Our Data Modernization Services migrate and upgrade legacy systems into cloud-native, future-ready platforms that power real-time analytics, personalization and AI-driven growth.
Why Choose Us
Your data infrastructure shouldn’t hold your business back. Our Data Modernization Services help you move beyond outdated platforms and fragmented systems to modern, cloud-native environments built for speed, scalability, and intelligence.
We enable seamless migration, eliminate technical debt, and prepare your data for real-time analytics, AI, and advanced personalization.
From assessment to migration and optimization, Decision Foundry ensures every modernization step adds measurable value. Whether you’re moving to Snowflake, BigQuery, Databricks, or Redshift, we help you design a data ecosystem that’s efficient, secure, and ready for tomorrow’s analytics.
What We Deliver
Identify risks, inefficiencies and modernization opportunities.
Move to scalable platforms like Snowflake, BigQuery, Databricks or Redshift.
Enable unified access to structured and unstructured data.
Tune storage, compute and queries for efficiency.
Ensure teams adopt modernized platforms effectively.
Why It Matters
Enable AI/ML capabilities with cloud-scale infrastructure. Reduce technical debt and accelerate delivery.
Gain agility with real-time customer insights. Leverage scalable systems for omnichannel personalization.
Improve visibility into revenue drivers with modernized reporting. Support faster, more accurate forecasting.
Common Questions
Data modernization is the process of migrating from legacy data systems (on-premise warehouses, decade-old ETL tools, scattered file-based reporting) to cloud-native platforms designed for real-time analytics, AI, and personalization. It's not just a lift-and-shift: modernization typically involves re-architecting pipelines, adopting a lakehouse or medallion pattern, and rebuilding governance for the cloud era.
Current-state assessment of legacy systems (Teradata, Oracle, on-prem Hadoop, SAS, etc.); target-state architecture design on Snowflake, Databricks, BigQuery, or Microsoft Fabric; pipeline modernization (legacy ETL → modern ELT with dbt / Fivetran / Airflow); data migration with quality controls; governance rebuild; user retraining; and a phased cutover plan that keeps the lights on during transition.
Migration moves data from system A to system B with minimal change ("lift and shift"). Modernization migrates AND redesigns, adopting cloud-native patterns, fixing accumulated technical debt, and unlocking capabilities the legacy system couldn't support (real-time, AI, governance at scale). Migrations get you the same broken system on a new platform; modernization gets you a better operational model. We do both, but we recommend modernization in 80%+ of cases.
A focused modernization (one source system, one target platform, 6-10 datasets) runs 12-20 weeks. A full enterprise modernization (multiple legacy systems, hundreds of pipelines, governance rebuild) runs 9-18 months, typically phased across releases. Cost varies widely with data volume, system count, and parallel-run requirements. Every engagement starts with a free discovery call and modernization assessment.
Every enterprise modernization we deliver assumes zero downtime. We design parallel-run windows where legacy and modern systems both produce data, validate parity, then cut over with rollback paths in place. For highly regulated environments, we maintain dual-write periods of 30-90 days before fully decommissioning the legacy. The architecture-first approach makes this possible: without it, modernization becomes a high-risk big-bang.
Since 2004 we've delivered enterprise data migrations across Teradata → Snowflake, Oracle → Databricks, on-prem Hadoop → cloud lakehouse, and dozens of other legacy → modern transitions. Snowflake Select Partner, Databricks Premier Partner, full Salesforce certifications. Our FDE engineers embed inside your team during cutover, when the platform is the easy part and the people are the hard part.