

SAS Data Management and IBM Cloud Pak for Data compete in the data management solutions category. IBM Cloud Pak for Data holds an advantage due to user endorsements for its integration and functionality capabilities.
Features: SAS Data Management excels in ETL processes, data quality, and data governance. It provides a unified view of enterprise data and supports multiple data sources through ODBC drivers. SAS's interface is user-friendly, even for non-technical users. IBM Cloud Pak for Data offers strong containerization and data virtualization. It incorporates Watson Knowledge Catalog, enhancing data governance and AI integration. It also features components for seamless cloud transitions.
Room for Improvement: SAS Data Management faces high licensing costs and requires stronger capabilities beyond ETL and improved database connectivity. IBM Cloud Pak for Data needs enhanced machine learning lifecycle management and a more streamlined installation. Users indicate complexity in cloud transitions and infrastructure needs.
Ease of Deployment and Customer Service: SAS Data Management is primarily on-premises, with mixed feedback on customer support due to accessibility and expertise issues. IBM Cloud Pak for Data fits cloud and hybrid deployments but can be complex to implement at scale. It offers dependable customer service to address deployment challenges.
Pricing and ROI: Both solutions are costly, with SAS Data Management yielding high satisfaction in sectors like pharmaceuticals due to compliance needs. IBM Cloud Pak for Data's expenses align with its extensive capabilities, although smaller enterprises may find affordability challenging. Both promise ROI in data reliability and efficiency gains.
It is easy to collect, organize, and analyze data no matter where it is, hence being able to make data-driven decisions.
We have been able to drive responsible, transparent, and explainable AI workflow to operationalize AI and mitigate risk and regulatory compliance easily.
Cloud Pak is a complicated system, and it's often difficult to find the right resource in IBM to help with specific issues.
The customer support for IBM Cloud Pak for Data is great and responsive.
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.
The support for SAS in Brazil is not the best one, but the support in Sweden is really good, as they visit the company and work to solve the issues.
IBM Cloud Pak for Data's scalability is very good; it can be used by any size of organization.
I have not noticed any downtime or lagging, especially when dealing with large data, so it is relatively very scalable.
IBM Cloud Pak for Data can be improved because processing speeds are sometimes slow.
Setting up the hybrid and multi-cloud environments is a long job and it takes time.
To improve IBM Cloud Pak for Data, I suggest more out-of-the-box integration.
There is significant room for improvement, especially with regard to using a hybrid approach that involves both CAS and persistent storage.
The setup cost is very expensive.
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 list price is high, but the flexibility in pricing is adequate.
From my experience, SAS Data Management is an expensive tool.
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.
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.
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.
SAS Data Management stands out because of its data standardization, transformation, and verification capabilities.
The metadata management feature of SAS Data Management helps a lot; creating your data marts or data lake with good naming conventions, library conventions, and so on is very important because it allows easy queries to find the whole structure, though I think metadata governance also depends on first definitions, not only on the tool.
| Product | Mindshare (%) |
|---|---|
| IBM Cloud Pak for Data | 1.3% |
| SAS Data Management | 1.2% |
| Other | 97.5% |

| Company Size | Count |
|---|---|
| Small Business | 8 |
| Large Enterprise | 15 |
| Company Size | Count |
|---|---|
| Small Business | 7 |
| Midsize Enterprise | 1 |
| Large Enterprise | 8 |
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.
Every decision, every business move, every successful customer interaction - they all come down to high-quality, well-integrated data. If you don't have it, you don't win. SAS Data Management is an industry-leading solution built on a data quality platform that helps you improve, integrate and govern your data.
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