Our Process

No two modernization projects are same and so is the approach. While the foundation tools are common, we take a tailored approach to ensure all bases are covered

Areas like: - Metadata conversion, Code Conversion (ETL, Scripts, SP, Views etc), Jobs Refactoring, Scheduling, Data Validation, and alike


Whether it is building one time history data pipelines or regular incremental data sync, we identify most optimized solution and automate in production environments

This helps in reliable data migration and achieving planned timelines

Assessment and Discovery is an important and crucial phase of any Data Modernization program. There are numerous objects spread across databases, reports, ETL tools, and scripts. Gaining a comprehensive understanding of these objects, their dependencies, data lineage, and dependent production jobs is essential to mapping use cases and developing a robust end-state architecture.

Our assessment methodology provides clarity on the migration strategy, planning, and deterministic costs, helping our clients reduce uncertainties and provide the necessary inputs to engage business stakeholders effectively.

We conduct in-depth analysis of the current state of data warehouses, identify and analyze workload patterns, and uncover underlying dependencies.

Our end deliverables during the assessment phase include a granular design of the target architecture, details on the tools and components involved, and an outline of how the end-to-end solution will be integrated. We provide a clear view of the pros and cons to help our customers make informed decisions.

We also provide a detailed plan for the entire program, broken down into phases and sprints where needed.

With a philosophy that no single solution fits all, we provide a targeted modernization approach for our clients. This approach is based on several factors such as the maturity of current data platforms, the business criticality of data, enterprise data strategy and roadmap, data management principles, and other operational considerations.

Based on these insights, we build a Minimum Viable Product (MVP) and evaluate various migration options such as "Lift and Shift," refactoring pipelines, full modernization, or hybrid approaches to determine the best fit for our clients. With extensive experience across these combinations, we are well-equipped to recommend the optimal approach that ensures the success of such programs.

Our Commitment to Stress-Free Data Migration
At Smart Analytica, we aim to make your data migration process as smooth and worry-free as possible. We minimize risks and uncertainties by providing accurate estimates for budget, time, and cost. Our approach ensures seamless transitions with effective change management and a comprehensive understanding of your workloads, allowing you to focus on your business with confidence.