Complex data challenges, translated into working solutions.

Business challenge

A traditional enterprise data warehouse can accumulate tightly coupled ETL jobs, overlapping data marts, duplicated transformations, and report-specific business rules. Moving platforms without understanding those dependencies risks breaking reconciliations and downstream reporting.

Solution approach

• Inventory source systems, warehouse objects, pipelines, reports, and critical business definitions.
• Group workloads into migration waves based on risk, usage, dependencies, and business value.
• Design a Microsoft Fabric target using OneLake and Lakehouse/Warehouse patterns appropriate to each workload.
• Rebuild transformations and curated data products with documented rules, automated checks, and reconciliation against the source EDW.
• Plan parallel validation, cutover, rollback options, and operational handover.

Reference architecture

Source applications and legacy EDW → controlled ingestion and landing → Microsoft Fabric OneLake → Lakehouse / Warehouse transformations → governed semantic models and reporting.

Business challenge

Payroll and compliance knowledge is often spread across policy documents, SOPs, reference guides, and frequently changing rules. Manual searching takes time, and a generic chatbot can return confident but unsupported answers if it is not grounded in approved sources.

Solution approach

• Ingest approved knowledge sources, capture useful metadata, and normalize content for retrieval.
• Chunk documents with contextual boundaries, generate embeddings, and index them in a vector database.
• Retrieve relevant passages for each question and pass only that context to the language model.
• Return source-linked answers, surface uncertainty, and route low-confidence or sensitive questions to a human reviewer.
• Apply identity-aware document access, audit logging, freshness checks, and a test set for retrieval quality and answer groundedness.

Reference architecture

Approved payroll/compliance sources → parsing and chunking → embeddings → vector database / hybrid search → permission-aware retrieval → LLM response with citations → evaluation and monitoring.

Business challenge

Payroll star schemas carry important fact-to-dimension relationships, historical rules, and business-specific calculations. Large on-premises workloads can be difficult to scale or tune, while an unplanned migration can introduce duplicate records, changed totals, or broken historical comparisons.

Solution approach

• Profile large fact and dimension tables, document keys and relationships, and identify incremental-load opportunities.
• Map source transformations and historical treatment before moving them to distributed processing.
• Land data in AWS storage and use Databricks with Delta Lake patterns for scalable transformation and curated datasets.
• Preserve business keys, effective-date logic, grain, and dimensional history where required.
• Validate counts, control totals, period-level payroll measures, duplicates, and downstream report outputs during parallel runs.

Reference architecture

On-prem SQL Server → secure incremental/batch ingestion → AWS storage → Databricks / Spark → Delta Lake bronze, silver, and curated layers → analytics and downstream data products.

Business challenge

Finance reporting frequently depends on workbooks shared across teams, manual consolidations, repeated formula logic, and different interpretations of the same KPI. This makes refresh cycles fragile and can leave analysts spending more time preparing data than understanding it.

Solution approach

• Inventory reports, workbook calculations, business definitions, owners, and refresh expectations.
• Consolidate source data and recurring transformation rules into reusable, documented logic.
• Model finance measures and dimensions consistently within Qlik Sense.
• Build role-appropriate dashboards, filters, and drill paths for recurring business questions.
• Validate dashboard totals against accepted Excel outputs and establish ownership for future changes.

Reference architecture

Finance source files and systems → standardized ingestion and validation → reusable finance data model → Qlik Sense apps and governed self-service exploration.

Business challenge

Revenue lifecycle workflows rely on information that may be distributed across line-of-business systems, file exchanges, and operational databases. Teams need to follow records across the lifecycle, investigate exceptions, and work from consistent data without overlooking privacy and access requirements.

Solution approach

• Establish controlled landing for inbound extracts and files in Azure Blob Storage.
• Use Azure Synapse orchestration and transformation patterns to standardize, validate, and move data through processing stages.
• Publish fit-for-purpose curated datasets into Azure SQL for operational queries and downstream reporting.
• Define data quality checks and traceability for key identifiers, statuses, dates, balances, and transaction relationships.
• Design access, auditability, and sensitive-data handling around the customer's policies and applicable requirements.

Reference architecture

Healthcare and revenue-cycle source systems → Azure Blob Storage landing → Azure Synapse pipelines and transformations → Azure SQL curated data → operational reporting, reconciliation, and analytics.