Salesforce Data 360 (formerly Data Cloud) unifies customer data into real-time profiles that power Agentforce, Marketing Cloud Next, and Einstein AI. Twopir architects and implements it the right way — across all Three Cs: Consistency, Connectivity, and Cost.
Data 360's promise is a unified customer profile — a single real-time truth set spanning every system. But how you model that data determines whether the promise holds. Unification is not a golden record: it's a dynamic, real-time representation built for activation, not governance.
Data 360 supports real-time streaming, batch ingestion, API integration, and zero-copy queries. Each has trade-offs. The question isn't just how you connect — it's why. What business value requires data to be fresh? What's the cost of it being stale?
Data 360 is consumption-based, not flat-rate. Every ingestion, transformation, segment refresh, and activation is metered. This is where most implementations go off the rails — elegant architecture, runaway budget. The crawl-walk-run approach is not optional.
Teams that model Data 360 like a data warehouse end up with bloated profiles, inefficient queries, and downstream platforms that can't use the data they need. Schema design for activation requires a different mindset — and most first implementations get this wrong.
Real-time streaming everything feels right until the invoice arrives. Teams that don't design for cost from day one find themselves throttling activations, disabling segments, or rearchitecting mid-flight — all of which costs more than getting it right initially.
Too-strict matching misses real customer connections across systems. Too-loose matching merges records that shouldn't be together. Without domain expertise and real-data testing, your unified profile is neither unified nor a profile — it's noise at scale.
Data 360 is the foundational data layer for Agentforce and Einstein AI. If the unified profile is incomplete, stale, or poorly modeled, your AI outputs reflect that. Garbage in, garbage recommendations out — regardless of how sophisticated the AI layer is.
Organizations choose zero-copy integration to avoid Data 360 storage costs and are surprised when their Snowflake or Databricks bill spikes. Zero-copy doesn't remove cost — it moves it. Architectures built without understanding this trade-off rarely stay within budget.
Agentforce agents use the unified customer profile from Data 360 to make decisions, surface context, and take actions. A properly implemented Data 360 is the difference between an agent that's genuinely useful and one that produces wrong answers because the underlying data is fragmented.
Marketing Cloud Next is built on top of Data 360. If you're moving to or already using Marketing Cloud Next, you are already using Data 360 — whether you realize it or not. The quality of your schema modeling directly determines the quality of every campaign and journey it powers.
Data 360 introduces a paradigm shift for MarTech teams: consumption-based cloud pricing, real-time orchestration, and enterprise-grade data architecture — all in one platform. It's not a CRM feature. It's a pay-as-you-go data infrastructure layer that demands architectural thinking.
Zero-copy integration with Snowflake, Databricks, and other cloud data platforms lets Data 360 query external data in place — opening activation use cases against data that would be expensive to fully ingest. But it requires careful governance to avoid cost surprises on the connected platform.
Merges identity, behavioral, transactional, and engagement data from every source system into a single dynamic profile — updated in real time as new events arrive.
Core CapabilityConfigurable deterministic and probabilistic matching rules that link records across systems — resolving the same customer across CRM, commerce, marketing, and external platforms.
Data UnificationIngests behavioral events, web interactions, and transactional data in real time — enabling personalization, journey triggers, and agent actions based on what customers are doing right now.
Real-TimeBuild audiences from unified profile data and activate them across Marketing Cloud, Advertising Studio, Tableau, or external platforms — with scheduled or real-time refresh.
ActivationQuery Snowflake, Databricks, and other cloud platforms in place without full ingestion — enabling activation against external data at a fraction of the storage cost.
IntegrationProvides the trusted data layer that grounds Agentforce agents — ensuring every agent action is based on accurate, real-time customer context rather than stale CRM fields.
AI EnablementDefine custom metrics — lifetime value, churn probability, engagement score — computed across unified profile data and surfaced as attributes for segmentation and activation.
IntelligenceMap source data to Salesforce's standard data model objects (Individual, ContactPoint, Engagement, Product) — with custom objects for industry-specific data requirements.
ArchitectureExplore unified profile data, run ad-hoc queries against your data model, and expose profile attributes to external applications via the Data Cloud Query API.
AnalyticsThe difference between a Data 360 deployment that performs and one that balloons in cost comes down to how it's designed before the first configuration is applied. Twopir runs architecture before we run setup.
We audit your existing Salesforce data landscape — which Clouds you're on, what external sources need connecting, current identity fragmentation across objects, and downstream use cases for activation including Agentforce, Marketing Cloud Next, and analytics. The output is a Data 360 architecture decision record: what gets ingested, what stays zero-copy, which integration modes apply to which sources, and a cost model for the first 12 months of operation.
We design your unified data model — mapping source system fields to Data 360 standard objects, defining identity resolution rules tuned to your actual data (not synthetic test records), and configuring survivorship logic for profile merging. This phase is where most implementations fail when done by generalist integrators; we treat it as a structured quality gate before a single record is processed in production.
We build ingestion pipelines for each source — real-time, batch, or zero-copy — with cost governance built into every connection. Segment definitions, calculated insights, and activation targets are configured and tested against real profile data. Agentforce data grounding and Marketing Cloud Next integrations are validated end-to-end before production cut-over.
We document the full data architecture, configure cost monitoring dashboards, and run enablement sessions for your CRM admins and marketing ops team. We establish a crawl-walk-run roadmap so your team knows which use cases to expand into next — and what governance checkpoints to hit before scaling consumption. You own the architecture; we've built it to be operated without depending on us.
Organizations that implement Data 360 well — with proper data modeling and cost governance — report the same set of operational improvements. These are the outcomes Twopir is built to deliver.
Every engagement starts with a data architecture assessment — identity fragmentation, source system inventory, downstream use case mapping, and a 12-month cost model. We don't configure Data 360 until we understand the exact shape of your data landscape. Generic configurations built on assumptions generate generic results and unpredictable bills.
Matching rules that pass on clean test data fail on real-world records with abbreviations, transliterations, and source system artefacts. Our sandbox validation phase runs identity resolution against a representative sample of your production data — so rules are tuned to your reality before they process a single production profile.
Consumption cost management is not a monitoring dashboard bolted on at the end. We design ingestion modes, segment refresh cadences, and activation governance into the architecture from the start. Teams we work with don't discover their Data 360 costs at renewal — they've been watching the trend since day one.
Data 360 is the data foundation for Agentforce, Marketing Cloud Next, Einstein AI, and every Salesforce product innovation going forward. We implement Data 360 with the full downstream stack in mind — not as a standalone project, but as the foundational layer that determines how well everything above it performs.
We've architected across the full spectrum of Salesforce complexity — multi-Cloud implementations, post-acquisition org merges, industry-specific data models, and orgs where data hasn't been governed in years. With 500+ deployments behind us, the patterns are familiar; the configuration is specific to you.
"Data 360 is powerful, yes. Essential, even. But simple? Not quite. It introduces a new paradigm — real-time data orchestration, consumption-based pricing, and enterprise-grade architecture that most internal teams haven't operated before. The teams that succeed treat it as a strategic shift, not a feature rollout. The Three Cs — Consistency, Connectivity, Cost — are where we spend our time before any configuration happens."
Start with a data architecture review. We'll map your current data landscape, identify the gaps in your unified profile, and show you exactly what a well-implemented Data 360 would unlock — for Agentforce, Marketing Cloud Next, and beyond.
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