$187B
Data engineering services market by 2030
Fortune Business Insights
$302B
Data analytics market by 2030
Grand View Research
$48B
Data pipeline tools market by 2030
Grand View Research
25%
EBITDA uplift for data-driven organizations
McKinsey
Foundation
What Is AI-ready data infrastructure?
AI-ready data infrastructure is the foundational layer of data pipelines, quality controls, governance frameworks, and storage architecture that allows an organization to build, train, and operate AI systems reliably at scale. The distinction between data infrastructure and AI-ready data infrastructure is data quality: Gartner estimates the average enterprise loses $12.9 million annually to poor data quality, and Forrester identifies it as the primary factor limiting AI adoption. Most organizations have sufficient data volume for AI but insufficient data quality and lineage documentation.
Data Engineering as the Foundation of Digital Transformation: The Quality Gap Your AI Program Cannot Afford
Every enterprise has data. Few have data they can trust. The gap between collecting data and extracting value from it is where billions are lost annually through poor quality, fragmented systems, and infrastructure that wasn’t designed for the demands of AI and real-time analytics.
43%
Of COOs rank data quality as their most significant data priority
This isn’t just an IT problem. Operations leaders across industries recognize that data quality directly impacts revenue, customer experience, and operational efficiency.
IBM Institute for Business Value, 2025
$12.9M
Average annual cost of poor data quality per enterprise
Organizations hemorrhage millions through duplicate records, inconsistent formats, and stale data — before they even attempt AI. Over 25% of organizations lose more than $5M annually.
Gartner / Acceldata, 2025
64%
Of organizations cite poor data quality as their biggest challenge
Nearly 2/3 of enterprises admit data quality is their primary obstacle. Worse, 67% say they don’t trust their own data which is the very foundation their AI initiatives depend on.
Precisely, State of Data Quality 2025
12x
Data engineering talent shortfall vs. demand
With 461K open positions and only 55K qualified candidates in Q2 2025, the data engineering talent gap is among the most severe in technology, making external partnerships essential.
Industry Analysis, Q2 2025
The single biggest factor limiting digital transformation ROI is not the AI model, the strategy document, or the budget. It is the quality of the data those models are trained on. An organization that builds its transformation on untrustworthy data builds it on sand.
Why Digital Transformation Programs Stall Without AI-Ready Data Infrastructure
The organisations that invest in structured AI strategy for business before committing to technology are the ones capturing this growth.
23x
More likely to acquire customers
McKinsey Global Institute
19x
More likely to be profitable
McKinsey Global Institute
28.7%
CAGR of data analytics market to 2030
Grand View Research
20%
Performance advantage over competitors
McKinsey
Our approach
How RedEx Builds AI-Ready Data Infrastructure: Quality First, Scale Second, Governance Always
Data Accuracy
Pipeline Latency
Lower Storage Costs
AI is only as good as the data it feeds on. RedEx builds modern, scalable data platforms that ingest, clean, and govern data from across your enterprise whether it’s IoT sensors on a factory floor or transaction logs in a mainframe. We work across Snowflake, Databricks, BigQuery, Redshift, and open-source stacks because the right choice depends on your workloads, your team, and your budget.
Modern Data Lakehouse Architecture
Data Governance & Quality Automation
Real-Time Streaming Pipelines (Kafka/Flink)
Legacy Data Migration
01
Business-First Architecture
We start with your business questions. Every data model, pipeline, and dashboard is designed to answer the questions that drive revenue and reduce cost.
02
Quality Before Quantity
We fix your data quality before building analytics on top of it. Because the most sophisticated ML model in the world is worthless if it’s trained on bad data.
03
Built to Be Maintained
Every platform we build comes with documentation, monitoring, and knowledge transfer. Your team owns it on day one, not after a 6-month transition period.
- PREMIUM Case Study
How We Help
Data Engineering Services: From Legacy Infrastructure to AI-Ready Data Foundation
From fragmented data silos to unified intelligence. We design, build, and operate data platforms that turn your most underutilized asset into your most powerful competitive advantage.
Data Platform Architecture
The platform decision you make today will determine which AI use cases are possible in the next three years and which are not. RedEx designs data platform architectures against your specific workloads, team capabilities, and 5-year AI roadmap, not against vendor marketing materials.
Data Pipeline Engineering
End-to-end ETL/ELT pipeline design, development, and optimization. Real-time streaming with Kafka, batch processing with Spark, and orchestration with Airflow built for reliability at scale.
Data Governance & Quality
The most common reason AI programs stall in pilot is not the model. It is the data feeding it. RedEx implements the governance frameworks, quality monitoring, and lineage tracking that make data trustworthy before an AI system depends on it.
Analytics & Business Intelligence
From self-service dashboards to embedded analytics. We design semantic layers, build data models, and deploy visualization platforms that turn data into decisions.
AI/ML Data Infrastructure
Build the data foundation that AI actually needs: feature stores, vector databases, training pipelines, and model serving infrastructure. Strategy-aligned, not science-project-driven.
Data Migration & Modernization
Migrate from legacy data warehouses and on-premise systems to modern cloud platforms. Zero-downtime migrations with validation frameworks that ensure nothing gets lost in translation.
End-to-End Capabilities
Data consulting & implementation services
From AI strategy for operations leaders to through full-scaled platform delivery, we bring the full spectrum of skills needed to transform.
Analytics in Action
Intelligence that drives decisions
From real-time operational dashboards and predictive analytics to self-service BI and embedded intelligence, we build analytics platforms that people actually use.
Value Drivers
Why AI-Ready Data Infrastructure Outperforms Data Storage as a Business Investment
AI Readiness
The single biggest factor limiting AI adoption isn't algorithms but data quality. We build the data infrastructure that makes AI initiatives succeed instead of stalling in pilot purgatory.
- Poor data quality is the #1 factor limiting AI scaling
Revenue Intelligence
Unified customer data platforms that connect marketing, sales, and product data to reveal revenue patterns invisible in siloed systems. Real-time analytics that drive pricing, personalization, and market expansion decisions.
- Data-driven organizations are 23x more likely to acquire customers
Operational Efficiency
Automated data pipelines that eliminate manual reporting, reduce data preparation time by 80%, and enable real-time operational dashboards. From reactive reporting to predictive operations.
Risk & Compliance
Automated data lineage, quality monitoring, and compliance reporting that reduces audit preparation from weeks to hours. GDPR, CCPA, SOX, and industry-specific regulatory frameworks built into the platform.
- Data governance market growing at 20.5% CAGR to $24B by 2034
Proof of Impact
Data Engineering in Action: Client Outcomes and Industry Insights
- Thought Leadership
- Thought Leadership
The DATA Methodology
How RedEx Builds AI-Ready Data Infrastructure as Part of Your Digital Transformation Program
We deliver results in weeks, not years. First results in 4-6 weeks. Full POC in 60 days.
D
Discover
Weeks 1-2
Comprehensive data maturity assessment. Audit your current data landscape: sources, quality scores, governance gaps, and technical debt. Design a target-state architecture aligned with your AI roadmap, not technology trends.
Output
A data maturity scorecard that identifies which use cases can be deployed now and which require data remediation first.
A
Architect
Weeks 3-4
Engineer the data platform layer by layer: ingestion pipelines, transformation logic, storage optimisation, and semantic models. Each component is tested, documented, and designed for the team that will maintain it after delivery.
Output
A fully documented platform architecture with component-level specifications and a data quality framework built into every pipeline from the first sprint.
T
Transform
Weeks 5-10
Production deployment with automated quality monitoring, alerting, and self-healing pipelines. We do not hand off a platform and walk away. We ensure it runs reliably with the observability your operations team needs to identify and resolve data quality issues before they reach the AI models depending on that data.
Output
A live platform with automated quality gates, a monitoring dashboard, and a documented runbook your team can operate independently.
A
Activate
Ongoing
Expand the platform to new data domains, use cases, and business units. Build internal data engineering capability through knowledge transfer, documentation, and training.
Output
A platform roadmap for the next 12 months covering new data domains, new AI use cases, and the capability-building program that reduces dependence on external partners over time.
The Modern Data Stack Evolution
Your data architecture should match your business maturity. We help you navigate the evolution from legacy systems to modern platforms at the pace that’s right for your organization.
foundation
Data Warehouse & ETL
Structured data, batch processing, traditional BI. The starting point for most enterprises, reliable but limited in flexibility, real-time capability, and support for unstructured data.
Technologies: SQL Server, Oracle, Teradata, Informatica
modern
Data Lakehouse & Streaming
Unified storage for structured and unstructured data. Real-time streaming, ML-ready infrastructure, and cost-effective scaling. The sweet spot for most enterprise data strategies today.
Technologies: Snowflake, Databricks, BigQuery, Kafka, dbt
advanced
Data Intelligence Platform
AI-native data infrastructure with automated governance, semantic understanding, and self-service analytics. Data products as first-class citizens with embedded quality and lineage.
Technologies: Data Mesh, Feature Stores, Vector DBs, Data Products
Tech Agnostic
We navigate the data platform landscape so you don't have to
Every technology recommendation in a RedEx AI strategy engagement is validated against your specific constraints, not against a preferred vendor relationship.
We make IT simple for you.
The modern data stack is fragmenting fast. Dozens of competing platforms, overlapping capabilities, and the real risk of vendor lock-in. We help you build for flexibility and interoperability instead of betting on a single vendor's roadmap.
- Snowflake: Cloud data warehouse with near-zero maintenance and elastic scaling
- Databricks: Unified analytics platform combining data engineering, science, and ML
- Google BigQuery: Serverless data warehouse with built-in ML and geospatial analytics
- AWS Redshift: Petabyte-scale data warehouse integrated with the AWS ecosystem
Tools & Frameworks We Work With
Apache Kafka / Flink: Streaming
Real-time event streaming and stream processing at scale
Apache Spark / dbt: Transformation
Large-scale data processing and SQL-based transformation workflows
Apache Airflow / Dagster: Orchestration
Workflow orchestration for complex data pipeline dependencies
Custom Data Products: RedEx
Purpose-built data products and APIs for enterprise-specific requirements
For Every Scale
Engagement Models
Not sure which model fits?
Not sure which model fits? The Data Readiness Assessment is the right starting point for any organisation evaluating AI deployment or data platform modernisation. Book a 30-minute call and we will confirm the right engagement in the first conversation.
Data Readiness Assessment (2 weeks)
Best for:
Organisations with approved AI use cases that are unsure whether their data is ready to support them, or organisations evaluating a data platform migration and needing a clear picture of current data quality before committing to a build.
What it includes:
- Data maturity scorecard
- source system audit
- data quality scoring across key domains
- AI readiness evaluation for specific use cases
- remediation roadmap
Platform Build
Best for:
Organisations ready to build or modernise a data platform to support their AI roadmap. Typically 3 to 6 months depending on data volume, source system complexity, and integration requirements.
What it includes:
- Full DATA framework delivery
- architecture design
- pipeline engineering
- data governance implementation
- quality monitoring
- analytics layer
- knowledge transfer
Migration Squad
Best for:
Organisations with a specific legacy data warehouse or on-premise system to migrate to a modern cloud platform. Zero-downtime migration with validation frameworks.
What it includes:
- Migration planning
- schema mapping
- data quality validation
- cutover
- post-migration monitoring
- documentation
Strategic Advisory Retainer
Best for:
CIO, CTO, or Chief Data Officers who need ongoing advisory on data platform strategy, vendor evaluation, AI data readiness governance, and data team capability building.
What it includes:
- Monthly advisory sessions
- architecture review
- vendor evaluation support
- quarterly data health reporting
- governance framework updates as AI use cases evolve.
FAQs
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.
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. RedEx’s DISCOVER phase produces a timeline estimate grounded in actual source system complexity within the first two weeks, before any build budget is committed. Migration timelines are always extended by zero-downtime requirements, which add 20 to 30% to the overall schedule but eliminate the business continuity risk of a hard cutover.
Start Your Transformation
Digital transformation begins with data you can trust. Every platform, every AI model, and every decision that follows is only as good as the foundation underneath it.