Salesforce Data Cloud vs. SAP Data Intelligence

I would recommend Salesforce Data Cloud as an 8 because it is great for keeping track of customer and sale information, it is easy to use, it is easy to generate reports and it is user friendly. However, it is very expensive and does not integrate with other CRMs well

Incentivized

If you have an SAP products ecosystem in your IT landscape, it becomes a no-brainer to go ahead with an SAP Data Intelligence product for your data orchestration, data management, and advanced data analytics needs, such as data preparation for your AI/ML processes. It provides a seamless integration with other SAP products.

Incentivized

• Integration with other systems is really working for us. Also having models developed in Data Cloud and then use for servers, Salesforce sales and for Marketing Cloud.

Incentivized

• It integrates well with our current ecosystem of SAP products, like HANA.
• It provides end-to-end machine learning operations, with tools for the complete model life cycle.
• It has a simple user interface for novice users, with complex tools also available for power users.
• It builds on SAP Data Hub, providing all the ETL functions of that tool with additional machine learning functionality.
• It can run in the cloud, no on-premise software management needed.
• Many programming languages are supported, it provides a sandbox environment for the user to develop in whichever style they prefer.
• SAP is very actively developing and improving it.

Incentivized

• Sometimes it can be very fidgety and you need a team of salesforce developers in order to implement the solution
• Can be expensive, but so are other providers

Incentivized

• Data transfer speed tends to be slow when there is poor internet connection since SAP Data Intelligence don’t synchronize data while offline. However, this is not vendor fault, that’s why we have implemented robust wireless technology internet connection in our organization.

Incentivized

Allow collaborations among various personas
with insights as ratings and comments on the
datasets Reuse knowledges on the datasets for new users Next-Gen Data Management and Artificial Intelligence

Incentivized

Because it is worth this, the analytics, reporting, CRM, security parameters, mobile support all make it this worth. Also, I personally believe it has high potential to grab customers attention and increase sales.

I think the troubleshooting process might be streamlined with improved error recording and tracing. A lot of information about issues and how to fix them is hidden away in the Kubernetes pods themselves. I'm not sure whether SAP Data Intelligence can fix this problem it may be connected to Kubernetes's design, in which case fixing it could need modifications inside Kubernetes itself.

Incentivized

Initially we struggle to get help from SAP but then dedicated Dev angel was assigned to us and that simplify the overall support scenario. There is still room of improvement in documentation around SAP Data intelligence. We struggle a lot to initially understand the feature and required help around performance improvement area,

Incentivized

I like Salesforce Data Cloud because like mentioned before, it is easy to use, easy to generate reports, create workflows, keep track of customer data, sales data, house data, etc. You can also import excel sheets with customer data as well and it will create the fields for you which is nice

Incentivized

One of the reasons to pick SAP Data Intelligence is the speed and security it provides, in addition to the excellent support it provides. It is also compatible with all popular databases, which is another reason to choose it.

Incentivized

• It's been really good because for example, now we can use some of the segmentation of our clients. We have all the models running in that cloud, so it's been easier for us having this information built and having the calculation of the segments that we want to use with clients.

Incentivized

• Automation of data management slashed tasks by over 60% in most departments for the first 8 months.
• Metadata catalogs have enabled us to categorize data from disjointed sources in one place.
• It runs multiple ML models which enhances flexibility when managing data.

Incentivized