Business problem
Reports disagree because the definitions differ. Teams spend the meeting reconciling numbers instead of deciding.
Services
Data Analytics
Reporting and analytical models built on defined data sources, so leaders can see operational performance without a manual spreadsheet cycle.
Solution
We start with the decisions the data should support, then trace the source fields and the metric definitions. Models and dashboards come after the definitions are agreed.
Capabilities
- Data analytics
- Business intelligence
- Metric definitions
- Warehouse and model design
- Operational dashboards
Benefits
- Shared definitions for the numbers leadership reviews
- Less manual preparation before a management meeting
- A source that can be audited back to the system of record
Technologies
- SQL
- Warehouses
- BI tools
- Event data
Implementation approach
- 01List the decisions and the current reports.
- 02Agree metric definitions and grain.
- 03Model the smallest useful dataset.
- 04Publish the dashboard with an owner for each metric.
Questions
How does a data analytics engagement start?
We start with a discovery conversation about the business problem, constraints, and what a useful outcome looks like. A written scope is agreed before delivery begins.
Can this be delivered with our internal team?
Yes. Work can be advisory, delivered by Curve Infotech, or shared with your team. The operating model is agreed during scoping.
Who owns the work product?
Ownership is defined in the statement of work. Commissioned deliverables are typically assigned to the client on payment, excluding pre-existing Curve Infotech tools and third-party software.
How is security handled?
Access, environments, and data handling are agreed up front. Production access is limited to what the engagement requires, and secrets are not stored in the application source.
Talk to our experts
Tell us about the business problem. We will reply with a scoped next step.