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Data Foundations

For SaaS & Enterprise Production Environments.
Data Strategy

Every Pipeline
One Data Platform
AI-Ready

DevIQ architects modern data platforms for SaaS and enterprise production environments that unify pipelines, platforms, and products into a single, scalable data platform.

Our work transforms fragmented data into a dependable, governed platform for analytics, workloads, and artificial intelligence, designed to grow and evolve with your organization.

Data Outcomes

Impacts of a Modern Data Platform

When your data is structured, reliable, and accessible, it changes how teams build, decide, and scale. These are the outcomes your organization can achieve once the right data foundation is in place.

Unified View of the Business

Data from products, platforms, and internal systems comes together in a single, trusted foundation. Teams share a consistent understanding of performance, customers, and operations without reconciling conflicting reports or definitions.
Faster, More Confident Decisions

Well-modeled data and reliable pipelines shorten the distance between questions and answers. Product, engineering, and business teams spend less time validating data and more time acting on insights with confidence.
Analytics That Scale with the Product

As products grow, analytics expands beyond dashboards. A strong data foundation supports product analytics, operational reporting, and executive visibility without constant rework or one-off solutions.
AI-Ready Data by Design

Clean, well-structured data enables advanced use cases like knowledge bases, recommendations, automation, and agents. AI initiatives move faster because data is already prepared for machine learning and intelligent systems.
Reduced Operational Drag

Production-grade pipelines, observability, and data quality practices reduce manual fixes and firefighting. Teams spend less time maintaining brittle workflows and more time delivering meaningful features and analysis.
A Platform Teams Can Build On

The data foundation functions as a shared platform rather than a bottleneck. Engineering, data, and product teams can extend it safely over time as new use cases, sources, and capabilities emerge.
Data Roadmap

From Disparate Sources to a Modern, AI-Ready Data Platform

Our data solutions follow a clear, end-to-end flow that turns scattered sources into a usable, scalable platform. You get a dependable data foundation ready for reporting, product analytics, and AI-driven use cases.

  • 01: Discover & Assess

    Create clarity around your current data landscape and highest-impact opportunities.

    • Inventory core data sources across products, platforms, and internal systems
    • Map how data is used today across reporting, operations, and product teams
    • Identify gaps, risks, and bottlenecks limiting reliability or scale
    • Prioritize near-term and long-term use cases, including analytics and AI

  • 02: Architect & Design

    Define a modern data architecture aligned to your product, teams, and growth plans.

    • Design a cloud-native data platform on Databricks, Azure, and/or AWS
    • Select storage and compute patterns that fit your data volumes and workloads
    • Model domains and data structures to reflect how your business operates
    • Establish security, access, and governance foundations that scale responsibly

  • 03: Build & Integrate

    Turn designs into production-ready systems your teams can trust and extend.

    • Implement pipelines that ingest, normalize, and transform data reliably
    • Integrate operational systems, SaaS tools, and product telemetry
    • Embed data quality checks, monitoring, and observability from day one
    • Deliver versioned, testable code that supports ongoing evolution

  • 04: Enable & Evolve

    Unlock your platform’s full potential and ready it for what’s next.

    • Deliver analytics-ready datasets and semantic models for BI and reporting
    • Enable self-service access for product, operations, and business teams
    • Prepare ML- and AI-ready datasets for models, agents, and automation
    • Continuously evolve the platform as products, users, and data grow
Data Solution Offers

Getting Started and Scaling to Production

Stuck on where to begin, lacking a cohesive strategy, or ready to build and scale into production, these structured, time-boxed engagements help your team move from napkins and backlogs into a solid data roadmap and production delivery cycle. DevIQ works alongside your team, phase-by-phase, from start to continuous improvement.

Data Foundations Review

Length: 2-3 weeks

For teams who know their data could be better but aren't quite sure where to begin, we inventory your sources and architect your roadmap.
Get Started →
Modern Data Platform Build

Length: 6-12+ weeks

For organizations ready to move beyond spreadsheets and ad-hoc scripts to building foundational pipelines and datasets.
Begin Your Build →
Ongoing Data Enablement

Length: 6+ months

For teams that need a long-term partner as their product and data evolve, from new integrations to performance tuning to analytics and AI.
Keep Growing →

Ongoing Data Enablement

Length: Multi-Phased

For teams that need a long-term partner as their product and data evolve, from new integrations to performance tuning to analytics and AI with a focus on continuous improvements to reliability, observability, and governance.

Keep Growing →

Transform Your Data, Today

It only gets harder from here. Start with a solid foundation and ready yourself for what's next.
<p>Talk to an Expert</p>

Proven, Efficient, and Data Certified.

Our well-certified team has real-world experience architecting and implementing Modern Data Platforms – using market leading data technologies from AWS, Azure, Databricks, Snowflake, ESRI, and DBT Labs. Plus, DevIQ is rigorously focused on growing our data partnerships and continuously earning industry leading certifications.

Data Case Studies

Data Foundations in Practice

These real-world, in-production case studies show how DevIQ enabled industry leading organizations to build and modernize their data platforms at enterprise scale to power analytics, products, and agentic AI initiatives.

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Modernizing Insurance Data for Analytics and AI

Evertree Insurance centralized underwriting and operational data while preparing the platform for advanced analytics and AI use cases.

  • Unified policy, risk, and operational data on Azure and Databricks
  • Delivered analytics-ready datasets for underwriting and reporting teams
  • Established a scalable foundation for future AI-driven risk insights
Read Case Study →
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Unifying Operational Data Across a Legacy Enterprise

O’Neal Steel consolidated fragmented legacy systems into a single data platform to improve visibility, reporting, and decision-making.

  • Integrated ERP, operational, and financial data into a centralized platform
  • Enabled faster, more reliable reporting for leadership and operations
  • Built a scalable data foundation to support automation and analytics
Read Case Study →
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Powering IIoT and Geospatial Intelligence

iHydrant built a data lake capable of ingesting large volumes of sensor and GIS data to support infrastructure monitoring and predictive insights.

  • Centralized IIoT sensor, asset, and geospatial data into a unified platform
  • Enabled advanced analytics and mapping for infrastructure visibility
  • Prepared data pipelines to support machine learning and predictive use cases
Read Case Study →
Data Insights

Featured Data Articles

From exploratory prototypes to enterprise-grade data solutions, DevIQ data engineers and solution architects share research, insights and reflections on modern data foundations.

Closing the Gap Between Data and Innovation with Databricks Apps

Start Reading

Data Lake vs Lakehouse vs Data Mesh: The Evolution of Data Transformation

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Modernizing SaaS Platforms for Business Growth

Start Reading
Data Governance

Securing Enterprise Data at Scale

DevIQ designs data platforms with governance at their core, ensuring data stays secure, discoverable, and reliable as it scales across teams, tools, and workloads.

  • Centralized Governance with Clear Boundaries
    Data assets are governed through a unified control plane that defines ownership, access, and usage across analytics, applications, and AI workloads.
  • Fine-Grained Access and Permissions
    Role-based permissions control access at the table, view, and column level, ensuring users and services see only what they are authorized to use.
  • Built-In Lineage and Discoverability
    Lineage and metadata make it clear where data comes from, how it changes, and when it is used, helping to understand impact and trust what is consumed.
  • Designed for Regulated and Sensitive Data
    Governance patterns support PHI, PII, and sensitive operational data by enforcing security and compliance directly at the data layer.
  • A Foundation for Analytics and AI
    Same access controls apply across reporting, machine learning, and AI workloads, creating a single, trusted foundation for all data-driven use cases.

Let's Talk Data Challenges with Real Solutions

Start with a 30-minute Data Discovery Call to talk directly with senior solution architects who've designed and deployed enterprise data products.

LET'S BUILD