01
Source assessment and data roadmap
Know which data you need and where it originates.
Reports use conflicting definitions, sources lack documentation and ownership or access permissions are unclear. Start with business questions and a representative data sample. Assess availability, freshness, permitted use and accountability, then propose connections and practical delivery stages with clearly defined acceptance criteria and dependencies.
Learn more
When it helps
Reports use conflicting definitions, sources lack documentation and ownership or access permissions are unclear.
What we do
- Inventory of databases, applications, files and owners
- Metric definitions, business rules and permissions
- Quality assessment and data-architecture roadmap
How it works
Start with business questions and a representative data sample. Assess availability, freshness, permitted use and accountability, then propose connections and practical delivery stages with clearly defined acceptance criteria and dependencies.
What you receive
- Source catalogue and data roadmap
- Metric glossary and prioritised improvement actions
Illustrative example
Illustration: sales and finance agree on the meaning of revenue and the authoritative source for their monthly report.
How we measure success
- Coverage of required sources
- Metric consistency and unresolved quality issues
After launch
Handover covers rules, documentation and responsibilities. The scope of data-quality monitoring, refreshes, support and further improvement is agreed separately.
Data integration, cleansing and synchronisation
Consistent records with less manual re-entry.
Distributed sources contain duplicate, missing or inconsistent records, producing conflicting reports and application behaviour. Define the master record, identifiers and change rules, then choose batch or event-driven transfers. Check ordering, retries, conflicts and recovery. Make data corrections traceable and agree on review before changing business-critical records.
Learn more
When it helps
Distributed sources contain duplicate, missing or inconsistent records, producing conflicting reports and application behaviour.
What we do
- ETL/ELT pipelines, mappings and validation
- Change data capture where the source supports CDC
- Duplicate detection and reconciliation after outages
How it works
Define the master record, identifiers and change rules, then choose batch or event-driven transfers. Check ordering, retries, conflicts and recovery. Make data corrections traceable and agree on review before changing business-critical records.
What you receive
- Data pipeline with quality checks
- Mappings, logs and reconciliation procedure
Illustrative example
Illustration: customer records from CRM and billing gain a shared identifier and an exception report for mismatches.
How we measure success
- Duplicates and invalid records
- Latency and reconciliation success
After launch
Handover covers rules, documentation and responsibilities. The scope of data-quality monitoring, refreshes, support and further improvement is agreed separately.
BI reports and dashboards
Metrics that support a clear next action.
Teams compile spreadsheets manually, reports arrive late and headline figures do not explain what caused a change. Identify the decisions each report must support, then reconcile calculations with a source-data sample. Adapt views to user roles, establish refresh behaviour and train report owners to interpret changes and recognise data limitations.
Learn more
When it helps
Teams compile spreadsheets manually, reports arrive late and headline figures do not explain what caused a change.
What we do
- Metric models and shared definitions
- Interactive dashboards, filters and drill-down views
- Refresh schedules, roles and calculation documentation
How it works
Identify the decisions each report must support, then reconcile calculations with a source-data sample. Adapt views to user roles, establish refresh behaviour and train report owners to interpret changes and recognise data limitations.
What you receive
- Dashboards with agreed indicators
- Documented calculations and user instructions
Illustrative example
Illustration: a manager monitors margin by product group and drills into the orders behind an unusual change.
How we measure success
- Agreement with source records
- Data freshness and report preparation time
After launch
Handover covers rules, documentation and responsibilities. The scope of data-quality monitoring, refreshes, support and further improvement is agreed separately.
Databases, warehouses and migrations
A sound foundation for applications, analytics and growth.
Slow queries, disconnected models and migrations without a validation plan make development and reporting harder to manage. Assess architecture, volumes and usage patterns before choosing a platform or change. Test representative workloads. For migrations, define downtime, data reconciliation, backups and rollback conditions, validating completeness before accepting the new environment.
Learn more
When it helps
Slow queries, disconnected models and migrations without a validation plan make development and reporting harder to manage.
What we do
- Database, warehouse and data-lake architecture
- Index, query and workload assessment
- Migration, result validation and rollback planning
How it works
Assess architecture, volumes and usage patterns before choosing a platform or change. Test representative workloads. For migrations, define downtime, data reconciliation, backups and rollback conditions, validating completeness before accepting the new environment.
What you receive
- Data model and implemented improvement or migration
- Measurements, documentation and recovery plan
Illustrative example
Illustration: move historical orders to a warehouse so analytical queries place less load on the operational application.
How we measure success
- Query times under agreed load
- Transfer completeness and operating costs
After launch
Handover covers rules, documentation and responsibilities. The scope of data-quality monitoring, refreshes, support and further improvement is agreed separately.
Forecasting and anomaly detection
Tested predictions with understandable limits.
Planning relies heavily on intuition, while unusual patterns become visible only after they have created a cost or delay. Assess history, seasonality and the business decision the result must inform. Compare the model with a simple baseline on a separate time period, assess the cost of false alerts and agree on when human judgement is required.
Learn more
When it helps
Planning relies heavily on intuition, while unusual patterns become visible only after they have created a cost or delay.
What we do
- Demand and time-series forecasting
- Anomaly detection and classification
- Baseline comparison and change monitoring
How it works
Assess history, seasonality and the business decision the result must inform. Compare the model with a simple baseline on a separate time period, assess the cost of false alerts and agree on when human judgement is required.
What you receive
- Forecasting or alerting pilot
- Accuracy report, limitations and monitoring rules
Illustrative example
Illustration: a sales forecast supports purchasing, while an unusual consumption pattern is flagged for review.
How we measure success
- Forecast error against the baseline
- Correct detections and false alerts
After launch
Handover covers rules, documentation and responsibilities. The scope of data-quality monitoring, refreshes, support and further improvement is agreed separately.
Data quality, governance and AI datasets
Data with clear ownership, lineage and purpose.
It is unclear who may use data, where it came from or whether its quality is sufficient for reports and model training. Assign owners, purposes and quality criteria. Establish checks, documentation and change approvals. For AI, review usage rights, label consistency and potential leakage between training and evaluation sets before using the data to assess a model.
Learn more
When it helps
It is unclear who may use data, where it came from or whether its quality is sufficient for reports and model training.
What we do
- Ownership, catalogues, lineage and retention rules
- Permissions, minimisation and access reviews
- AI labelling and separate training/test datasets
How it works
Assign owners, purposes and quality criteria. Establish checks, documentation and change approvals. For AI, review usage rights, label consistency and potential leakage between training and evaluation sets before using the data to assess a model.
What you receive
- Governance rules and quality overview
- Documented dataset or source catalogue
Illustrative example
Illustration: a labelled support-request dataset receives access rules and an independent test set for model evaluation.
How we measure success
- Completeness and label consistency
- Coverage of ownership, lineage and permissions
After launch
Handover covers rules, documentation and responsibilities. The scope of data-quality monitoring, refreshes, support and further improvement is agreed separately.
Scope and delivery options
Start forecasting and larger data solutions by reviewing quality and available history. Define access, sources, responsibilities and retention, export and deletion rules in the data plan.
Technologies and approaches
- SQL
- Working with structured database data.
- ETL / ELT
- Moving and transforming data for downstream use.
- BI / data warehouse
- Reporting from connected, agreed data.
- Data lake
- Storage for different data types; suitability is assessed against actual use.
- MDM / data governance
- Agreed master records, data owners and quality rules.
- Predictive models
- Statistical or AI models evaluated on held-out data.
