Data Cloud

Hi @Abhishek Sharma,

Data Cloud is so much more than a Data Storage option. A bit about data cloud:

1: Data Cloud is a Consumption-based product, and ultimately that's a good thing. In the past, rather than having to pay mostly for licenses that were utilized heavily by some and hardly at all by others, this model is tied to the value you receive from the product. So as you're looking at Data Cloud consider: "How will I find value in the data I'm bringing into the platform?" And educate yourself on the way in which Data Cloud charges data services credits for the various usage types: https://lnkd.in/gxKRvnP7 

2: Unified Data by itself is not a use case. It's a driver of use cases. Implementing Data Cloud without thinking about how to actually make use of your data is like buying a sports car without knowing how to drive. Make sure you have practical, achievable, and valuable use cases in mind as you think about your potential implementation, and make sure your Account Executives and Solution Engineers can validate the use cases are pressure-tested before moving forward with your implementation. 

3: Data Cloud has real-time capabilities, but is not a replacement for all real-time, transactional integrations between platforms, especially when there is no use case for unified data. Simply said, "if it ain't broke don't fix it", and that goes for order confirmations and other simple transactional stuff going from one platform (Commerce) to another (Marketing Cloud). 

4: Remember your 5Vs of data when planning what to ingest and how to ingest it into Data Cloud:

Volume & Variety: How many systems / objects / fields / rows are needed for my use case? 

Veracity: Is the data good quality and ready for harmonization? 

Value: Does the data support an actual business outcome & KPI? 

Velocity: How do my use cases drive how quickly I need my data? 

5: The majority of Data in Data Cloud comes from outside of Salesforce, and if you don't harmonize your data, your insights will suffer. Data Cloud uses a highly normalized data model, based on the Salesforce Customer 360 model and that means you must invest in understanding this model before mapping your data. This is easy for Salesforce sources which come pre-mapped, but for other sources you'll need to understand (and validate) how your data maps to it in order to take advantage of tools like Calculated Insights, Identity Resolution, and Segmentation. 

6: Data Cloud can transform data and apply formulas, but it should not be used as a method for "fixing" bad data. It's mostly a system of reference from your connected systems of record, meaning an accurate representation of the data within them is important. "Fixing" through manipulation in Data Cloud sweeps data cleanliness issues under the rug at best and creates an unreliable source of reference at worst.