Data & Analytics

Turn Data Into Your Competitive Edge

End-to-End Data Engineering, Analytics & AI

From raw ingestion to production-ready machine learning models, we design and build data infrastructure that scales. Let your data work harder — with faster insights, reliable pipelines, and AI-powered predictions.

200+

Data Pipelines Built

80+

Models Deployed

99.5%

Data Accuracy

35%

Cost Reduction

Start Your Data Journey Explore Services

What We Offer

Comprehensive solutions tailored to your specific needs and goals.

Data Science & AI

Statistical modelling, predictive analytics, and AI solutions that extract actionable intelligence from your data.

Predictive and prescriptive modelling
Natural language processing (NLP)
Computer vision systems
A/B testing frameworks

6-12 weeksLearn more →

Data Engineering

Reliable, scalable data pipelines and infrastructure that ensure clean, timely data flows across your organisation.

ETL / ELT pipeline development
Data lakehouse architecture
Real-time streaming pipelines
Data quality & observability

4-10 weeksLearn more →

Machine Learning

Custom ML model development, MLOps infrastructure, and model lifecycle management from training to production.

Supervised & unsupervised learning
MLOps pipeline setup
Model monitoring & retraining
Feature engineering

8-16 weeksLearn more →

Big Data Analytics

Distributed processing architectures for large-scale data workloads — batch, streaming, and interactive query.

Spark & Databricks implementation
Cloud data lakehouse design
Petabyte-scale query optimisation
Streaming with Kafka / Kinesis

6-14 weeksLearn more →

Data Migration

Zero-downtime, validated data migration between databases, data warehouses, and cloud platforms.

Legacy to cloud migration
Cross-warehouse migration
Data validation & reconciliation
Schema translation & mapping

3-10 weeksLearn more →

Comprehensive Data Solutions

From ingestion to insight — we handle the full data lifecycle

Data engineering building scalable pipelines that ingest, transform, and deliver clean data across batch and streaming workloads
Machine learning developing and deploying ML models that generate predictions, automate decisions, and surface hidden patterns in your data
Big data processing designing distributed architectures on Spark, Databricks, and cloud-native services that handle petabyte-scale workloads
Data migration safely moving data between platforms — on-premise to cloud, legacy warehouses to modern lakehouses — with zero data loss
Analytics & BI building dashboards, KPI frameworks, and self-service analytics environments that put insights in the hands of decision-makers

Key Benefits

Faster Time to Insight

Automated pipelines and pre-built analytics layers reduce reporting lag from days to minutes.

Days → Minutes

Trustworthy Data

Automated data quality tests and lineage tracking ensure every metric is accurate and auditable.

99.5% data accuracy

35% Cost Reduction

Right-sized compute, efficient query patterns, and cloud optimisation cut infrastructure spend.

Average savings

Scalable Architecture

Data platforms designed to scale from gigabytes to petabytes without re-architecture.

Petabyte-ready

ML in Production

End-to-end MLOps means models move from notebook to production with monitoring and retraining built in.

Production ML

Zero-Loss Migration

Rigorous validation and reconciliation ensure no data is lost or corrupted during platform migrations.

100% data integrity

Our Process

A proven approach that delivers results consistently.

Discovery & Assessment

1-2 weeks

Data audit, infrastructure review, stakeholder interviews, and goal alignment.

Current state data auditData maturity assessmentPrioritised roadmapRisk register

Architecture & Design

1-3 weeks

Data architecture design, tool selection, and pipeline blueprinting.

Data architecture diagramPipeline design specsTech stack decision logData model documentation

Build & Integrate

4-12 weeks

Pipeline development, model training, integration testing, and deployment.

Production-ready pipelinesDeployed models or analytics layerIntegration testsMonitoring & alerting setup

Handover & Optimise

1-3 weeks

Production launch, documentation, team training, and ongoing optimisation.

Full documentation suiteTraining materialsCost & performance baselineSupport handover

Why Choose DevSimplex for Data Services?

We go beyond dashboards and reports — we build the foundations that make data a reliable, computable business asset.

Full-Stack Data Expertise

Our team spans data engineering, data science, ML engineering, and analytics — so you get end-to-end coverage without stitching together multiple vendors.

Production-Grade Pipelines

We build robust, observable ETL/ELT pipelines with proper error handling, monitoring, and incremental loading — not fragile scripts that break in production.

Insight-Driven Delivery

Every engagement is tied to measurable business outcomes — faster reporting, reduced churn, improved forecasting — not just technical deliverables.

Data Governance Built-In

We implement data quality checks, lineage tracking, access controls, and compliance guardrails from day one — so your data is trustworthy and auditable.

Cloud-Native & Modern Stack

We work with Snowflake, BigQuery, Databricks, dbt, Airflow, Spark, and the full modern data stack — selecting the right tools for your scale and budget.

Iterative & Agile

We deliver working data products in short sprints, validate with stakeholders early, and iterate — reducing the risk of multi-month projects that miss the mark.

Real-World Use Cases

Examples from projects we've delivered — with real challenges, solutions, and outcomes.

E-commerce

Challenge

Siloed data across ERP, CRM, and web analytics preventing unified customer view

Solution

Built a centralised data lakehouse with real-time event streaming and customer 360 dashboards

Results

60% faster marketing decisionsUnified customer attribution25% increase in conversion through ML recommendationsSingle source of truth for all teams

ROI: 280% ROI within 10 months

Financial Services

Challenge

Manual fraud detection process with high false-positive rates increasing operational cost

Solution

Deployed real-time ML fraud scoring pipeline integrated with transaction processing

Results

40% reduction in fraud losses60% drop in false positivesSub-100ms scoring latencyAutomated case triage

ROI: 350% ROI within 12 months

Healthcare

Challenge

Disparate patient data systems hampering clinical reporting and compliance

Solution

HIPAA-compliant data warehouse with automated clinical dashboards and audit logging

Results

Reporting time cut from days to minutesFull HIPAA compliance achievedProactive patient outcome alertsReduced manual data preparation by 80%

ROI: 320% ROI within 14 months

Case Studies

Real results from real projects.

E-commerceMegaRetail Group

Retail Analytics Platform

Eight disconnected data sources with no unified reporting, causing weekly manual consolidation effort

Results

90% reduction in manual reporting effort

Unified customer view across all channels

Report refresh from 3 days to 15 minutes

Self-service analytics adopted by 120+ users

Financial ServicesFinGuard Payments

Real-Time Fraud Detection

Rule-based fraud system generating 45% false positives and missing sophisticated fraud patterns

Results

40% reduction in fraud losses

False positives dropped from 45% to 12%

P99 scoring latency under 80ms

Model retraining automated weekly

View All Case Studies

What Our Clients Say

"DevSimplex built our data platform from scratch. Within three months we had clean, reliable data flowing into dashboards that our whole business uses daily. The pipeline quality is exceptional."

James Okafor

Head of Data, ScaleUp Commerce

"Their ML team understood the business problem, not just the technical one. The fraud model they deployed has saved us over a million dollars in its first year with minimal manual intervention."

Priya Mehta

Chief Risk Officer, SwiftPay Financial

Frequently Asked Questions

What data platforms and tools do you work with?

We work across the modern data stack: Snowflake, BigQuery, Databricks, Redshift for warehousing; dbt, Spark, Kafka, Airflow for transformation and orchestration; Python, scikit-learn, TensorFlow, PyTorch for ML; and Tableau, Looker, Power BI for BI. We choose the right tools for your scale and budget.

How do you ensure data quality and accuracy?

We implement automated data quality tests at every pipeline layer using tools like dbt tests and Great Expectations, complemented by data observability monitoring. All critical metrics have documented lineage so you can trace any number back to its source.

Can you work with our existing data infrastructure?

Yes — we always start with a discovery phase to understand your current stack, data sources, and constraints. We integrate with and extend existing infrastructure rather than forcing a full replacement unless that's the right call.

How long does a typical data project take?

Timelines vary by scope: a targeted ML model can take 6-10 weeks; a full data platform build typically runs 12-20 weeks. We work in sprints and deliver working increments early so you see value before the project ends.

Do you handle ongoing data platform support?

Yes. We offer managed data platform support covering pipeline monitoring, incident response, model retraining, and iterative improvements. We can also upskill your internal team and hand over full ownership when you're ready.