Our approach to data modernization
Our approach to data modernization spans six interdependent capability zones, each built to solve a distinct organizational pressure point. Data strategy work starts with a clear-eyed assessment of where the business stands and where its data architecture needs to go, covering enterprise data strategy, maturity assessment, and architecture aligned tightly to governance and integrity standards. Data analytics capabilities center on next-generation business intelligence and AI-enabled analytics that surface actionable insights, not dashboards nobody reads. Data governance and management handles the unglamorous but critical work: master data management, lineage tracking, quality assurance, and the catalogs that make shared access genuinely feasible. Data operations introduces agility through Data as a Service models, ServiceNow integration, and data monetization frameworks. Data engineering modernizes the underlying infrastructure with advanced pipelines, ETL transformation, migration solutions, and the modeling work that makes scaled analytics possible. And data IP capabilities apply all of this to specific business functions, including customer service, marketing, contact centers, and IT asset management. Think of it as a blueprint, not a menu. Each capability reinforces the others, which is why organizations engaging across the full stack consistently see disproportionate returns.
Execution is where most data modernization programs break down
The strategy looks credible in a presentation. Six months into a migration project, complexity overtakes the original estimate and momentum stalls. We address this directly through a structured methodology that applies AI at the operational level, not just as a framing device. Migration strategy begins with a tailored assessment of source and target databases, producing a report that captures real complexities, effort, and honest timelines. From there, AI/ML-led schema migration extends coverage wherever standard conversion tools fall short, with generative AI closing the gaps. Code conversion runs through a Gen AI-based accelerator paired with a human-in-the-loop methodology, so output is fast and actually correct. Data migration uses Gen AI for script creation, custom processes for change data capture, and built-in recovery mechanisms. Post-migration data validation is generated through AI-created test cases aligned with downstream consumption patterns, removing the guesswork from a stage organizations routinely underinvest in. The cloud-based migration framework is platform-agnostic and adapts to complexity and multi-environment scenarios. That matters enormously for enterprises still mid-rationalization on their infrastructure. This is what enterprise AI solutions at the engineering layer actually look like in practice.
Our solutions on ADAM: The productized form of this methodology
Four AI-native capability sets, each targeting a specific pressure point in the modern data stack. AI Data Management and Governance covers data quality, master data management, cataloging, and business glossaries. Two capabilities stand out. Agentic Data Quality gives data stewards a visually intuitive tool that doesn’t just surface problems but helps prioritize and implement fixes, making agentic AI governance a practical reality. AI-enabled Data Lineage maps data flow across disparate systems through an interactive interface that traces origins, transformations, and destinations end to end. AI Data Operations moves beyond monitoring into active problem resolution. Data observability tracks pipelines and completion rates. Log analytics monitors jobs in real time, detects issues proactively, delivers automated root cause analysis, and resolves problems without manual intervention. That’s AI-powered data governance working at the operational level. AI Data Analytics activates the intelligence layer, enabling teams to ask complex questions and surface trends, patterns, and opportunities through AI-enabled BI. Gen AI enhances decision-making by generating visual summaries of complex datasets and producing precise answers to critical business questions. AI Data Engineering focuses on the infrastructure that makes all of this sustainable: migration to modern platforms like Google BigQuery, SQL query optimization post-migration, and validation frameworks that ensure accuracy and consistency at scale. ADAM integrates with Snowflake, Databricks, and Google Cloud. Compatibility is built in, not bolted on.
