DATA ENGINEERING & INTEGRATION
Make the data behind your decisions dependable.
RedEx provides data engineering services that connect business systems and make information usable for reporting, applications and AI.
We assess your sources, design and implement data pipelines, and maintain the checks and operating processes that keep them useful as your business changes.
Reconcile definitions
Agree what important fields and measures mean.
Reduce repeated preparation
Connect and transform data for the agreed workload.
Expose quality problems
Identify missing, duplicate or inconsistent records.
Keep data maintainable
Define monitoring, ownership and recovery instructions.
WHEN THIS SERVICE FITS
When teams have data but cannot rely on the answer.
Your sales reports disagree. Teams export and reconcile spreadsheets before every review. Product records differ across systems. Or an AI workflow cannot access the current, approved information it needs.
RedEx starts with the decision or process you need to support. We identify the relevant sources, agree what the data means and define how accurate, complete and current it must be. This gives the engineering work a clear purpose and a practical way to assess success.
Service scope
What our data engineering services cover
01 /
Data assessment & architecture
Inventory the relevant sources, profile sample records and identify gaps in access, quality and ownership.
Define the target data flow, storage approach and delivery priorities around the reporting, application or AI workload.
02 /
Data integration & pipelines
Connect source systems through agreed APIs, database interfaces or file transfers.
Build the extraction, transformation and loading steps that move data to its destination, with schedules, validation and recovery behaviour defined.
03 /
Data modelling & reporting foundations
Organise data around the entities and measures your teams use, such as customers, products, orders and sales.
Agree identifiers, relationships and calculation rules so consuming reports and applications use consistent definitions.
04 /
Data quality & traceability
Check for missing values, duplicate records, invalid formats and inconsistent relationships.
Make exceptions visible, assign owners and document how source records become downstream outputs.
05 /
Data preparation for AI
Prepare the selected information for the intended AI task. For retrieval-based applications, this can include document ingestion, metadata, access controls, indexing and refresh processes.
Evaluate retrieval against representative questions before expanding the scope.
06 /
Pipeline maintenance & improvement
Monitor agreed freshness and quality checks, investigate failed runs and manage changes to source structures.
Maintain runbooks and a prioritised backlog so pipelines continue to serve the business as systems and requirements evolve.
Technology approach
Choose the architecture around the workload.
RedEx is technology agnostic. We evaluate the systems you already use, data volume, update frequency, access requirements and your team’s operating capacity before recommending an architecture.
A focused integration may be sufficient; a broader data platform needs a clear business and operational case.
01 /
Integration & transformation
Define how APIs, databases and files connect, how records are transformed, and how failed or repeated runs are handled.
02 /
Storage & access
Assess the appropriate database, warehouse, lake or lakehouse arrangement against the data and its consumers.
Include permissions, retention and operating cost in the decision.
03 /
AI retrieval
Consider keyword, semantic or combined retrieval where the task requires it.
A vector database is an implementation option, rather than the starting assumption for every AI use case.
04 /
Operation
Design scheduling, monitoring, testing and documentation alongside the pipeline.
Evaluate how another engineer will diagnose a failure or safely change a transformation.
CMS, commerce, CRM and ERP platforms may provide or consume data. The data architecture defines their interfaces and responsibilities without requiring every system to be replaced.
Delivery lifecycle
From data strategy to implementation & maintenance
Start at the stage you need. Assessment, implementation and ongoing support have separately agreed scope, deliverables and commercial terms.
01 / Strategy
Establish what the data must support
Select the workload, review sources and access, agree definitions and assess quality. Compare implementation options and identify prerequisites.
OUTPUTS
Source inventory, sample-data findings, ownership map, target architecture and prioritised delivery plan.
02 / Implementation
Build and validate the data flow
Develop mappings, transformations and pipelines. Test representative records, exceptions and recovery paths, then connect the agreed reporting, application or AI consumer.
OUTPUTS
Working pipelines, data models, validation evidence, access configuration, release plan and operating guidance.
03 / Maintenance
Keep the flow useful as systems change
Monitor the agreed data checks, investigate failures and manage changes to sources and consuming systems. Prioritise improvements against business impact and operating effort.
OUTPUTS
Monitoring and exception records, updated documentation, service reporting and an improvement backlog within the agreed support scope.
WHAT THE WORK LOOKS LIKE
Make one important Report explainable
Illustrative first scope: one sales measure from source to report.
For one agreed reporting scope, define what counts as a sale, which date determines the reporting period and how returns, cancellations and missing customer identifiers are handled. Map the required fields from their source to the report and give unresolved differences an owner.
Agree acceptance checks before building:
- A sample report reconciles with source records under the agreed rules.
- Returns and cancellations are treated consistently.
- Missing or invalid records are flagged for review.
- Reprocessing the same input does not duplicate the output.
- Users can see when the data was last updated.
- A failed run creates an alert with an owner and recovery instructions.
Additional sources, historical backfills, real-time updates and further reports can be scoped after the first flow is validated.
What the assessment delivers
An inventory of relevant sources and access owners, sample-data quality findings, agreed business definitions, integration dependencies and implementation options. The assessment identifies what is ready to build, what needs remediation and who must resolve each prerequisite.
Technical and operating considerations
Agree source access, permitted uses, retention, refresh frequency and expected volumes. Define how schema changes, failed runs, late records and historical corrections will be handled. Document monitoring, recovery and responsibilities across RedEx, your team and third-party providers.
SELECTED PROJECTS
Explore the work behind the approach.
BUYING QUESTIONS
Before you commit.
What do data engineering services include?
Data engineering services cover the design, implementation and operation of systems that collect, transform and deliver data. A RedEx engagement may include source assessment, architecture, integration pipelines, data modelling, quality checks and maintenance, depending on the workload you need to support.
Do we need a new data platform?
Not necessarily. An existing database, reporting environment or application integration may support the requirement. We assess the current setup and compare improving it with introducing a new platform, including the cost and effort of ongoing operation.
How do you know when your data is ready for AI?
AI readiness for data is assessed across four dimensions: quality, completeness, governance, and lineage. Quality measures the accuracy and consistency of data values. Completeness measures whether the data covers the time periods, entities, and attributes the AI model needs. Governance measures whether there are controls ensuring the data remains accurate over time. Lineage measures whether you can trace every data point back to its source for audit and debugging purposes.
Most organisations score well on completeness (they have large volumes of data) and poorly on quality, governance, and lineage. RedEx’s Data Readiness Assessment scores your data across all four dimensions and identifies which AI use cases can proceed immediately and which require remediation work first. The assessment takes two weeks and produces a written report your CTO and business sponsors can review together.
Do we need a data lake or vector database?
That depends on what you need to store and how it will be used. A data lake may suit some large or varied datasets; a vector database may support similarity-based retrieval. Neither is a universal requirement. We choose the storage and retrieval approach around the workload, existing environment and operating needs.
What is the ROI of fixing data quality before AI implementation?
The ROI calculation has two components. The direct saving is the elimination of the $12.9 million average annual cost that Gartner attributes to poor data quality: duplicate processing, incorrect decisions, rework, and regulatory penalties.
The indirect return is the AI programs that succeed rather than stalling in pilot: McKinsey’s research shows that organisations with high-quality data infrastructure are 23 times more likely to acquire customers and 19 times more likely to be profitable than those without.
In practice, most RedEx data quality engagements identify two to three AI use cases that can be deployed within 90 days using data that already meets quality thresholds, generating immediate return while the broader data infrastructure program runs in parallel.
What is the difference between a data warehouse and a data lakehouse?
A data warehouse stores structured data in predefined schemas optimised for SQL queries and traditional business intelligence. It excels at reporting on known questions but is expensive to change when business questions evolve.
A data lakehouse combines the low-cost storage and flexibility of a data lake with the performance and governance features of a data warehouse: it can store structured, semi-structured, and unstructured data, supports both SQL analytics and machine learning workloads, and allows schema changes without full table rebuilds.
For organisations building AI infrastructure in 2026, the data lakehouse is typically the right target architecture because it supports the variety of data types that modern AI models require without forcing a choice between analytics performance and ML flexibility. RedEx recommends the right architecture based on your specific workloads, not the most recently marketed platform.
What data engineering work is required before a company can begin its AI digital transformation program?
At minimum, four data infrastructure elements must be in place before an AI program can deliver reliable production results.
First, data quality monitoring: automated checks that flag data anomalies before they reach the model.
Second, data lineage documentation: the ability to trace every value in a training dataset back to its source.
Third, a feature store or data model designed for the specific AI use case, not repurposed from a reporting model.
Fourth, a governance framework that defines who can access, modify, and consume the data the AI system depends on.
Organisations that attempt AI deployment without these four elements typically produce models that perform well in testing and fail in production, which is the definition of the pilot purgatory that prevents AI programs from scaling. RedEx builds all four as part of every AI/ML data infrastructure engagement.
How long does a data platform modernisation or migration take?
A focused data platform build for a single domain, for example a sales analytics platform or an IoT data pipeline, typically takes 8 to 12 weeks from assessment to production. A full enterprise data platform modernisation covering multiple source systems, business domains, and AI use cases typically takes 4 to 9 months.
The variable that most affects timeline is source system complexity: organisations with well-documented modern source systems move faster than those with undocumented legacy systems and proprietary data formats.
Scope depends on the number and condition of sources, access constraints, transformation complexity, historical data, update frequency and acceptance requirements. An assessment helps define a first delivery scope and identify dependencies before a schedule and price are agreed.
Who owns data quality and what happens after launch?
Business owners define what records and measures mean and resolve issues that require source corrections. Engineering teams implement the agreed checks, transformations and monitoring. RedEx can maintain the pipelines and support improvements under a defined agreement, with escalation and recovery responsibilities made clear.
Bring the report, integration or AI workflow your data needs to support.
Tell us which decision or process is affected, where the information comes from and what your team currently does to make it usable. RedEx can help assess the problem, implement the data flow and maintain it after launch.
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