Salesforce Data Cloud Consulting
Plan, build and run Data Cloud around the decisions you need to make, with a data model, identity rules and activations your team can understand and maintain.
Most teams arrive at Data Cloud (now branded Data 360) with the same frustration: customer data sits in a CRM, a commerce platform, a marketing tool and a few warehouses, and nobody can say with confidence how many customers they have or which ones to talk to next. Data Cloud can bring that data together, but only if the use cases, data model and identity rules are designed before the connectors are switched on.
GeekMidst helps marketing, data and platform teams take Data Cloud from licence to working activation. We focus on a small number of business outcomes first, build the foundation they need, and leave you with something your own team can extend.
Who this is for
- Marketing leaders who want segments built on a unified profile rather than on one system's view of the customer.
- Platform owners and architects who need a data model, ingestion plan and governance that will hold up as more sources are added.
- Teams that have already started and are stuck on duplicate profiles, slow segments, unexpected consumption or activations that never quite match the counts in the UI.
Problems we usually help with
- Sources ingested without a clear grain or purpose, so mapping to the data model is guesswork.
- Identity resolution producing over-merged or under-merged profiles.
- Calculated insights that disagree with finance or CRM reports.
- Segments that take too long to refresh or cannot be activated where they are needed.
- No clear owner for the data model, refresh schedules or consent rules.
What we do in a Data Cloud engagement
Use-case and data scoping
We start from the decisions you want to make, then work back to the sources, fields and refresh frequencies those decisions need.
→Data model and ingestion design
Data streams, Data Lake Object to Data Model Object mapping, transforms and the grain of each source, documented before build.
→Identity resolution
Match and reconciliation rules tuned against your real data quality, with test cases for edge conditions.
→Insights and segmentation
Calculated insights for metrics such as spend or recency, and segments built on them for marketing and service use.
→Activation
Publishing segments and attributes to Marketing Cloud, advertising platforms or other supported targets (check current activation targets for your org), with checks that counts and fields arrive as expected.
→Run and govern
Job monitoring, refresh schedules, ownership and a change process so the platform stays trustworthy after go-live.
How an engagement runs
- 01
Discover
Workshops with marketing, data and IT to agree two or three priority use cases, the systems involved and what success looks like in practical terms.
- 02
Assess sources
Profile each candidate source for identifiers, completeness, duplicates and update patterns. This is where most surprises are found, so we do it before design.
- 03
Design
Produce the data model mapping, identity strategy, insight definitions, segment logic and activation targets, and review them with your team.
- 04
Build in phases
Configure streams, mappings, transforms, identity rules, insights and segments for the first use case, then repeat for the next one.
- 05
Validate
Reconcile counts at each layer, test identity outcomes against known records and confirm activated data in the destination system.
- 06
Hand over
Walk your team through the configuration, run books and monitoring, and agree who owns what from here.
What you receive
| Deliverable | What it contains |
|---|---|
| Use-case brief | Priority use cases, owners, required data and acceptance criteria |
| Source assessment | Field-level notes on identifiers, quality issues and refresh patterns per source |
| Data model mapping | Source to DLO to DMO mapping, grain decisions and transform logic |
| Identity design | Match rules, reconciliation rules and the test cases used to validate them |
| Insight and segment catalogue | Definitions, SQL or builder logic, refresh approach and intended consumers |
| Run book | Monitoring checks, refresh schedules, common failures and how to diagnose them |
What we need from you
Data Cloud projects depend on access and decisions more than on configuration effort. To keep things moving we will ask for:
- A named business owner for each use case, and someone who can make data model decisions.
- Admin access to the relevant org, ideally with a sandbox or non-production environment for early work where your setup allows it.
- Access to source system owners, sample extracts and documentation of key fields.
- Your consent and preference rules, and a view from your privacy or legal team on how they apply to unified profiles. We implement the rules; we do not provide legal advice.
Risks to plan for
- Consumption. Data Cloud usage is metered, and ingestion volume, refresh frequency and segment design all affect it. We design with this in mind, but you should check your contract and current consumption reporting rather than rely on estimates.
- Source data quality. Identity resolution cannot fix missing or shared identifiers. Some cleansing belongs upstream.
- Scope creep. Adding sources before the first use case is live is the most common reason projects stall.
Questions we hear
Do we need clean data before starting?
Can you work with Data Cloud already in place?
Which use case should come first?
Does Data Cloud replace our data warehouse?
Will our team be able to maintain it?
Planning a Data Cloud rollout?
Tell us about your sources, your first use case and where you are today, and we will suggest a sensible starting scope.
