The new data imperative: Accelerating unification

Why now? Delivering personalization with data unification and modernization

Customers and members expect more personalization from their banks and credit unions every year. They want a stronger, personal relationship with their financial institution and the ability to engage on their own terms. As such, many financial institutions now use generative AI to deliver instant personal service in the digital context - a boon for credit unions, banks, and community banks doubling down on relationship banking.

To keep up and remain competitive, Melissa Haarmaan, VP of Strategic Accounts at Zennify, underscored the pressing need for adaptation in the financial sector, saying that “Financial institutions must embrace rapid innovation and rethink their data strategies".

Some of these strategies include going back to basics, such as:

• Assessing your existing data architecture
• Identifying areas to modernize to leverage AI and ML effectively
• Understanding where your organization is on the data modernization maturity curve

The foundation of future-ready data architecture

Larry Dyson, Technical Director at Zennify, highlighted the challenges posed by outdated data systems.

"Our clients frequently express frustration over the limitations of their existing data architectures, which often fail to support the agility needed for real-time analytics and AI applications”.

Therefore, modern data infrastructures must be agile, robust, and capable of supporting high-speed data operations to meet the demands of current and future technologies. One strategy is to leverage the Salesforce ecosystem - and we can start with MuleSoft.

Integrating with MuleSoft for seamless data flow

MuleSoft plays a powerful role in facilitating effective data integration. Ramya Veerubhottla, Senior Manager, Channel Architect at MuleSoft, elaborated on the integration challenges that financial institutions face.

"Integrating disparate data systems is a major hurdle for almost 95% of decision-makers in the sector. Our aim at MuleSoft is to streamline this process through our robust API-led connectivity solutions, making data accessible and actionable for AI systems," she explained.

Veerubhottla also detailed how MuleSoft’s tools help orchestrate data flows across multiple platforms, enhancing operational efficiency and enabling real-time analytics.

Unifying data and improving customer experience with Salesforce

Teresa Silva-Torres, a Senior Solutions Engineer at Salesforce, spoke about the transformative impact of Salesforce’s Customer 360 initiative on the financial services industry.

"This initiative is about more than just understanding who your customers are—it's about delivering a consistently personalized experience across all platforms. Unified data is the backbone of this effort, allowing us to tailor services and predict customer needs effectively," Silva-Torres commented.

Overcoming data strategy challenges

The webinar also tackled the practical challenges of implementing a unified data strategy, such as:

• Data quality
• Compatibility with legacy systems
• Maintaining data privacy and security

Teresa Silva-Torres offered insights into best practices for data integration, emphasizing the importance of establishing rigorous data governance frameworks to ensure data integrity and compliance with regulatory standards. Here’s where Salesforce Customer Data Cloud comes in:

Stage 1: Data Sources

Customer data might be trapped in different places - in the CRM, in cloud storage, or locked in web and mobile apps. It might already be stored on Snowflake or another data lake. If a bank or credit union were to improve their customer personalization, they will need to get all of that data into one place. 

Stage 2: Connect

Through out-of-the-box connectors, APIs, and Mulesoft, Salesforce Data Cloud can connect and ingest data from all those third-party data sources. With Zero-Copy ETL, data from data lakes can be ingested without copying it over, preserving its security. At this point, there are massive data stacks that have been compiled, but more needs to be done. 

Stage 3: Prepare

For all this data to be usable, it needs to be Prepared. This is where data records are transformed to normalize and group the data. 

Stage 4: Harmonization

This is where all that prepared data and Salesforce “speak the same language”. For example, if one data source calls users subscribers and another calls them contacts, here is where we’ll map all of these to a single field in Salesforce. By mapping all this data to Salesforce objects, you can create a canonical model that becomes the foundational source of truth for all things moving forward. 

Stage 5: Create Unified Customer Profiles

At this point, it’s recommended that you consolidate duplicate data records to create unified customer profiles.

Now that we’ve unlocked and harnessed the power of your data, we’re ready to use it with Data Cloud - bringing all of this unified data to life, in the last three segments.

Stages 6-8: Segment, Analyze, Predict, and Act

Imagine being a marketer, and being able to create hyper-personalized marketing segments with just a few clicks. Imagine having this wealth of data feeding your analytics and dashboards so that you have a more complete picture of key trends. And imagine being a banker having data-driven insights surfaced within the flow of work, giving them vital details about the person sitting across from them so that they can have better, stronger impact conversations.

We can also use that data to create AI actions or automated flows.

So imagine unified data powering AI-generated account POVs or case summaries. Imagine being able to create real-time automations so that a customer is alerted if anomalous activity is detected on their credit card. We can accomplish all of this, and so much more, now that we’ve unified your trapped data and made it actionable within the flow of work, and even within third party marketing tools and data lakes.