Data assessment
Map the sources, owners, definitions and quality issues that determine whether reporting can be trusted.
Data engineering & analytics
A clearer path from business data to useful decisions.
Data pipelines, analytical models and reporting that help teams work from consistent information instead of disconnected extracts.
Discuss your requirementsWhen this can help
Recognise the challenge?
- Reporting takes too much manual effort to prepare and reconcile.
- Different teams use conflicting definitions for the same business metric.
- Useful information is spread across applications, files and databases.
Potential scope
What an engagement can cover.
The combination depends on your requirements; scope is agreed before delivery.
Pipelines & transformation
Move and transform data with explicit validation, refresh requirements and handling for incomplete or failed inputs.
Models & reporting foundations
Organise data around agreed business definitions, with traceability back to source systems.
Dashboards & business intelligence
Build reporting around the decisions people need to make, with appropriate filtering, access and explanations of each metric.
Tangible outputs
Know what you’re working towards.
Specify the deliverables and acceptance criteria that matter for your engagement.
- A source inventory and agreed reporting requirements
- Data transformations and pipelines within the agreed scope
- Documented metric definitions and validation rules
- Dashboards, refresh instructions and handover documentation
What we need to consider.
Data access, source quality, refresh frequency, ownership and retention requirements all affect the solution. A dashboard cannot resolve inconsistent underlying data on its own.
How to start.
Share the decisions you need to support, the reports you use today and where the underlying information lives. Avoid sending sensitive datasets in an initial enquiry; access can be arranged separately.
More about the delivery approachUseful to know
Your questions, answered.
Can you improve existing reports?
Yes. We can start with how a report is produced, where its numbers come from and what users need to change. The work may involve the reporting layer, underlying data flows or both.
Which tools do you work with?
Relevant tools include Python, SQL, PostgreSQL and Power BI. Tool choices should fit your existing environment, operating needs and internal skills.
Can reporting include sensitive information?
Access, data handling and any applicable organisational requirements need to be agreed before data is shared or processed. Please describe the requirement without sending sensitive records.