Disconnected sources
Exports, spreadsheets, and operational records disagree or lose their context.
Data & Decisions
Data & Business Intelligence
I build reporting workflows, operational dashboards, and decision-support tools that connect business questions to inspectable data. Automation reduces repeat work; people retain judgment.
Direct answer
The work begins with a business decision and the available data. I define metrics, validate inputs, and build reports or interfaces that make signals easier to inspect. Automation supports repeatable collection and reporting, with human review where interpretation matters.
Context
Unclear metrics
Teams use different definitions for the same commercial question.
Slow reporting
Manual preparation delays decisions and makes analysis difficult to repeat.
Weak provenance
Charts hide data quality, source limits, and assumptions.
Signals without action
A report needs to clarify what to investigate or decide next.
Deliverables
Data and metric contracts
Sources, definitions, validation rules, and known limits.
Reporting pipelines
Repeatable preparation and computation from raw data to reports.
Operational dashboards
Focused views for reviewing signals and deciding what needs attention.
CRM and commercial analysis
Segmentation, campaign analysis, and customer insight grounded in available records.
Decision-support prototypes
Scenario and forecasting tools with assumptions visible.
Documentation and handover
Traceable logic and a practical path for maintaining the workflow.
Applications
CRM and customer segmentation
Campaign performance reporting
Operational dashboards
Metric and data-quality audits
Inventory forecasting prototypes
Commercial decision support
Process
Define the decision
Identify the business question, users, sources, and useful output.
Agree metrics and boundaries
Define calculations, data quality, assumptions, and review ownership.
Build the reporting workflow
Connect preparation, computation, and the interface people will use.
Validate and document
Test representative records, inspect edge cases, and document the logic.
Frequently asked questions
What data can we start with?
Exports, spreadsheets, or approved database sources. The first step is to inspect what the records can support.
Can AI calculate the metrics?
Core metrics should be computed and validated deterministically. AI can help explain accepted results with the limitations visible.
Can you connect existing tools?
Yes, where suitable APIs, exports, or webhooks are available. Access and integration scope are agreed before implementation.
Can this work for small teams?
A focused report or internal dashboard can be enough. The scope follows the decision rather than the size of a platform.
What platforms do you use?
The choice depends on data, volume, maintenance, and the existing environment. Python, SQL, a database, or a web interface may form part of the workflow.
How do you keep quality control?
Validation rules, source visibility, agreed metric definitions, and review points make errors and assumptions inspectable.
What does a first project include?
A defined decision, a source and metric contract, one working reporting flow, representative validation, and documentation.
Have data that needs a clearer decision?
Send the business question, available sources, and current reporting process. We can define a focused first system.