IT Central Station is now PeerSpot: Here's why

IBM Cloud Pak for Data OverviewUNIXBusinessApplication

IBM Cloud Pak for Data is #3 ranked solution in top Data Virtualization tools and #28 ranked solution in top Data Integration Tools. PeerSpot users give IBM Cloud Pak for Data an average rating of 8 out of 10. IBM Cloud Pak for Data is most commonly compared to Azure Data Factory: IBM Cloud Pak for Data vs Azure Data Factory. IBM Cloud Pak for Data is popular among the large enterprise segment, accounting for 74% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a computer software company, accounting for 27% of all views.
IBM Cloud Pak for Data Buyer's Guide

Download the IBM Cloud Pak for Data Buyer's Guide including reviews and more. Updated: July 2022

What is IBM Cloud Pak for Data?

IBM Cloud Pak® for Data is a fully-integrated data and AI platform that modernizes how businesses collect, organize and analyze data to infuse AI throughout their organizations. Cloud-native by design, the platform unifies market-leading services spanning the entire analytics lifecycle. From data management, DataOps, governance, business analytics and automated AI, IBM Cloud Pak for Data helps eliminate the need for costly, and often competing, point solutions while providing the information architecture you need to implement AI successfully.

Building on the streamlined hybrid-cloud foundation of Red Hat® OpenShift®, IBM Cloud Pak for Data takes advantage of the underlying resource and infrastructure optimization and management. The solution fully supports multicloud environments such as Amazon Web Services (AWS), Azure, Google Cloud, IBM Cloud™ and private cloud deployments. Find out how IBM Cloud Pak for Data can lower your total cost of ownership and accelerate innovation.

IBM Cloud Pak for Data was previously known as Cloud Pak for Data.

IBM Cloud Pak for Data Customers

Qatar Development Bank, GuideWell, Skanderborg Music Festival

IBM Cloud Pak for Data Video

IBM Cloud Pak for Data Reviews

Filter by:
Filter Reviews
Industry
Loading...
Filter Unavailable
Company Size
Loading...
Filter Unavailable
Job Level
Loading...
Filter Unavailable
Rating
Loading...
Filter Unavailable
Considered
Loading...
Filter Unavailable
Order by:
Loading...
  • Date
  • Highest Rating
  • Lowest Rating
  • Review Length
Search:
Showingreviews based on the current filters. Reset all filters
Software Consultancyy at a tech services company with 10,001+ employees
Real User
Plenty of features, multiple services available, but machine learning could improve
Pros and Cons
  • "The most valuable features of IBM Cloud Pak for Data are the Watson Studio, where we can initiate more groups and write code. Additionally, Watson Machine Learning is available with many other services, such as APIs which you can plug the machine learning models."
  • "There is a solution that is part of IBM Cloud Pak for Data called Watson OpenScale. It is used to monitor the deployed models for the quality and fairness of the results. This is one area that needs a lot of improvement."

What is our primary use case?

We have used IBM Cloud Pak for Data for machine learning and data science use cases for various customers.

What is most valuable?

The most valuable features of IBM Cloud Pak for Data are the Watson Studio, where we can initiate more groups and write code. Additionally, Watson Machine Learning is available with many other services, such as APIs which you can plug the machine learning models. Microsoft Azure has services, and you can have them be involved between any downstream applications for receiving results.

What needs improvement?

There is a solution that is part of IBM Cloud Pak for Data called Watson OpenScale. It is used to monitor the deployed models for the quality and fairness of the results. This is one area that needs a lot of improvement.  The automated machine learning models can be created based on combining those two models on the IBM Cloud. This is an area that is needing improvement.  AIOps learning is used to manage the different versions of models of the data using these same models that have been made. This needs to improve. One large hurdle we face is the AI lifecycle, this is a feature I would like to see. IBM needs to simplify the installation process and the administration process, which should be more streamlined. There should be more focus on decoupling the individual solutions to allow customer flexibility. The customer might not have all of it, but now they can disable it. The lifecycle data provides an entire range starting from collection of data, organizing, analysis and improving it. This happens in real life, where customers do not like to put all their eggs in the same basket. They need a diversified platform, so they may select IBM Cloud Pak for Data for the sorting processes, then for the machine learning, they can do it using Watson Machine Learning and Watson Studio, whereas maybe for the design innovation part, they may go for some other solution. This homogeneity, or the diversification, they should be able to achieve  All of their solutions should be made in a fashion that can be plug and play. The installation or the setup process should not be complex, and integration with other solutions should be available.  

For how long have I used the solution?

I have been using IBM Cloud Pak for Data for approximately two years.
Buyer's Guide
Denodo vs. IBM Cloud Pak for Data
July 2022
Find out what your peers are saying about Denodo vs. IBM Cloud Pak for Data and other solutions. Updated: July 2022.
610,518 professionals have used our research since 2012.

How was the initial setup?

The initial setup was a little complex. It is not that user-friendly, and it needs quite a bit of expertise to install, the installation between various different vendors is even more difficult, such as deploying it on the IBM Cloud is relatively easier than having it installed in Amazon AWS or Microsoft Azure, or similar cloud service. This is an area where a lot of improvement is required.  We have been working with IBM Cloud Pak for Data version 2.0. After two years there was 3.0, which was improved a lot. Many of the processes have been made simpler, but there is still some improvement needed. Now we are in version 4.0. 

What about the implementation team?

IBM Cloud Pak for Data was not installed by us, it was installed by IBM.

What other advice do I have?

IBM Cloud Pak for Data is a very useful tool because it has the entire gamut of tools. Starting from collecting the data from various sources using direct integration or through data virtualization and then organizing it into catalogs and applying their organization's policies or if they have other enforcement policies on top of that. We can build the model from that data because you can refine the data. There is a lot of AI inside this single solution, we could bring a lot of homogeneity into the organization. The data discovery process and the finding of the right set of data for the right problem will become much easier because it's a very good solution. I rate IBM Cloud Pak for Data a seven out of ten. The main problem that happens in machine learning projects is that people come in and then work on making the models, and then the model is deployed in production. However, I find that there is a bias in collecting the model, and the model has to be more advanced. The model which you have built, it's very much dependent on the data from which the model was built.  Most of the solutions have features for this in the new version of the model, while they don't provide support for the version of the data or the parameters of the data on which the model was built. When a new person joins the organization, or if the engineer who has worked on it has left the organization, if a new person comes in, and they don't have that reference data on which the model was built then we have to start from scratch. This is an area where many of the solutions which are on the market don't have a solution. This is where IBM should focus more on providing a solution. It is a major area that not only IBM but other vendors, should start working on to provide a solution. I understand Microsoft is working on a solution for this, and they have a new concept that they are introducing which is called Machine Teaching. If there is only a partial resolution or solution for this problem to structure data, they have to focus more on bringing new, innovative solutions.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

IBM
Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
Flag as inappropriate
PaulSorelli - PeerSpot reviewer
IBM Data & IA Technology Consultant at a tech services company with 10,001+ employees
Real User
Top 5Leaderboard
The catalog helps you implement data governance
Pros and Cons
  • "One of Cloud Pak's best features is the Watson Knowledge Catalog, which helps you implement data governance."
  • "One thing that bugs me is how much infrastructure Cloud Pak requires for the initial deployment. It doesn't allow you to start small. The smallest permitted deployment is too big. It's a huge problem that prevents us from implementing the solution in many scenarios."

What is most valuable?

One of Cloud Pak's best features is the Watson Knowledge Catalog, which helps you implement data governance. 

What needs improvement?

One thing that bugs me is how much infrastructure Cloud Pak requires for the initial deployment. It doesn't allow you to start small. The smallest permitted deployment is too big. It's a huge problem that prevents us from implementing the solution in many scenarios.

It's expected to grow, but we can't start with a large deployment. Usually, the customers go win another option, so we miss the opportunity to implement this platform.

For how long have I used the solution?

We've been using Cloud Pak for Data for around a year.`

What do I think about the stability of the solution?

Cloud Pak is stable in my experience, but I don't typically operate it. I design the platform.

How was the initial setup?

Setting up Cloud Pak is always complex. It requires a knowledge of containers and OpenShift. You also need to understand how to integrate the solution into your environment, or you can use it on a cloud, but it's not simple. It takes about one or two months if you are deploying a simple platform with only a few components.

What other advice do I have?

I rate Cloud Pak for Data eight out of 10. It's an excellent tool that offers us many valuable features, but you have to understand how the solution will affect your operational model and rethink the way you deal with data. You're not only implementing a new tool but also an operational model. 

Which deployment model are you using for this solution?

Hybrid Cloud
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Flag as inappropriate