DATA ANALYTICS & ENTERPRISE REPORTING
A consistent data foundation for enterprise analytics.
We assess how data from business applications is prepared, integrated and made available for analysis. Our focus is an analytics architecture aligned with your data sources, business definitions, quality controls and operational requirements.
Discuss your data requirementsDATA SOURCES
Start by understanding the systems behind the data.
We assess SQL Server databases, ERP and CRM applications and existing file sources in terms of their content, access methods and business owners. Understanding how source systems record transactions helps establish the meaning of shared business concepts across applications.
- Identifying sources, data owners and access permissions
- Clarifying the business meaning of customer, product, account and transaction records
- Assessing source workload, data volumes and freshness expectations
DATA INTEGRATION · ETL / ELT
Design data movement with rules and controls built in.
ETL transforms data before loading it into its destination; ELT performs transformations within the target data platform. We assess the appropriate approach against your infrastructure, business rules and data volumes, planning initial loads, incremental transfers and reruns with data consistency in mind.
- Designing source-to-target mappings, transformation rules and staging areas
- Defining how changes and deleted records are tracked for incremental loads
- Planning load dependencies, error logging and reruns without duplicating data
DATA WAREHOUSING & DIMENSIONAL MODELLING
Translate shared business definitions into analytical data structures.
A data warehouse brings data from different sources together for analysis and retains history where required. In a star schema, fact tables represent business events such as sales or stock movements, while dimension tables describe context such as customers, products and dates. We assess data marts as analytical datasets focused on a specific business area.
- Defining fact table grain and dimension keys
- Assessing shared dimensions and history requirements across business areas
- Defining data mart boundaries and shared definitions for finance, sales or operations
SQL & DATA PROCESSING
Translate business rules into traceable processing steps.
We review existing SQL Server queries, joins and aggregation rules against analytical requirements. We assess reusable datasets through views and appropriate processing steps, examining execution plans, indexes and the impact on source systems through measurement.
- Validating joins, aggregations and period calculations against business definitions
- Planning processing dependencies and repeatable execution
- Reviewing execution plans, index usage and processing times under existing conditions
DATA QUALITY
Make the basis of reported results verifiable.
We define checks for missing values, duplicate records, invalid codes and inconsistencies across systems. Quality is considered throughout the flow from sources to reporting, with clear responsibilities for reviewing control results and tracking issues.
- Checks for completeness, uniqueness, validity and referential consistency
- Comparing record counts and business totals between source and target
- Defining ownership, exception logging and remediation processes for quality issues
ENTERPRISE REPORTING
Build reports on shared business definitions.
Enterprise reporting infrastructure aligns metric definitions, data scope and period rules. We assess the datasets feeding management and operational reports against freshness, reconciliation and data ownership requirements. Power BI report design and visualisation are covered by our dedicated consulting service.
Explore Power BI & Business Intelligence Consulting- Documenting metric definitions, calculation rules and data scope
- Defining publication and freshness expectations for reporting datasets
- Tracing reported indicators back to source records and transformation steps
ANALYTICS ARCHITECTURE
A roadmap shaped around your requirements and infrastructure.
We define responsibilities across source, ingestion, preparation, storage and delivery layers. Technology choices are assessed against existing systems, team capabilities, access rules and operating conditions. New platform decisions follow clarified requirements, without assuming migration to a particular technology as a prerequisite.
- Documenting data layers, dependencies and flows
- Assessing access, data ownership, retention and monitoring requirements
- Defining priorities, responsibilities and a phased implementation plan
PROJECT APPROACH
From business requirements to validated data flows.
We work with business teams, data owners and technical teams to assess analytics infrastructure, clarifying scope and acceptance criteria from the outset.
Understand sources and expectations
We review existing data structures, reporting challenges and priority business questions to define the scope of work.
Design the data flows and architecture
We bring integration rules, dimensional structures, quality controls and operational responsibilities into a shared plan.
Validate and prepare for operation
We test source-to-target reconciliation and business scenarios, planning monitoring, documentation and knowledge transfer.
LET’S DISCUSS YOUR REQUIREMENTS
Let’s plan the next step for your analytics infrastructure.
Share your requirements for integrating sources, addressing data quality or developing enterprise reporting infrastructure. Together, we can define an appropriate scope of work.