Salesforce Data 360 Consulting

Yes. Data 360 is the current name for what Salesforce called Data Cloud, and earlier Customer 360 and Genie. In the Agentforce 360 platform it is the System of Context: the unified, governed data layer Agentforce and the Customer 360 apps read from. In Q1 FY27 Salesforce reported Data 360 ingested 52 trillion records, up 136 percent year over year.

Both. It is a customer data platform packaged as a real-time data activation layer. The architecture and governance work is more like a data warehouse project than a CRM project, which is why a data team is the right team to lead it.

Do we need Data Cloud if we already have Snowflake or Databricks?

Sometimes. Data Cloud is best when activation into other Salesforce clouds and Agentforce is the priority. The warehouse is best for analytics and AI training. We help you decide where each layer fits.

Data Cloud is the current product name. Customer 360 was the brand. Genie was an earlier internal codename. They are the same thing.

A focused first-activation build runs 8 to 12 weeks. A full enterprise deployment with multiple data spaces, identity resolution, and warehouse integration typically takes 16 to 24 weeks.

Data Cloud bills on consumption through credits rather than a per-user licence, which catches teams out because the cost follows usage rather than headcount. The meters that matter are data ingested, rows processed during transformation and identity resolution, queries run, and profiles activated to other systems. Identity resolution is the one that surprises people, because reprocessing a large customer base repeatedly consumes far more than the initial load. Ask your account team to model your real record volumes and refresh frequency before signing, and check whether your agreement already includes credits from another Salesforce product.

Most overruns we see come from three habits. Ingesting entire source objects when a subset of fields would serve, refreshing on a schedule far tighter than anyone actually uses, and running identity resolution across the full base when an incremental pass would do. The fix is unglamorous: map each stream to the use case that justifies it, set refresh rates from real decision cadence rather than the default, and monitor credit consumption weekly for the first quarter so the trend is visible while it is still cheap to correct.

We design the data model, ingest from Sales Cloud, Service Cloud, Marketing Cloud, and the warehouse, and stand up the first activations in 8 to 12 weeks.

We design ruleset logic that actually unifies customer profiles instead of multiplying them. Match keys, exclusion rules, fuzzy matching, all documented.

We build calculated insights that match canonical metric definitions in the warehouse, so Data Cloud and the BI layer agree on the numbers.

We design segments tied to real business outcomes, then activate to Marketing Cloud, Sales Cloud, Agentforce, and external paid media.

We design the bidirectional flow between Data Cloud and Snowflake, BigQuery, or Databricks. Zero copy or replicated, depending on the use case.

Agentforce reads from Data Cloud. If the data is dirty, the agents are dirty. We make Data Cloud Agentforce-ready before agent build starts.

We have been doing data foundation work for eight years. Data Cloud is just a Salesforce-flavored CDP. The fundamentals are the same.

Most Data Cloud projects fail on identity resolution. We have done identity work in healthcare, financial services, and retail. The rules are the hard part.

Data Cloud is not the only system of record. We design clean integration between Data Cloud and Snowflake or Databricks so the warehouse stays the single source of truth.

We build Data Cloud knowing Agentforce will read from it next. The data hygiene that makes agents work is the data hygiene we install.

Data Cloud is the data layer Agentforce reads from. We build the layer before the agents.

Identity rules, metric definitions, and ownership for the unified customer profile.

Salesforce Data Cloud consulting: identity resolution, calculated insights, real-time activation, and the data hygiene that makes Agentforce actually work. Austin, TX.

Data Cloud consulting: Data Cloud is the layer Agentforce, Sales Cloud, and Marketing Cloud all pull fr...

A Salesforce admin handoff. Data Cloud is a data platform, not a CRM feature.

A black-box implementation. Every ingestion, identity rule, and calculated insight is documented.

The work concentrates in ingestion and identity resolution. These are the factors that move the effort.

Each system ingested into Data Cloud adds mapping and identity resolution.

Matching profiles across systems is harder where keys are inconsistent.

Duplicate and conflicting data takes more to reconcile into a unified profile.

Feeding the unified profile into marketing, service, and AI adds work beyond ingestion.

You want a unified customer profile across Salesforce and beyond.

You need the warehouse and pipelines built first: see Data Foundation.

Data 360 (renamed from Data Cloud in Salesforce's FY27 reorganization) is the layer Agentforce, Sales Cloud, and Marketing Cloud all pull from. If the data is dirty, every downstream Salesforce experience is dirty. We come from the data foundation side, which is the right place to start a Data Cloud project from.

Salesforce Data 360 (renamed from Data Cloud) is the layer Agentforce, Sales Cloud, and Marketing Cloud all pull from, so dirty data makes every downstream Salesforce experience dirty. Thinklytics comes from the data foundation side, which is the right place to start a Data Cloud project, and resolves identity before wiring it into the CRM.

Start with an Analytics Truth Audit. We assess your Salesforce data, define the Data Cloud architecture, and give you a 90-day implementation plan.