Over the past two posts, we explored Salesforce's new data platform - Data Cloud - and saw how it can drive transformation in financial services. By creating unified customer profiles, Data Cloud unlocks the power of connected insights to enhance risk modeling, improve advisor productivity, and more. But how can financial institutions actually get started with Data Cloud and integrate it into their tech stack? Here is our guide:

1. Identify High-Impact Use Cases

When beginning your Data Cloud journey, it's important not to get overwhelmed trying to ingest and connect every data source at once. Instead, we recommend clients prioritize 1-2 high-value use cases tied to key business objectives. This focused approach helps drive engagement and justify future expansion. Examples of common use cases include:

• 360 client insights for wealth managers to deepen relationships
• Real-time fraud detection for retail banks to reduce risk
• Customer churn predictions for insurance providers to improve retention
• Personalized cross-sell recommendations for investment firms to increase wallet share

By aligning Data Cloud to targeted outcomes like these, you can demonstrate tangible benefits and ROI as you scale.

2. Catalog Your Data Landscape

What sources contain the valuable customer data needed to enable your priority use cases? This could include:

• Core systems like lending platforms, policy admin systems, trading platforms
• Product usage data from online/mobile banking solutions
• External apps like fintech solutions and third-party data sources

Thoroughly cataloging your existing data landscape highlights which systems require integration for those high-value use cases. This inventory also aids in planning for future expansion.

3. Design Your Data Model

Next, map out the ideal future state data model needed for your use cases - what entities, attributes, associations, and hierarchies are required to power personalized insights and intelligent recommendations? For example, key entities might include Customer, Account, Policy, Product, Location and more. Relevant attributes could contain demographic, financial, behavioral and contextual data. The data model provides the foundational blueprint for Data Cloud to harmonize records from various sources into unified customer profiles.

4. Establish Data Governance

With more data being consolidated into Data Cloud, it's critical to clarify key areas upfront:

• Data ownership: Who owns which data sets and sources?
• Security protocols: What level of encryption is applied during transit and at rest? How is access authenticated?
• Access controls: What user roles can view, edit, delete data?
• Usage policies: What are appropriate vs prohibited uses of data?

Addressing these governance considerations ensures continued compliance, transparency, privacy and responsible data practices as you scale Data Cloud across your organization.

5. Start Ingesting!

With the business objectives, data sources, data model and governance foundation established, now you're ready to start activating actual data flows into Data Cloud. This can occur through:

• Batch loading: Bulk historic data synchronized on a scheduled basis
• Real-time streams: Continuous feeds of transactional data

Leverage pre-built connectors and integration tools for faster pipelines. As volumes increase over time, ensure adequate capacity for ingestion and data processing.

We Can Guide Your Data Cloud Journey

As a Salesforce partner with proven expertise in Data Cloud and a dedicated focus on financial services, Vantage Point can help you through every step of this journey - from use case design to implementation and beyond. Reach out if you want to learn more about how Data Cloud can transform your institution!

hello@vantagepoint.io