Data & AI
Data Cloud
Cloud23 implements Salesforce Data Cloud to unify customer data from core systems, channels and CRM into real-time profiles that power personalisation, analytics and AI.
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Data & Analytics Consulting
Cloud23 provides data and analytics services for Salesforce-centred organisations — data strategy, single-customer-view architecture, Data Cloud implementation, dashboards and the data foundations that AI initiatives depend on.
Data and analytics is the practice of turning operational data into trustworthy, decision-ready information — spanning data strategy, architecture, integration, quality management, reporting and, increasingly, the foundations for AI. Cloud23 provides this for Salesforce-centred organisations: designing the single customer view, implementing Salesforce Data Cloud where it fits, building reporting and dashboards, and preparing data estates for AI capabilities such as Agentforce.
Salesforce Data Cloud is Salesforce's data platform for unifying customer data from any source into a single, real-time profile that the rest of the platform — service, marketing, analytics and AI agents — can act on. It changes what is possible, but it does not change the fundamentals: analytics and AI are only as good as the data architecture, quality and governance beneath them, which is where most initiatives quietly fail.
Cloud23 anchors data work to decisions rather than infrastructure: which choices the business needs to make better, what information those choices require, and only then what architecture supplies it. With AI, data and integration as the pillars of our practice — and delivery teams spanning Salesforce, MuleSoft and AWS — we build data foundations as part of working journeys, not as warehouse projects that never meet a user.
What you get
Deliverables
Data strategy and governance framework
A data strategy tied to named business decisions, with ownership, quality standards and governance sized to your organisation rather than imported from a textbook.
Customer data architecture
The design of your single customer view: authoritative sources, matching and survivorship rules, and where Data Cloud, the CRM or a warehouse should hold what.
Data quality and migration
Profiling, cleansing, deduplication and migration of data into Salesforce — with the runbooks and reconciliation evidence that regulated migrations demand.
Reporting and dashboards
Operational and executive reporting built on the Salesforce platform and tools such as CRM Analytics and Tableau, designed around the questions each audience actually asks.
AI-readiness assessment
A concrete evaluation of whether your data can support the AI use cases you intend — coverage, quality, permissions and grounding — with a costed path to readiness.
How we work
A delivery approach built for enterprise
- 01
Discover
We start from decisions: which business questions matter, who asks them, and what data they would need to be answered well.
- 02
Assess and architect
The data landscape is audited — sources, quality, ownership, flows — and a target data architecture is designed against the decisions it must serve.
- 03
Build foundations
Quality remediation, integration pipelines and data models are built for the highest-value use case first, not the whole estate at once.
- 04
Deliver insight
Dashboards, reports and activation use cases go live with the people who will use them, tested against real decisions.
- 05
Embed and govern
Ownership, stewardship and a governance rhythm transfer to your team, so data quality is maintained rather than re-purchased every few years.
When it fits
Consider this when
- Reports from different systems disagree and no one fully trusts the numbers.
- An AI or Agentforce initiative is planned and the honest answer on data readiness is 'we are not sure'.
- Customer data is fragmented across core systems, CRM and channels, with no single reliable view.
- Regulatory and management reporting consumes days of manual effort every cycle.
- Salesforce holds rich operational data that never reaches the people making decisions.
In practice
Example engagements
- A single-customer-view programme for a bank, unifying customer data across core banking, CRM and channel systems into one governed profile.
- Data quality remediation ahead of an Agentforce deployment, so AI agents ground their answers in data the business actually trusts.
- An executive and operational dashboard suite on CRM Analytics or Tableau, replacing a monthly spreadsheet cycle.
- The data migration workstream within a Financial Services Cloud implementation, from profiling through reconciled cutover.
FAQ
Frequently asked questions
What do data and analytics services from a Salesforce partner cover?
They cover the full path from raw operational data to decisions: data strategy and governance, customer data architecture, integration and quality management, migration, reporting and dashboards, and readiness for AI capabilities such as Agentforce. A Salesforce-centred partner adds specific depth in how customer data behaves inside the platform — objects, sharing, Data Cloud and the analytics tools native to it.
What is Salesforce Data Cloud?
Salesforce Data Cloud is a data platform that ingests and harmonises customer data from Salesforce and external sources into unified, real-time customer profiles. Those profiles can then drive segmentation, personalisation, analytics and AI agents across the Salesforce platform. It is powerful, but it is an architectural decision — it needs a deliberate design for sources, identity resolution and governance, not just a licence.
What is a single customer view?
A single customer view is one reliable, consolidated representation of each customer, assembled from all the systems that hold parts of their story — accounts, products, interactions, consents. It is the foundation for decent service, cross-sell, regulatory reporting and AI alike, and in banking it is usually the single most valuable data asset a transformation can produce.
Why does AI readiness depend on data?
AI systems act on the data they are given: incomplete, duplicated or wrongly-permissioned data produces confidently wrong answers at scale. Before deploying agents or predictive models, the data they will ground on needs assessed coverage, quality, access control and lineage. That assessment is fast and cheap compared with discovering the problems in production.
Can Cloud23 work with data outside Salesforce?
Yes — most engagements involve it. Customer truth usually lives partly in core systems, warehouses and third-party sources; our integration practice on MuleSoft and our AWS capability exist precisely because the data that matters rarely sits in one platform.
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Ready to talk data & analytics?
A short conversation with our team is the fastest way to understand whether this is the right engagement for you, and what it would involve.