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.

Neutral

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.

Positive

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.

I am using the solution for spend analytics and contract analytics. I'm specifically using it to understand if there's any contract leakage. In spend analytics, I'm using it specifically for category management. I design category strategies using multiple dimensions from the spend analysis. We also use it for improvements in supplier relationship management, such as on-time payment.

The data sits in silos. DataStage allows me to connect to different data sources. It also allows me to do ETL or ELT. It has a good integration with data warehouses.

The product must improve its performance. We see typical cloud-related issues in the solution. IBM can still focus more on keeping the performance up and keeping it 100% available all the time.

I have been using the solution for more than a year.

I rate the stability a seven out of ten.

The tool is scalable. I rate the scalability an eight out of ten. More than 500 people are using the solution in our organization.

The first line of support is strong enough. I do not have to put in additional effort.

The solution is expensive. It must improve its costs.

I was not fully part of the core team or implementation. The solution is suitable for data analytics and digital transformation. People considering the solution must decide their use case before exploring the tool’s functionalities or components. Typically, people use an IT-heavy approach, adopting functionalities first and then looking for use cases. I would advise them to do the exact opposite.

Whatever we do should be in line with the business purpose and the business vision. We must be mindful of the business model and the organization's maturity to consume the insights. These technologies are targeted towards delivering the right decision-making aids. We should not make it an IT agenda.

Overall, I rate the product a nine out of ten.

The last project I worked on was for the World Bank. I was tasked with designing the architecture for an e-commerce system aimed at rural people in Mexico, enabling them to sell products. I handled all the requirements and design aspects. The main focus of the project was the implementation of artificial intelligence and effective data management. I proposed IBM Cloud Pak for Data and an update to the cloud platform. 

Unfortunately, the project was not successful in terms of booking the final customer, as the required products were quite expensive. However, I designed the entire structure and architecture. That was one of my most recent projects. Another ongoing project with Cloud Pak is working on sales proposals for various customers in Mexico. The adoption of Cloud Pak for Data in Mexico is relatively slow compared to Data Station and Information Server, which is faster.

The main focus is on projecting sales and managing product renewals in the stores. That's more or less what I'm currently involved with regarding Cloud Pak for Data.

Its data preparation capabilities are highly valuable. You need to ensure that the data is properly created and provided in the most reliable format, especially for accurate predictions. 

For instance, IBM offers numerous artificial intelligence algorithms that require the data to be in the correct form and with precise information. We need to ingest all the data into the artificial intelligence models. I believe this is one of the primary objectives of IBM Cloud Pak for Data.

There are several specific connectors that we need to use, such as the one for SAP or XML. These connectors are not fully integrated into Cloud Pak for Data. However, they are very useful in database and information services for many of the projects I've worked on. 

Right now, the product is trying to be more maturity in terms of connectors. That, I believe, is an area where Cloud Pak can improve. Obviously, they are constantly working on refining the product. We are currently on version 4.5, and I have good relationships with some people at IBM.

They are actively striving to iterate and release new versions of the product. They are also focusing on improving Information Server and Rapid Stage 5.

In future releases, it would be beneficial to have more advanced data curation features. Specifically, I'm referring to data analysis and the quality dimensions associated with it. In my experience, this aspect is not as mature in Cloud Pak for Data compared to Information Server or database information analyzer, as they have been working more extensively on these areas.

They have been more focused on developing and enhancing those specific aspects. However, based on my research and discussions with my peers at IBM, I believe these features will be included in Cloud Pak in the near future.

I have been using IBM Cloud Pak for Data for three years. The last version I worked with was 4.5.

I would rate the stability an eight out of ten. The solution have lot of issues right now.

I would rate the scalability of this solution a nine out of ten. 

The customer service and support are good. It is one of the main part of the solution.

Positive

It depends because the IT infrastructure for Cloud Pak for Data is in the cloud. The implementation process is different from traditional system deployments. So, I would say it depends. 

I come from many years of working with Data Station and Information Server, which follow a classic implementation approach. However, implementing Cloud Pak is somewhat different. The vision or the concept behind it needs to be approached differently.

It's quite expensive. Small companies cannot afford such a product. It's mainly targeted towards larger companies.

The pricing is very high. 

You need to have sufficient funds and experienced personnel because it is a highly technical solution. It's not something that can be easily implemented without proper knowledge and expertise. 

For management, I would suggest taking smaller steps and gradually adapting the product within the company. Starting with smaller projects rather than diving into a major implementation.

The solution has a lot of potential, so I would rate it a nine out of ten.