At a glance
The Snowflake AI Data Cloud is a cloud-based data platform that helps companies to efficiently store, manage, and analyze structured and unstructured data.
Snowflake impresses with its extraordinary performance, scalability and elasticity.
Salesforce is the leader in CRM, but reaches its limits when it comes to large data sets, for example in the context of AI.
The integration of Salesforce and Snowflake enables the analysis of consistent and complex data in real time.
Sales, marketing and service can use the data more comprehensively, gain deeper insights and make well-founded decisions.
Introduction
Artificial intelligence (AI) is enriching more and more IT applications, devices and processes – in both private and business life. New functions are added every day. As recently as fall 2024, for example, Salesforce introduced Agentforce introduced its AI agents. The autonomous, individual AI assistants have since complemented all Salesforce solutions and that means: a lot of data is needed!
This data forms the basis for well-founded decisions and targeted action. Those who have access to comprehensive, consistent and up-to-date data can react more quickly to market and customer requirements. And thus have a decisive competitive advantage.
For many companies Salesforce is a central point of contact in this context. The CRM system is considered the world leader when it comes to recording and maintaining customer data. However, it reaches its limits as soon as huge amounts of data need to be analyzed in real time, merged or used beyond the traditional CRM framework.
This is where data platforms such as Snowflake come into play. They allow large data sets from different sources to be stored and analyzed.
What is Snowflake?
Snowflake is the cloud-based data platform from the software-as-a-service provider of the same name. The solution for data management and analysis is operated entirely by Snowflake. This means that companies do not have to set up or maintain their own complex infrastructure.
Snowflake‘s AI Data Cloud combines the functionalities of a data warehouse and a data lake in a single platform. It is designed to process all data - from structured data sets to semi-structured and unstructured data. What makes it special is not only how Snowflake stores data, but also the real-time analysis of complex data.
Traditional databases often have a fixed amount of memory and computing power. Snowflake, on the other hand, separates the two resources and scales them independently of each other. This enables the platform to cope with short-term peak loads without any loss of performance. Computing power and storage capacities adapt automatically to requirements.
Snowflake also offers comprehensive security mechanisms to protect sensitive information and comply with data protection regulations. A pay-as-you-go model ensures that customers only pay for the service they actually use.
Challenges of Salesforce data management
Anyone who uses the CRM system from Salesforce knows its strengths: the structured recording of customer data, user-friendly workflows and a solid basis for customer relationship management. However, as soon as extensive data sets are needed, for example in AI applications such as Agentforce, the CRM system starts to falter.
Three typical weak points stand out in particular:
Data volume: The amount of data that Salesforce can easily process is limited.
Integration: Connecting different data sources, for example external ERP systems or unstructured data, is usually a demanding and time-consuming process.
Actuality: With very large amounts of data, the timeliness decreases. Real-time analyses come to a standstill. They deliver less meaningful results and are too slow for time-critical decisions.
To overcome these challenges, the “Bring Your Own Lake (BYOL)” principle has become established. Instead of relying exclusively on CRM‘s own functions, companies integrate additional, high-performance data platforms such as Snowflake. In this way, they can expand their database as they wish without Salesforce losing performance.
Advantages of Salesforce and Snowflake
The combination of the Salesforce Data Cloud and the Snowflake AI Data Cloud combine expertise in customer management with a powerful data platform. Together, they cover the entire data management process – from the collection and maintenance of data to its integration and analysis through to automated recommendations for action. The synergy of the two solutions is an advantage for companies.
1. Seamless data integration
Different data from CRM and ERP systems or external sources can be seamlessly merged with Salesforce and Snowflake. Instead of data silos and redundant interfaces, a central data platform is created that manages and links structured and unstructured information.
2. Zero-copy integration
Salesforce’s zero-copy integration (zero-ETL) supports seamless data integration. Bidirectional data exchange between the Salesforce Data Cloud and Snowflake means that data is immediately available without having to be physically copied or moved. This prevents redundancies or inconsistencies, reduces latency and enables meaningful insights to be gained quickly.
3. Real-time analyses
Snowflake processes data in real time and reflects changes immediately. This means that users can always work with up-to-date and accurate information. The customer journey can also be tracked live, with every interaction flowing directly into the CRM system and triggering automated processes. This creates optimized marketing touchpoints and personalized customer approaches.
4. High data quality
If you want to obtain reliable results from Salesforce, you need to evaluate consistent and correct data. Snowflake therefore offers integrated monitoring and cleansing functions that detect inconsistencies, manage versioning and ensure high data quality. At the same time, the platform helps to meet GDPR and other compliance requirements.
5. Personalized customer approach
Rough segmentation is often not enough for marketing and sales teams. They want to reach target groups with finely tuned measures. Snowflake analyzes large amounts of data and provides teams with deep insights into the behavior and preferences of their customers. Salesforce takes over the personalized approach, for example through automated workflows that dynamically serve different target groups.
6. Flexible scalability
System failures during peak loads are the worst-case scenario for any data-driven organization. It is therefore a huge advantage if the data architecture grows with the organization. The Snowflake AI Data Cloud is scalable without companies having to procure additional hardware or launch major IT projects. The volume of data and the number of users are not limited. If requirements change, the platform adapts in real time.
“A powerful data platform is the basis for more room for maneuver. It allows us to react agilely to market developments and changing customer requirements.”
Bernd Meier, Managing Director Salesfive
Use Cases: Salesforce und Snowflake im Einsatz
Die Integration von Salesforce und Snowflake hilft Unternehmen, ihre Daten umfassender zu nutzen, tiefere Einblicke zu gewinnen und datengestützte Entscheidungen in verschiedenen Geschäftsbereichen zu treffen.
Data integration with Salesforce and Snowflake: Why now?
Data analysis is no longer a static, retrospective tool. Salesforce and Snowflake show what holistic data management can look like: automated, scalable, networked and in real time.
The number of use cases for AI and machine learning will continue to increase. For companies, this means having to set up data processes that are even more performant, autonomous, faster and more precise. Investing in a professional and scalable data infrastructure now will give you a sustainable advantage over your competitors.
The integration of Salesforce and Snowflake forms a solid basis for this. It reduces downtime risks, increases data quality and enables almost unlimited growth. Data-driven business processes can be created across the company, which not only increase efficiency, but also improve the understanding of customer needs.
