More trustworthy decisions
Make the path from business data to an explanation easier to inspect, so teams can act with a clearer understanding of the numbers.
FROM POSSIBILITY TO PRACTICE
What it does for your business
For your data and analytics team, “SQL” can become a repeatable workflow rather than a separate task handled from scratch each time. AI can help work through a technical task with the relevant system context, preparing changes or explanations for an engineer to inspect. It fits into data preparation, analysis and business reporting, using your business information and the standards your team already works to.
The value it could bring
These are the improvements to evaluate against your current process. We agree the scope and test the value with your team.
Shorter implementation cycles
Reduce repetitive technical work and investigation so engineers can get to a reviewable result sooner.
Preserve the engineering context
Keep the proposed work connected to existing standards, acceptance criteria and the checks needed to release it.
EVIDENCE FROM OTHER BUSINESSES
Published ROI evidence
We have not linked a comparable published financial ROI study for this use case yet. That leaves it unranked, rather than assigning an estimated score. A pilot can measure benefits and total implementation and running costs in your business.
How it works in your business
STEP 1
Bring in the right context
Select the relevant information from your databases, spreadsheets and analytics workspace. Agree what a good result looks like with your data and analytics team, including the rules, examples and permissions the workflow needs.
STEP 2
Turn the task into a workflow
Ask the database the question you meant to ask. Use the specification and existing architecture to propose an approach, create the relevant artefacts and check the result against the task's acceptance criteria.
STEP 3
Put the result to work
Return a reviewable change or technical explanation with the relevant checks, so it can move through the team's normal engineering process. Analysts verify queries, calculations, definitions and assumptions before a result informs a business decision.
AN EXAMPLE IN PRACTICE
A member of your data and analytics team needs help with “SQL”. They supply approved data sources with agreed access, together with the relevant instructions and the result they need. The workflow prepares an initial result with its supporting context, flags missing information and returns it for review. The owner can correct it and use the accepted result in the team's databases, spreadsheets and analytics workspace.
Start with a focused pilot
Choose one workflow, one team and a representative set of real tasks. We establish the current baseline, build the first version and review the results together before expanding it.
What we would start with
Depending on the scope, useful inputs include:
- Approved data sources with agreed access
- Metric definitions, schema and business rules
- Known examples and quality checks
We agree access and integration with your databases, spreadsheets and analytics workspace as part of the design.
How we would measure value
- Time to a reviewed, accepted change
- Failures caught by tests, review and post-release checks
- Reconciliation to trusted figures and time to a usable analysis
Analysts verify queries, calculations, definitions and assumptions before a result informs a business decision.
LET’S BUILD IT TOGETHER
Could this help your business?
Tell us how your team works today and what you would like to improve. We’ll explore the opportunity, shape a practical first step and build it with you.
Talk to us about this use case