The refrain "data is the new gold" may be overused, but the sentiment isn't untrue. Data is extremely valuable, with one notable caveat: the quality of your data matters. If you make decisions based on insufficient data, you may make incorrect choices.
Tools like Salesforce Data Cloud solve low-quality data issues, making them an increasingly vital component of business infrastructure. So, you may need to invest in a Customer Data Platform (CDP) to ensure you are extracting the maximal value from your data in the near future. The question is which solution is best for your purposes.
Our team of software implementation experts guides companies through digital transformations. Leveraging their insights, this article explores Salesforce Data Cloud, how it compares to other structures, and what makes it a viable—or ideal—solution for different scenarios.
Comparing Salesforce Data Cloud to The Top Three Alternative Data Platforms and Covering Potential Integrations
Salesforce Data Cloud, along with the following three platforms, are best-in-class solutions. Each option is a tried and true option for business applications. However, there are differences between these solutions, resulting in distinct ideal use cases. In other words, the best CDP for your unique infrastructure and operations might not be ideal for others.
To pick the solution that will yield the highest ROI, you need to familiarize yourself with each option's pros and cons. Moreover, you may want to consider integrating some of these platforms to build a tech stack that offers the best of both worlds.
Adobe Real-Time CDP (Customer Data Platform)
With a 4.4 star rating, Salesforce Data Cloud's Gartner market review score slightly edges out Adobe Real-Time CDP's 4.2 rating. However, both scores denote a high level of customer satisfaction.
The notable approval rating of both solutions positions Salesforce Data Cloud and Adobe Real-Time CDP as two of the leading contenders for many companies. However, while both platforms aim to unify customer data and enable personalized marketing, they take distinct approaches that cater to specific business needs. Let's break down their features, strengths, and differentiators.
Salesforce Data Cloud
Salesforce Data Cloud integrates seamlessly into the Salesforce ecosystem, making it the go-to option for organizations heavily reliant on Salesforce CRM, Marketing Cloud, and Service Cloud. Its primary focus is data unification to create a single source of truth for customer profiles, enabling personalized experiences across sales, marketing, and service channels.
Salesforce enables customizable experiences through predefined customer journeys powered by its AI tool, Einstein. Yet, While effective for automation, its instant, real-time activation capabilities are more limited compared to Adobe.
Overall, Salesforce Data Cloud is well-suited for CRM-driven environments where sales and marketing teams can leverage its predictive capabilities for lead scoring and campaign optimization.
Adobe Real-Time CDP
Adobe Real-Time CDP emphasizes real-time data activation and omnichannel experiences, integrating deeply with Adobe Experience Cloud. This makes it particularly effective for organizations prioritizing dynamic personalization and immediate customer engagement across email, web, push notifications, and more.
With Adobe Sensei AI, dynamic segments can be activated instantly, delivering highly customized experiences across multiple channels in real-time. This positions Adobe well for businesses needing immediate, personalized responses to customer actions.
Salesforce Data Cloud Vs. Adobe Real-Time CDP: Which Platform Should You Use
Choose Salesforce Data Cloud if:
- Your organization is deeply embedded in Salesforce tools like CRM, Marketing Cloud, and Service Cloud.
- You prioritize data unification across sales and marketing for a cohesive customer journey.
- Your campaigns rely on predictive AI insights rather than real-time activations.
Choose Adobe Real-Time CDP if:
- You need real-time activation for omnichannel campaigns.
- Your business requires advanced personalization powered by AI.
- You already use Adobe Experience Cloud for marketing and customer engagement.
Ultimately, the choice depends on your ecosystem, data strategy, and business priorities. For businesses operating across both ecosystems, leveraging Salesforce Data Cloud and Adobe Real-Time CDP together may offer the best of both worlds.
Snowflake
There's a decent amount of confusion surrounding the differences between Salesforce Data Cloud and Snowflake. These platforms serve distinct purposes, yet the lack of clarity surrounding this topic may be because these tools are best viewed as complementary rather than competing tools.
How Salesforce Data Cloud and Snowflake Differ
At their core, Salesforce Data Cloud and Snowflake differ in their primary functions and target users:
Snowflake
- Snowflake is a robust data warehouse designed for scalable and secure data storage.
- It excels in advanced analytics, reporting, and handling massive volumes of structured and semi-structured data.
- Snowflake is ideal for data scientists and engineering teams who need a high degree of flexibility for data transformation and modeling.
Salesforce Data Cloud
- A Customer Data Platform (CDP), Data Cloud focuses on creating unified customer profiles by aggregating first-party data from multiple sources.
- It enables real-time customer engagement through marketing, sales, and service activities.
- While it is less flexible for data engineering tasks, it integrates seamlessly with Salesforce's suite, including Marketing Cloud, Sales Cloud, and Einstein AI tools.
In short, Snowflake is a data warehouse, while Salesforce Data Cloud is a customer engagement platform.
Integration Between Salesforce Data Cloud and Snowflake
Salesforce and Snowflake have embraced their complementary strengths through robust integration capabilities. The Snowflake Connector and Data Share feature introduced in Salesforce Winter '24 significantly enhances interoperability by enabling real-time data sharing between the platforms with Zero ETL (Extract, Transform, Load). This integration eliminates the delays and complexity of traditional batch processing, empowering businesses to:
- Feed Salesforce Einstein AI with up-to-date data directly from Snowflake for smarter recommendations.
- Seamlessly analyze and activate transactional data stored in Snowflake alongside unified profiles in Data Cloud.
- Achieve bi-directional data access through upcoming features like Bring-Your-Own-Lake (BYOL) Data Federation.
When to Choose Salesforce Data Cloud, Snowflake, or Both
Salesforce Data Cloud is ideal for companies seeking real-time, actionable customer insights. On the other hand, Snowflake is better suited for organizations that need a powerful data warehouse for analytics and large-scale data engineering.
Combining these tools offers enterprise businesses the best of both worlds: the ability to store, analyze, and activate data seamlessly. Companies that opt for both solutions are able to scale their data strategy more effectively, creating a competitive advantage in customer engagement and operational efficiency.
Databricks
Much like the compatibility of Snowflake and Data Cloud, Databricks and Salesforce are best used in tandem. The recent partnership between the two platforms has further enhanced the integration potential of both solutions.
Databricks excels as a unified platform for data engineering, machine learning (ML), and advanced analytics, positioning itself as a leader in the data lakehouse architecture. Combining the best features of data lakes and warehouses, Databricks enables organizations to handle structured, semi-structured, and unstructured data at scale.
The Databricks and Salesforce partnership revolutionizes data sharing and integration with Salesforce Data Cloud, introducing Zero-ETL data sharing to eliminate complex processes and enable seamless, low-latency data access. The Bring Your Own Lake (BYOL) feature allows real-time, bi-directional data federation, enriching Salesforce customer profiles with external data from Databricks. Businesses can also train AI models in Databricks using unified Salesforce data and deploy them back into Salesforce to enhance marketing, sales, and service automation.
This collaboration benefits industries like banking, where CRM data and market trends inform predictive cross-selling models; automotive, enabling hyper-personalized recommendations from purchase history and online activity; and gaming, where in-game telemetry and CRM data optimize engagement strategies. Together, these platforms streamline data sharing and empower advanced analytics and AI-driven insights across sectors.
Should You Integrate Databricks With Salesforce Data Cloud?
Databricks integrations are ideal for organizations that:
- Prioritize advanced analytics and AI/ML workloads.
- Require lakehouse architecture to unify data lakes and warehouses.
- Seek real-time data processing for complex transformations and predictive analytics.
- Need flexibility in handling structured, semi-structured, and unstructured data.
With its deep integration into Salesforce Data Cloud, Databricks is now an even more compelling choice for enterprises looking to optimize their data strategies while unlocking the full potential of AI-driven decision-making.
Choosing the Right Data Solution
Salesforce Data Cloud stands out as the go-to solution for companies deeply invested in Salesforce, offering unmatched integration and functionality for sales, marketing, and service teams. However, alternatives like Snowflake, Databricks, and Adobe Real-Time CDP cater to specific needs and industries, providing viable options for organizations outside the Salesforce ecosystem.
Businesses must evaluate their existing infrastructure, budget, and goals when deciding on a data integration platform. For Salesforce-centric organizations, Data Cloud aligns with current systems and supercharges their potential, delivering insights that drive growth and efficiency.
If you want to learn more about implementing a CDP, contact our team of experts. In the meantime, learn which Salesforce AI tools require Data Cloud here.
