What is Data Modernization and why is it important for enterprises?

Data modernization refers to the process of upgrading legacy data infrastructure and storage to modern, cloud-native systems and management practices. For enterprises, this means using AI/ML, unifying fragmented data, and making faster, data-driven decisions while keeping customer experience in mind. There are multiple reasons why it’s important for enterprises today: Making better-informed business decisions, Handling increasing data volumes for scalability, Reducing manual tasks and infrastructure costs through automated workflows, Improving customer experience through centralized customer data, Maintaining a competitive edge and agility in rapidly-changing markets.

What challenges do enterprises face during data migration and modernization?

Enterprises today face significant challenges during data migration and modernization processes, with some of them centered around data quality issues, security risks, and legacy systems. Prominent challenges include: Failed migrations due to poorly managed data or inaccurate information, Semantic or structural mismatches due to system incompatibilities, Operational downtime when migrating large volumes of data, Exposure to breaches while data is in transit, Lack of change management post-migration.

How do Data Modernization Solutions drive business transformation?

Data modernization solutions today drive business transformation by replacing outdated, on-premises systems with unified, cloud-native platforms that improve scalability and drive better decisions. They also boost customer experiences through personalization, tighten security, and help position your enterprise as a market leader in data transformation. The business outcomes for this include significant cost reductions and increased agility.

What is the typical process followed in a Data Modernization project?

The process within a data modernization project usually involves transforming legacy, on-premises data systems into a modern, AI-ready architecture. It is an iterative process with many levels of refinement, focusing on maximizing security and data access. Here’s a process map illustrating what happens in a data modernization project: Assessment and strategy (Data audit and roadmap creation), Planning and designing (Architecture design and technology selection), Data integration and transformation (Data cleansing and ingestion), Data migration (Execution and data reconciliation), Implementation and optimization (DataOps setup and training teams), Post-migration maintenance (Monitoring and FinOps).

How do enterprises ensure governance, compliance, and security in modernization initiatives?

For governance, compliance, and security, enterprises usually adopt a secure-by-design approach that embeds risk management into the development lifecycle. This is done from the start, followed by the creation of a unified framework that combines automated technology, clear policies, and structured organizational oversight. Some of the key strategies for all three elements include: “Shift Left” security for early vulnerability detection, Using automated compliance tools to automatically scan infrastructure-as-code, Implementing a zero-trust architecture, Utilizing role-based access control.

Why partner with a Data Modernization Consulting Company like Tredence?

At Tredence, we help you unlock the power of data with AI-driven intelligence as part of your modernization efforts. With deep expertise in cloud data platforms and capabilities in building and supporting data engineering pipelines, DataOps, and MLOps frameworks, we’ve helped lead improvements in internal processes for various clients. With our purpose-built accelerators, you can achieve data migration and modernization by 50%, generating actionable insights while saving costs.

Data Migration & Modernization Services

Go beyond lift and shift to unlock deeper data value—modernize technically, functionally

Tredence can help you not only migrate your data to a modern cloud-native ecosystem, but also offers industry-aligned advisory services that enable last-mile adoption of your enterprise AI and ML, GenAI adoption and self-service analytics.

Assessment and target state design

We evaluate the maturity of your data platform and identify critical gaps across architecture, governance, and scalability. Leveraging decision trees and comprehensive scorecards, we design a Target State Architecture that is fully aligned with your business priorities.

Establishing data quality & governance

We enable enterprise-wide trusted data quality across layers and domains by setting up robust data governance. This covers cataloging, lineage, security, ownership and other core pillars to manage and govern data assets effectively.

Data model design and pipeline build

We design and implement comprehensive, industry-tailored data models—including Retail 360, CPG, and Healthcare & Life Sciences—along with domain-specific models for supply chain and customer data. These models are powered by optimized data pipelines that seamlessly integrate and enrich them with the required data and KPIs to deliver actionable insights.

Establishing XOps

We establish DataOps, PlatformOps, AIOps, and FinOps practices to drive continuous improvement across data pipelines, platform operations, and cost efficiency. By streamlining development, security, and operations—and enabling end-to-end observability across data, ML, AI, and platforms—we ensure enhanced performance, reliability, and scalability.

Build BI, analytics and ML model

We develop next-generation, scalable BI and analytics dashboards that deliver deep insights into key metrics and performance indicators, reinforced with robust security and governance. In parallel, we design, build and optimize ML models across diverse use cases, enabling both predictive and prescriptive analytics to drive smarter, data-driven decision-making.

Build Gen AI and Agentic use cases

We design and implement Gen AI and agentic solutions tailored to business needs—ranging from intelligent automation and natural language interfaces to decision-support systems. Our capabilities include building domain-specific LLMs, integrating them with enterprise data, and enabling secure, governed deployments. By embedding reasoning, orchestration, and multi-agent collaboration, we drive measurable efficiency, innovation, and business impact.

Accelerate data modernization with AI-powered, scalable, data migration solutions

Tredence fast-tracks data migration with AI and GenAI-driven accelerators and frameworks, ensuring seamless transitions across architectures and platforms.

Our intelligent solutions automate, optimize, and de-risk modernization, enabling enterprises to harness AI-powered insights faster. With a proven GenAI-first approach, we transform legacy systems into future-ready, scalable, and efficient data ecosystems, unlocking unparalleled business value.

Discovery and rationalization accelerators

Tredence has developed a data discoverability solution that captures existing solution artifacts, complexity, and lineage, with the capability to rationalize objects, pipelines, and models for migration to the target architecture. Our Report Rationalization solution extracts report metadata to build disposition frameworks, streamlining and optimizing report portfolios.

Data ingestion accelerator

We have built a metadata-driven ingestion framework with the capability to connect to diverse sources, capture change data, and load into raw and curated layers. The framework embeds data quality checks and encryption by design, enabling secure, scalable, and automated data ingestion across all major cloud-native data platform.

Data governance accelerators

We offer a comprehensive Data Quality suite spanning Data Engineering, Master Data Management, and Anomaly Detection—seamlessly embedded within data pipelines to ensure trust and reliability. Complementing this, our Gen AI–powered cost and pipeline observability solution detects anomalies, delivers actionable insights, and drives proactive optimization.

T-Modeler

We have developed a Gen AI–powered accelerator that enhances Tredence’s industry-standard data models by incorporating customer BRDs and source data, enabling the rapid creation of customized, scalable data models. These models are purpose-built to support advanced analytics and Gen AI use cases, accelerating time-to-value and business impact.

MLWorks

Tredence’s MLWorks accelerator enables comprehensive observability for large-scale ML models, ensuring real-time monitoring, proactive drift detection, and faster debugging. With end-to-end visibility, pipeline traceability, and root cause analysis, it enhances model reliability, performance, and governance, allowing enterprises to scale AI confidently.

GenAI-powered Solutions

Tredence’s Gen AI–powered data engineering frameworks accelerate delivery by over 50% through capabilities in discoverability, code conversion, data observability, and automated data model generation. In addition, our industry-focused Gen AI functional accelerators unlock greater value from the existing data estate, enabling customers to drive faster, more informed business decisions.