our queries. They should improve on that.

I have been using IBM Cloud Pak for Data for the past five years.

IBM Cloud Pak for Data is stable.

IBM Cloud Pak for Data's scalability has been great since we started using this platform. I have not noticed any downtime or lagging, especially when dealing with large data, so it is relatively very scalable.

Customer support should be more responsive and reach and respond on time.

Customer support needs some improvement, as they are not always unresponsive, but sometimes they are not quick to respond to our queries. They should improve on that.

We switched from using Azure Databricks, and the reason why we switched is that IBM Cloud Pak for Data has been very helpful and innovative because it increased our workflow and collaboration using an integrated multi-cloud platform. It also enables us to deploy in any flexible way, on-premises or cloud, which saves time and hard disk space.

Setting up the hybrid and multi-cloud environments is a long job and it takes time.

The two main challenges that I face are setup complexity and customer support responsiveness.

Regarding my experience with pricing, setup cost, and licensing, for a small organization, the price might be relatively high, but for huge enterprises such as ours, the price is relatively affordable. The setup is easy, with no complexity, and no time wasting.

I have seen a return on investment, and I can share that we save a lot of time by predicting outcomes faster using the platform built with data fabric architecture. We save at least 70 percent of our time. It has been easy to collect, organize, and analyze data no matter where it is from multiple sources. The manual catalog is eliminated to save costs by 50 to 60 percent. We have been able to drive responsible, transparent, and explainable AI workflow to operationalize AI and mitigate risk and regulatory compliance easily.

Regarding my experience with pricing, setup cost, and licensing, for a small organization, the price might be relatively high, but for huge enterprises such as ours, the price is relatively affordable.

Before choosing IBM Cloud Pak for Data, I evaluated other options, including Cloudera Data Platform.

I would advise others looking into using IBM Cloud Pak for Data that it is a very great tool that is an all-in-one, real-time data analytics solution that provides a phenomenal user experience. It increases data transparency, saves a lot of time, and it saves cost as well. It is a great tool that transforms and analyzes data regardless of the area, making it a sure-bet tool. I would rate this product a nine out of ten.

The main use case for IBM Cognos is for business intelligence and reporting.

IBM Cognos has been available for many years, and we use regular dashboarding and for producing scheduled reports and some mandatory regulatory reporting. All our departments use Cognos, and the actual Cognos reports are developed by those different teams.

IBM Cognos is very stable and has been around for many years, with many users familiar with it, making it a reliable solution for our institution. Because of our long association with Cognos, we have good pricing.

The benefits of choosing IBM Cognos, in addition to saving on cost, include having institutional knowledge about maintaining this infrastructure and enough people who have developed on Cognos in the past, which creates comfort in its use. Cognos is a reliable solution, and developer productivity is high because of the long history of development on it.

I do not know if Cognos has all the features that users are looking for since we provide it as our standard and do not maintain infrastructure for other tools.

I am actually very new to the organization and have been here for less than a year.

I would rate IBM's support at about a seven or eight out of ten because we have good support coverage owing to our long association with IBM. We are good on the support front. IBM support is very supportive, and I would rate them an eight out of ten based on our long relationship with them.

DataStage is not difficult to set up, but we had a lot of challenges in setting up IBM Cloud Pak for Data cluster on-premises. Our infrastructure team faced many challenges when they were doing it because we had to first stand up an OpenShift cluster on-premises before deploying IBM Cloud Pak for Data solution.

The setup for IBM Cloud Pak for Data is very complex, and our teams responsible for standing up the environment struggled a lot. This might also be due to the learning curve since we had not used containerized solutions in the past.

The pricing and setup cost are handled by a different procurement team. Our IT procurement team is centralized, so licensing and the actual cost of the software are taken care of by a different team altogether.

I am not sure about the main differences between IBM Cognos and some other business intelligence tools such as Tableau or Microsoft because many members of the user community have previously experienced those reporting tools before joining our college. However, due to the variety of cloud offerings, users are often able to subscribe directly without having to approach IT for reporting tools, given they have the budget.

I do not utilize Dell PowerStore or Dremio because I work for a university setting with a very simple infrastructure, where we just use Cognos and IBM DataStage.

I do not know if my organization uses AWS as a main cloud provider. We are not on the cloud in a major way and are still on-premises for most of our solutions. In fact, even IBM DataStage, we are using IBM Cloud Pak for Data version, but it is installed on-premises, and we haven't progressed much on how to migrate to the cloud yet.

I am not sure if we use AWS as a cloud provider since we do have some SaaS applications that we subscribe to, but I do not know where they are hosted. I just know we have access to the application for the user interface, and the data is pulled out using an API, but we do not know where it is hosted.

I do not utilize Cognos ad hoc reporting because I do not develop reports. We only host the Cognos infrastructure for our different user groups, and the report development is completed by them. Our infrastructure team provides the hardware, and our system engineering team provides the installation and application maintenance for Cognos.

I think some users are using the interactive dashboards feature, and there are also other tools such as Power BI and Tableau that some users automatically use. However, our IT organization only provides Cognos as an enterprise business intelligence and reporting tool. Other tools are subscribed to separately by different people.

I am not the right person to speak on the machine learning capabilities, as my responsibility is to work with different IT teams who maintain systems across the university. I connect to them using IBM DataStage to fetch their data, perform ETL activities, and load the data into an Oracle database. My team maintains the infrastructure for DataStage and Cognos, but actual development is done by other people.

I use IBM DataStage, which we call IBM Cloud Pak for Data, as we migrated from InfoSphere DataStage to IBM Cloud Pak for Data, and it is installed on-premises in our data center. IBM Cloud Pak for Data version is more or less a modern OpenShift cluster-based platform.

The best features of IBM Cloud Pak for Data include a very modern approach to providing data capabilities under one umbrella, with various services such as artificial intelligence and machine learning capabilities, real-time integration, and data virtualization, though each has separate licenses associated with them. We are currently only using the DataStage license.

We have not evaluated data virtualization, but I recognize it as a good capability for exploring and experimenting with data, especially for those unfamiliar with data modeling. However, we are not using it due to cost considerations.

The developer productivity for DataStage on IBM Cloud Pak for Data is the same as on the old tool, InfoSphere. It does not change anything because the core capabilities remain consistent.

Overall, I would rate Cognos a nine out of ten from a pure infrastructure stability and support perspective because we are comfortable and know what to do, considering the long-term use of Cognos.

Overall, I would rate IBM Cloud Pak for Data a nine out of ten in terms of capabilities. It mirrors the traditional InfoSphere version of DataStage with a good ETL tool that covers all features expected from such tools.

We did not purchase through a marketplace such as AWS. This is all from a long association with IBM directly through negotiations with our procurement team, as we have been a large IBM customer for many years. I would rate this review a nine out of ten overall.

My primary use case for Cloud Pak is that I am the reference Data steward for the Africa regions in the banks where I work. My main objective is to capture the reference data in Caltech or Data and ensure that people profile or QA their data.

This is due to the fact that a large percentage of data is actually reference data, not by volume, but by the number of tables. The group-approved reference data is used to assure quality and ensure people know what they have; that's my primary use case for Cloud Pak.

There's a whole bunch of stuff I really like. I love the way that I can start at a very basic level with my data management journey by capturing my policies, justifying my data, and putting them into different categories to say this is data relating to individuals, for example, or data relating to geography. Those base-level data management components, together with the reference data, can then be reused whether I want to figure out where the data is coming from—using Nantucket, for example—or checking the quality of my data.

Often, when I check the quality of my data, I might find an issue, but that data did not originate in the system where I found the issue. So, I need to use Nantucket to track back to where that data originally came from so I can fix it at the source. I love that component of Cloud Pak.

I do not do much with the machine learning or AI pieces. It is probably because I can start at a basic level with data management: policies, rules, categories, reference data, and business terms. From there, I can work my way into a more granular level, applying all of that information on top of my actual data to understand what my data looks like, where it came from, and where it went wrong, managing it throughout the cycle.

What I would love to see is an end-to-end, almost a training demo database of some sort, where one of the biggest problems with data management is demonstrated.

There are so many components to data management, and more often than not, people understand one thing really well. They may understand DataStage and how to move data around, but they do not see the impact of moving data incorrectly.

They also do not see the impact of everyone understanding a piece of data in the same way. I would love Cloud Pak to come with a demo database that illustrates the different components of data management in a logical way, so I can see the whole picture instead of just the area I'm specializing in.

It would be great if Cloud Pak, from a data modeling point of view, allowed us to import our PDMs, for example. It would be ideal to import and create business terms in Cloud Pak. The PEA would be great to create the technical data. The association between the business and the technical metadata could then be automated by pulling it through from your ACE models. The data modeling component is available in Cloud Pak.

Additionally, when it comes to Cloud Pak, even though it has the NextGen DataStage built into it, there is Cloud Pak for data integration as well. Currently, I do not think we have a full enough understanding of how CP4D and CP4I can enhance each other.

I have used the solution since the end of 2021.

Scalability is endless if I can pay for it. Obviously, it is just for containers, however, I have to pay more.

The response time is quick, however, solving the problem is not always as fast. Cloud Pak is a complicated system, and it's often difficult to find the right resource in IBM to help with specific issues.

The setup was very complete and very complex.

We did the implementation with IBM.

The setup cost is very expensive. The cost depends on the pieces of the solution I'm using, how much data I have, and whether it's on the cloud or on-prem.

I've looked at Talend, Calibra, Denodo, Purview, and AWS Glue. It depends on the client's maturity in data management. If the client is only looking to do data quality as a small piece of data management, Denodo would be an excellent choice. If they are looking for end-to-end data management and have the technical resources to get Cloud Pak running and enabled with all functionalities, then definitely Cloud Pak. The choice depends on the maturity of the company.

Cloud Pak is a very, very, very good system. I'm super impressed with it. The learning curve is high, but I gain so much when I finally figure it out.

Overall product rating: seven out of ten.

IBM Watson Catalog and data pipelines are the most valuable features of the solution.

Previously, we used to extract the information in the DSX and the XML formats. IBM Cloud Pak for Data exports information mostly on the ISX, which is an encrypted format. The only challenge with the tool is the metadata queries we try to understand.

We have to go with the lineage and other packages that come with IBM. Previously, we created our own reports depending on the existing command line export of the mappings. The solution's catalog searching or map search needs to be improved.

I have been using IBM Cloud Pak for Data for two years.

We usually recommend the solution for medium and large-scale organizations.

My current organization is a Gold Partner with IBM. Whenever we reach out to the support team, the turnaround time is about 24 to 48 hours, which is pretty decent.

I rate the solution’s technical support an eight to nine out of ten.

The solution’s initial setup is easy.

The solution's pricing is competitive with that of other vendors. The pricing also depends on the number of users.

If people are with the existing stuff, I would definitely suggest they go with IBM Cloud Pak for Data. I usually recommend the solution for the financial sector, where I worked for about ten years. I worked with IBM for almost eight years. Unless they want to migrate to a new product completely, I recommend IBM Cloud Pak for Data to explore current business. It is easy to integrate the tool with other solutions.

Except for metadata queries, metadata validations, and metadata integrations, I don't see any issues with the solution. I would recommend the solution to other users if it supports their existing infrastructure.

Some people don't want to put their data in the cloud because they are concerned about how the data is secured with encryption and decryption. For such cases, we have listed out all the pros and cons of the solution to suggest them to users.

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