- Source systems and ownership
- Business definitions and quality rules
- Consumers, latency and retention needs
Technology / Data
Turn distributed data into operational clarity.
Data pipelines, quality controls, models and analytics that provide reliable context for engineering, operations and AI enabled decisions.
Service overview
Make data trustworthy, observable and useful at the point where people or systems must act.
Data pipelines, quality controls, models and analytics that provide reliable context for engineering, operations and AI enabled decisions.
- Service model
- Use focused specialist support for a defined gap, or assemble a multidisciplinary team around a complete workstream. The structure follows the interfaces and decisions in the scope.
- Delivery
- Choose a project, dedicated team, hybrid engagement or managed service. Milestones, client responsibilities and acceptance criteria are agreed before execution.
- Control
- Scope, assumptions, evidence, ownership and review rhythm remain visible. Changes and unresolved risks are recorded with an accountable decision owner.
- ETL/ELT
- Streaming
- APIs
- Pipeline and lineage definition
- Quality and reconciliation evidence
- Data product operating ownership
- AI and automation
- Digital platforms
- Digital twins
Lifecycle positionInventory
Sources, owners, quality and consumers.
Core capabilities
Capabilities included.
Each capability explains the work itself, how it connects with the surrounding product or operating system, and the evidence needed to make the result usable.
Data ingestion
Batch, streaming and API acquisition across operational and technical sources.
Practical coverage
Preserve source context and manage arrival failures.
Transformation
Clean, normalize, enrich and structure data for defined consumers.
Practical coverage
Make each transformation testable and traceable.
Data quality
Rules, profiling, thresholds and accountable exception handling.
Practical coverage
Turn data problems into visible operational work.
Data modelling
Business and technical models that create consistent meaning.
Practical coverage
Align teams around common entities, measures and relationships.
Analytics
Operational metrics, trends, root cause views and decision support.
Practical coverage
Move from reporting activity to explaining performance.
Data operations
Monitor freshness, failures, lineage, performance and change impact.
Practical coverage
Operate the data capability as a production service.
Solutions
Solutions for common delivery needs.
The starting point can be a technical problem, a capacity gap or the need for a complete delivery workstream. CX24 first clarifies the current state, desired outcome and decision ownership.
Reports disagree because sources and definitions differ.
How CX24 respondsSources, owners, quality and consumers. The scope, responsible owners and evidence needed for closure are agreed before execution begins.
Data quality issues are discovered only after downstream use.
How CX24 respondsMeaning, grain and relationships. The scope, responsible owners and evidence needed for closure are agreed before execution begins.
Engineering or operations needs near real time visibility.
How CX24 respondsPipelines, transformations and quality controls. The scope, responsible owners and evidence needed for closure are agreed before execution begins.
AI development is blocked by unreliable or undocumented data.
How CX24 respondsAnalytics and machine ready data products. The scope, responsible owners and evidence needed for closure are agreed before execution begins.
Delivery approach
A clear path from scope to evidence.
The exact gates change by service, but ownership, review and measurable outputs stay explicit. Each stage establishes the information needed to enter the next one responsibly.
Inventory
Sources, owners, quality and consumers.
Stage outcomeSource and lineage map is prepared or updated before the work advances.Model
Meaning, grain and relationships.
Stage outcomeData pipelines is prepared or updated before the work advances.Engineer
Pipelines, transformations and quality controls.
Stage outcomeQuality framework is prepared or updated before the work advances.Serve
Analytics and machine ready data products.
Stage outcomeAnalytics product is prepared or updated before the work advances.Observe
Reliability, lineage, quality and value.
Stage outcomeAgreed next stage evidence is prepared or updated before the work advances.What you receive
Typical deliverables.
Deliverables are adapted to the client environment and agreed acceptance criteria. The aim is to leave behind usable engineering, technology or operating capability, not presentation material alone.
Source and lineage map
Ownership, flow, transformations and dependencies.
Assumptions, ownership, dependencies and the agreed review status are recorded with the output.Data pipelines
Versioned ingestion, transformation and serving.
Working files, source information and revision status are organised so the client team can continue using them.Quality framework
Checks, thresholds, exceptions and remediation.
Results include the relevant checks, open issues, limitations and approval evidence, not only the final conclusion.Analytics product
Measures, interfaces and documented interpretation.
Final handover identifies accepted scope, residual risk, next actions and the accountable owner for each action.Technology and methods
ETL/ELTStreamingAPIsSQLPythonData qualityWarehouse/lakehouseBI and operational analytics
Evidence clients can review before acceptance.
Proof is defined through the engagement itself: accepted outputs, a visible decision trail, agreed measures and a usable handover.
Accepted outputs
Deliverables are mapped to agreed criteria, version status, accountable owners and review decisions.
Decision trail
Inputs, assumptions, interfaces, changes, exceptions and approvals remain connected to the work.
Delivery measures
Progress, quality, risk, backlog or service measures are selected for the actual engagement.
Usable continuity
Working files, source information, runbooks and handover actions leave the client able to continue.
Client names, project details and outcome claims are published only with permission. Evidence for a specific engagement is confirmed through the agreed scope, reviews and acceptance records.
Choose a delivery structure that fits the responsibility.
Team shape, governance, commercial structure and acceptance are matched to the outcome CX24 is asked to deliver.
Defined project
Bounded scope, milestones, deliverables and acceptance criteria for a specific outcome.
Dedicated team
Stable specialist capacity integrated with client leadership, standards and delivery rhythms.
Delivery centre
Multidisciplinary capacity with named governance, shared methods and transparent reporting.
Managed service
Recurring responsibility operated against controls, service levels and improvement measures.
Build · Enable · Operate