How Salesforce Data Cloud Works: Unlocking Customer Insights

Salesforce has just bet $8B that clean data is the future of AI Early this year, Salesforce announced its $8 billion acquisition of Informatica, an enterprise leader in AI-powered cloud data management. This isn’t just another big tech deal, it’s a strategic move to fortify Salesforce’s AI foundation across every layer of its platform. Informatica brings world-class capabilities in data cataloging, integration, governance, metadata management and master data management. By combining these with Salesforce’s existing stack, including Data Cloud, Agentforce, Customer360, Mulesoft and Tableau, the company is setting up a “system of understanding” for truly trustworthy enterprise AI. In regulated industries such as healthcare, financial services and the public sector, AI adoption often stalls due to compliance constraints. Informatica helps bridge that gap through full data transparency, auditability and high-caliber governance which makes it possible to deploy intelligent agents at scale safely and responsibly. Beyond empowering AI, this acquisition reflects a more disciplined M&A strategy from Salesforce, one that balances innovation with profitability. The price tag was notably modest compared to past large-scale deals like Slack and aims to drive sustainable value rather than hype. I believe that this move signals that future AI systems won’t just rely on models but it will be grounded in trusted data, context and governance. How do you think having a more trusted, governed data layer will impact AI-powered tools in your organisation? #AI #CloudComputing #FutureOfWork #DigitalTransformation #Salesforce #DataCloud #Agentforce #Customer360 

Salesforce’s Data Cloud may be the most under-discovered data tech yet. It’s not a data lake. It’s an entirely new layer in the stack, built for one purpose: activating your data. Data teams are finally spending more time on activation - executing the recommended decision in downstream systems. That’s what turns data from a cost center into a revenue accelerant. But in the old data stack, activation meant painful integrations between the lake and Slack, CRM, ERP, and every other business system. Most data teams don’t have the bandwidth or skills for that. We use Data Cloud to bring together hundreds of streams into a single data model, and activate it natively across marketing, sales, success, and analytics. Build once. Reuse everywhere. It complements, not replaces, your data lake and operational systems. If your company runs on Salesforce, Data Cloud could be your shortcut to ROI. More on Data Cloud from our engineering leader Muralidhar Krishnaprasad: https://lnkd.in/eQV3vUeb Data teams - what tools are helping you get past the last-mile activation problem?

That’s how easy data pipelines should be. Most integration tools still make you fight with: ⚙️ Endless config screens 📝 Cryptic forms 🔄 Fragile pipelines But what if setting up a pipeline felt like talking to a friend? In our demo, I just told SyncApps: “Sync Salesforce contacts to BigQuery every night.” And it was done — no scripts, no chaos. Because building data pipelines shouldn’t require days of setup. It should just… work. 👉 If you could describe your dream integration in one sentence, what would it be? #DataIntegration #AI #iPaaS #Automation #SaaS #DataEngineering #AIProducts

Oodles of data but little insight and no action?? Read how IBM and Salesforce are using an Iceberg to bring data to Data Cloud to power the Agentic era of AI CRM

AI doesn’t move the number until it moves your records, workflows, and decisions. Our new guide shows how to turn Salesforce Einstein into real outcomes—using the Trust Layer, Prompt Builder, Copilot/Agentforce, and CRM Analytics—plus a 30-day rollout and simple guardrails so security says “yes.” 👉 https://bit.ly/47WvnFa #Salesforce #Einstein #RevOps #Analytics #AIStrategy #GTM

"Why are we adding another data layer when we already have a data warehouse?" This question comes up in nearly every Data Cloud conversation I have with CDOs and CIOs leading AI transformation initiatives. After implementing Agentforce programs with dozens of enterprise customers, here's what I've learned about why Data Cloud isn't duplication—it's differentiation: The Traditional Stack Wasn't Built for Agents Your existing data warehouse excels at historical reporting and batch analytics. But when an AI agent needs to understand customer sentiment from a support case, cross-reference real-time inventory, and personalize an offer—all within a 200ms response window—traditional architectures create bottlenecks, not breakthroughs. Three Critical Capabilities That Change Everything: Unified Customer 360: Data Cloud harmonizes data across systems in real-time, creating the complete customer context agents need to be truly intelligent, not just automated. Vector-Ready Architecture: Built-in semantic search and RAG capabilities mean your agents can understand intent and context, not just execute pre-programmed workflows. Zero-Copy Data Sharing: Connect to existing warehouses without migration risk while enabling real-time agent decision-making. The ROI Reality Check: Our customers typically see 40% faster agent response times and 60% improvement in first-contact resolution when agents have access to unified, real-time customer data versus traditional siloed approaches. The question isn't whether you can afford to implement Data Cloud—it's whether you can afford to deploy agents without it. For CDOs and CIOs evaluating AI transformation: What's your biggest concern about integrating Data Cloud with your existing architecture? #AI #Agentforce #DataCloud #DigitalTransformation #Salesforce

🌎 Data Clouds have emerged as the critical foundation for enterprise AI adoption. They unify siloed systems, improve data quality, and embed governance so AI models can deliver accurate, contextual, and actionable insights. 🌐 This white paper explores four leading Data Cloud solutions-Salesforce Data Cloud, Databricks, Snowflake, and SAP Data Cloud-analyzing their strengths, limitations, and best-fit use cases. We also explore how each platform is applied across industries to enable customer experience, supply chain resilience, analytics, and AI innovation. 🍁 At Vanrish, we help organizations select, implement, and optimize the right Data Cloud to accelerate AI adoption and business transformation. Optimize Your Data Cloud with Vanrish Technology 📌 Salesforce Data Cloud -Build a real-time Customer 360, Deliver personalized experiences at scale, Integrate Agentforce AI into customer journeys 📌 Databricks Data Cloud - Deploy a Lakehouse architecture, Scale AI/ML, streaming, and unstructured data, Implement MLOps & governance with Unity Catalog 📌 Snowflake Data Cloud - Unlock scalable analytics & BI, Enable secure data sharing & collaboration, Optimize performance & cost for SQL-driven workloads 📌 SAP Data Cloud - Unify ERP, finance, and supply chain data, Drive process intelligence & compliance, Connect operations with AI-powered insights ✨ Our Promise: From customer engagement to advanced analytics, from AI innovation to operational excellence — Vanrish Technology ensures you choose the right Data Cloud and turn it into measurable #datacloud #ai #salesforce #databricks #snowflake #sap #innovation #data https://lnkd.in/eqErPp5q

🚀 Data Cloud: The New Frontier for AI + Data Choosing the right Data Cloud is critical for unlocking AI-driven value. Here’s a quick take on the four leaders shaping the market: 🔹 Salesforce Data Cloud – Best for real-time Customer 360 and personalized engagement powered by Agentforce. 🔹 Databricks Data Cloud – Ideal for Lakehouse architecture, advanced AI/ML, and governance with Unity Catalog. 🔹 Snowflake Data Cloud – Strong in scalable analytics, SQL workloads, and secure cross-organization data sharing. 🔹 SAP Data Cloud – Purpose-built to unify ERP, finance, and supply chain data, enabling process intelligence. ✨ Each has unique strengths — the key is aligning the right Data Cloud with your business goals. ✅ Our white paper "Choosing the Right Data Cloud for your AI Journey" provides a comprehensive, in-depth analysis of Salesforce, Databricks, Snowflake, and SAP Data Clouds — including their strengths, limitations, and best-fit use cases. We’d love to hear your perspective: 👉 Which Data Cloud do you see as the best fit for your organization — and why? #datacloud #data #ai #Salesforce #Databricks #Snowflake #SAP Salesforce Databricks Snowflake SAP

GTM shift from data orchestration -> context orchestration I’ve been thinking a lot today about the shift from data integration (CDPs, rETL, Zapier-style workflows) → context integration (persistence + portability of meaning across apps). CRM fields are useful for humans, but in the AI-driven GTM era we should supply agents / LLMs with richer, portable context. right now it mostly lives in silos: 1/ MCP points in the right direction, though today it’s still more about tooling control than thematic control. 2/ clay-like tools let you drop in any unstructured medium (pdf, meeting notes), get structured results and push it elsewhere.  but still fundamentally field-based. 3/ AI copilots (gong, granola) generate insights and copy, but don’t move context across systems. what’s missing in many GTM products today is the "context" interface. there’s no native a place where I can drop unstructured artifacts (product feature launch, playbook, notes, etc) and have them persist as context around an account, reusable across apps. I need to treat “context bundle” as a first-class object, not an afterthought to enrichment. the big open design question: should that interface be inside CRM (as a “context folder”) or outside CRM (a middleware layer that syncs context into whatever surface you use)? the closest thing I’ve seen to this vision is probably Octave (cc Zach Vidibor) anyone else?

Fully agree. Context is king and the main thing that matters. Except that transcripts, recordings, web scraping, enrichment etc only scratches the surface of context. What's fundamentally missing is all the context that is locked up in silos where work happen. Think chat messages with colleagues, whiteboards, tasks, docs. Only way to solve this is a converged solution (like ClickUp) that ties all of that together.